This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus...This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.展开更多
Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours ...Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.展开更多
A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the t...A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the traditional prescribed performance control in which the shape of the performance function is constant, this paper exploits new performance functions which can change the shape of their function according to different symbols of initial errors and can ensure the error convergence with a small overshoot. The neural backstepping control and the minimal learning parameters (MLP) technology are employed for exploring a prescribed performance controller (PPC) that provides robust tracking attitude reference trajectories. The highlight is that the transient performance of tracking errors is satisfactory and the computational load of neural approximation is low. The pseudo rate (PSR) modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation and high accuracy.展开更多
Multi-objective optimization design of the gas-filled bag cushion landing system is investigated.Firstly,the landing process of airbag is decomposed into a adiabatic compression and a release of landing shock energy,a...Multi-objective optimization design of the gas-filled bag cushion landing system is investigated.Firstly,the landing process of airbag is decomposed into a adiabatic compression and a release of landing shock energy,and the differential equation of cylindrical gas-filled bag is presented from a theoretical perspective based on the ideal gas state equation and dynamic equation.Then,the effects of exhaust areas and blasting pressure on buffer characteristics are studied,taking those parameters as design variable for the multiobjective optimization problem,and the solution can be determined by comparing Pareto set,which is gained by NSGA-Ⅱ.Finally,the feasibility of the design scheme is verified by experimental results of the ground test.展开更多
The attitude control problem of the kinetic kill vehicle is studied in this work. A new mathematical model of the kinetic kill vehicle is proposed, the linear quadratic regulator technique is used to design the optima...The attitude control problem of the kinetic kill vehicle is studied in this work. A new mathematical model of the kinetic kill vehicle is proposed, the linear quadratic regulator technique is used to design the optimal attitude controller, and the pulse-width pulse-frequency modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the reaction thrusters. The methods to select the appropriate parameters of pulse-width pulse-frequency are presented in detail. Numerical simulations show that the performance of the LQR/PWPF approach can achieve good control performance such as pseudo-linear operation, high accuracy, and fast enough tracking speed.展开更多
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically...A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.展开更多
In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory opti...In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.展开更多
A differential steering system is presented for electric vehicle with motorized wheels and a dynamic model of three-freedom car is built.Based on these models,the quantitative expressions of the road feel,sensitivity,...A differential steering system is presented for electric vehicle with motorized wheels and a dynamic model of three-freedom car is built.Based on these models,the quantitative expressions of the road feel,sensitivity,and operation stability of the steering are derived.Then,according to the features of multi-constrained optimization of multi-objective function,a multi-island genetic algorithm(MIGA)is designed.Taking the road feel and the sensitivity of the steering as optimization objectives and the operation stability of the steering as a constraint,the system parameters are optimized.The simulation results show that the system optimized with MIGA can improve the steering road feel,and guarantee the operation stability and steering sensibility.展开更多
Hydraulic hybrid vehicles (HHV) with secondary regulation technology has the potential of improving fuel economy by operating the engine in the optimum efficiency range and making use of regenerative braking. Hydros...Hydraulic hybrid vehicles (HHV) with secondary regulation technology has the potential of improving fuel economy by operating the engine in the optimum efficiency range and making use of regenerative braking. Hydrostatic transmission technology has the advantage of higher power density and the ability to accept the high rates and high frequencies of charging and discharging, both of which are not favorable for batteries, but the lower energy density requires special power matching design and control strategy to coordinate all the powertrain components in an optimal manner. A multi-objective optimization method is proposed to distinguish the components size values of HHV by considering the requirements of driving cycles and technology aspects. The regenerative braking strategy and energy control strategy based on the optimized HHV is proposed to recovery the braking energy and distribute the regenerated braking energy. Simulation results show that by taking the optimized configuration of HHV, adopting the regenerative braking strategy and energy control strategy are helpful to improve the system efficiency and fuel economy of HHV under urban driving cycles.展开更多
In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion ...In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.展开更多
The vehicle semi-active suspension with magneto-theological damper(MRD) has been a hot topic since this decade, in which the robust control synthesis considering load variation is a challenging task. In this paper, ...The vehicle semi-active suspension with magneto-theological damper(MRD) has been a hot topic since this decade, in which the robust control synthesis considering load variation is a challenging task. In this paper, a new semi-active controller based upon the inverse model and sliding mode control (SMC) strategies is proposed for the quarter-vehicle suspension with the magneto-rheological (MR) damper, wherein an ideal skyhook suspension is employed as the control reference model and the vehicle sprung mass is considered as an uncertain parameter. According to the asymptotical stability of SMC, the dynamic errors between the plant and reference systems are used to derive the control damping force acquired by the MR quarter-vehicle suspension system. The proposed modified Bouc-wen hysteretic force-velocity (F-v) model and its inverse model of MR damper, as well as the proposed continuous modulation (CM) filtering algorithm without phase shift are employed to convert the control damping force into the direct drive current of the MR damper. Moreover, the proposed semi-active sliding mode controller (SSMC)-based MR quarter-vehicle suspension is systematically evaluated through comparing the time and frequency domain responses of the sprung and unsprung mass displacement accelerations, suspension travel and the tire dynamic force with those of the passive quarter-vehicle suspension, under three kinds of varied amplitude harmonic, rounded pulse and real-road measured random excitations. The evaluation results illustrate that the proposed SSMC can greatly suppress the vehicle suspension vibration due to uncertainty of the load, and thus improve the ride comfort and handling safety. The study establishes a solid theoretical foundation as the universal control scheme for the adaptive semi-active control of the MR full-vehicle suspension decoupled into four MR quarter-vehicle sub-suspension systems.展开更多
The design and analysis of an intelligent vehicle suspension with MR dampers should address hybrid semi-active control goals, such as rejection of current-switching discontinuity and MR-damper hysteresis, asymmetric d...The design and analysis of an intelligent vehicle suspension with MR dampers should address hybrid semi-active control goals, such as rejection of current-switching discontinuity and MR-damper hysteresis, asymmetric damping from the symmetric MR-damper design, robustness on the vehicle operation parameter uncertainties and consideration of essential multiple suspension goals. Following the proposed skyhook-based asymmetric semi-active controller (Part I ) for achieving the above goals, herein, a set of suspension performance measures and three kinds of varying amplitude harmonic, rounded pulse and really measured random excitations are systematically defined, and the sensitivity of quarter-vehicle MR-suspension performance to variations in operating conditions is thoroughly analyzed. The results illustrate that the proposed skyhook-based semi-active MR-suspension in the asymmetric mode yields relatively superior dynamic responses to meet the multiple suspension performances of ride, rattle space, road-holding and dynamic tire force transmitted to the pavement, and has desirable robustness on variations in operating conditions of vehicle load and speed and the road roughness.展开更多
In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental ...In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e.,“web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model.Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem,we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition(MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition(MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally,compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume(HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.展开更多
Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when mode...Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm (GA), non-dominated sorting in genetic algorithm-Ⅱ (NSGA-Ⅱ) also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ (GNSGA-Ⅱ) is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.展开更多
A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with t...A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with the UAV network,we first consider both achievable secrecy rate maximization and total transmit power minimization,and formulate a multi-objective optimization problem(MOOP)using the weighted Tchebycheff approach.Then,by supposing that only imperfect channel state information based on the angular information is available,we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones.Next,we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector.Finally,simulation results illustrate that the Pareto optimal trade-off can be achieved,and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.展开更多
Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high...Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects.To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes,a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research.The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block,imbedding the SPP block into the detection network,cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task,and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes.A Chinese vehicle license plate image dataset was built for training and testing the improved network,and the location and class of the license plates in each image were accurately labeled.The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates,almost images have multiple license plates with different sizes.Experiments demonstrated that the multilicense plate detection method obtained 83.4%mAP,98.88%precision,98.17%recall,98.52 F1 score,89.196 BFLOPS and 22 FPS on the test dataset,and whole performance was better than the other five compared networks including YOLOv3,SSD,Faster-RCNN,EfficientDet and RetinaNet.展开更多
The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy vehicles.The global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in th...The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy vehicles.The global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in the development of PHEVs.This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs.The algorithm combines the Radau Pseudospectral Knotting Method(RPKM)and the Nondominated Sorting Genetic Algorithm(NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle.The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM.The RPKM results serve as the fitness values in iteration through the NSGA-II method.The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87%improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM.The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality,with the added benefits of faster and more uniform solutions.展开更多
文摘This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.
基金supported by the National Natural Science Foundation of China(61773398 61703421)
文摘A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the traditional prescribed performance control in which the shape of the performance function is constant, this paper exploits new performance functions which can change the shape of their function according to different symbols of initial errors and can ensure the error convergence with a small overshoot. The neural backstepping control and the minimal learning parameters (MLP) technology are employed for exploring a prescribed performance controller (PPC) that provides robust tracking attitude reference trajectories. The highlight is that the transient performance of tracking errors is satisfactory and the computational load of neural approximation is low. The pseudo rate (PSR) modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation and high accuracy.
文摘Multi-objective optimization design of the gas-filled bag cushion landing system is investigated.Firstly,the landing process of airbag is decomposed into a adiabatic compression and a release of landing shock energy,and the differential equation of cylindrical gas-filled bag is presented from a theoretical perspective based on the ideal gas state equation and dynamic equation.Then,the effects of exhaust areas and blasting pressure on buffer characteristics are studied,taking those parameters as design variable for the multiobjective optimization problem,and the solution can be determined by comparing Pareto set,which is gained by NSGA-Ⅱ.Finally,the feasibility of the design scheme is verified by experimental results of the ground test.
文摘The attitude control problem of the kinetic kill vehicle is studied in this work. A new mathematical model of the kinetic kill vehicle is proposed, the linear quadratic regulator technique is used to design the optimal attitude controller, and the pulse-width pulse-frequency modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the reaction thrusters. The methods to select the appropriate parameters of pulse-width pulse-frequency are presented in detail. Numerical simulations show that the performance of the LQR/PWPF approach can achieve good control performance such as pseudo-linear operation, high accuracy, and fast enough tracking speed.
基金Supported by the National Natural Science Foundation of China(6160303040,61433003)Yunnan Applied Basic Research Project of China(201701CF00037)Yunnan Provincial Science and Technology Department Key Research Program(Engineering)(2018BA070)
文摘A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.
基金supported by the Natural Science Foundation of Tianjin(12JCZDJC30300)the Research Foundation of Tianjin Key Laboratory of Process Measurement and Control(TKLPMC-201613)the State Scholarship Fund of China
文摘In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.
基金Supported by the National Natural Science Foundation of China(51375007,51205191)the Visiting Scholar Foundation of the State Key Lab of Mechanical Transmission in Chongqing University+1 种基金the Funds from the Postgraduate Creative Base in Nanjing University of Aeronautics and Astronauticsthe Research Funding of Nanjing University of Aeronautics and Astronautics(NS2013015)
文摘A differential steering system is presented for electric vehicle with motorized wheels and a dynamic model of three-freedom car is built.Based on these models,the quantitative expressions of the road feel,sensitivity,and operation stability of the steering are derived.Then,according to the features of multi-constrained optimization of multi-objective function,a multi-island genetic algorithm(MIGA)is designed.Taking the road feel and the sensitivity of the steering as optimization objectives and the operation stability of the steering as a constraint,the system parameters are optimized.The simulation results show that the system optimized with MIGA can improve the steering road feel,and guarantee the operation stability and steering sensibility.
基金supported by National Natural Science Foundation of China (Grant No. 50875054)National Key Laboratory of Vehicular Transmission of China (Grant No. 51457050105HT0112).
文摘Hydraulic hybrid vehicles (HHV) with secondary regulation technology has the potential of improving fuel economy by operating the engine in the optimum efficiency range and making use of regenerative braking. Hydrostatic transmission technology has the advantage of higher power density and the ability to accept the high rates and high frequencies of charging and discharging, both of which are not favorable for batteries, but the lower energy density requires special power matching design and control strategy to coordinate all the powertrain components in an optimal manner. A multi-objective optimization method is proposed to distinguish the components size values of HHV by considering the requirements of driving cycles and technology aspects. The regenerative braking strategy and energy control strategy based on the optimized HHV is proposed to recovery the braking energy and distribute the regenerated braking energy. Simulation results show that by taking the optimized configuration of HHV, adopting the regenerative braking strategy and energy control strategy are helpful to improve the system efficiency and fuel economy of HHV under urban driving cycles.
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.
基金Supported by National Natural Science Foundation of China(Grant Nos.51475246,51277098)
文摘The vehicle semi-active suspension with magneto-theological damper(MRD) has been a hot topic since this decade, in which the robust control synthesis considering load variation is a challenging task. In this paper, a new semi-active controller based upon the inverse model and sliding mode control (SMC) strategies is proposed for the quarter-vehicle suspension with the magneto-rheological (MR) damper, wherein an ideal skyhook suspension is employed as the control reference model and the vehicle sprung mass is considered as an uncertain parameter. According to the asymptotical stability of SMC, the dynamic errors between the plant and reference systems are used to derive the control damping force acquired by the MR quarter-vehicle suspension system. The proposed modified Bouc-wen hysteretic force-velocity (F-v) model and its inverse model of MR damper, as well as the proposed continuous modulation (CM) filtering algorithm without phase shift are employed to convert the control damping force into the direct drive current of the MR damper. Moreover, the proposed semi-active sliding mode controller (SSMC)-based MR quarter-vehicle suspension is systematically evaluated through comparing the time and frequency domain responses of the sprung and unsprung mass displacement accelerations, suspension travel and the tire dynamic force with those of the passive quarter-vehicle suspension, under three kinds of varied amplitude harmonic, rounded pulse and real-road measured random excitations. The evaluation results illustrate that the proposed SSMC can greatly suppress the vehicle suspension vibration due to uncertainty of the load, and thus improve the ride comfort and handling safety. The study establishes a solid theoretical foundation as the universal control scheme for the adaptive semi-active control of the MR full-vehicle suspension decoupled into four MR quarter-vehicle sub-suspension systems.
基金Senior Visiting Scholarship of Chinese Scholarship Council (No.20H05002)Jiangsu Provincial Natural Science Foundation of Education Commission of China (No.03KJB510072)Jiangsu Provincial Six Categories of Summit Talents of China (No.2OO6194)
文摘The design and analysis of an intelligent vehicle suspension with MR dampers should address hybrid semi-active control goals, such as rejection of current-switching discontinuity and MR-damper hysteresis, asymmetric damping from the symmetric MR-damper design, robustness on the vehicle operation parameter uncertainties and consideration of essential multiple suspension goals. Following the proposed skyhook-based asymmetric semi-active controller (Part I ) for achieving the above goals, herein, a set of suspension performance measures and three kinds of varying amplitude harmonic, rounded pulse and really measured random excitations are systematically defined, and the sensitivity of quarter-vehicle MR-suspension performance to variations in operating conditions is thoroughly analyzed. The results illustrate that the proposed skyhook-based semi-active MR-suspension in the asymmetric mode yields relatively superior dynamic responses to meet the multiple suspension performances of ride, rattle space, road-holding and dynamic tire force transmitted to the pavement, and has desirable robustness on variations in operating conditions of vehicle load and speed and the road roughness.
基金supported by the National Natural Science Foundation of China (72071206,71690233)the Science and Technology Innovation Program of Hunan Province (2020RC4046)。
文摘In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e.,“web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model.Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem,we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition(MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition(MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally,compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume(HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.
基金supported by National Natural Science Foundation of China (No.60474059)Hi-tech Research and Development Program of China (863 Program,No.2006AA04Z160).
文摘Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm (GA), non-dominated sorting in genetic algorithm-Ⅱ (NSGA-Ⅱ) also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ (GNSGA-Ⅱ) is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.
基金supported by the Key International Cooperation Research Project(61720106003)the National Natural Science Foundation of China(62001517)+2 种基金the Shanghai Aerospace Science and Technology Innovation Foundation(SAST2019-095)the NUPTSF(NY220111)the Foundational Research Project of Complex Electronic System Simulation Laboratory(DXZT-JC-ZZ-2019-009,DXZTJC-ZZ-2019-005).
文摘A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with the UAV network,we first consider both achievable secrecy rate maximization and total transmit power minimization,and formulate a multi-objective optimization problem(MOOP)using the weighted Tchebycheff approach.Then,by supposing that only imperfect channel state information based on the angular information is available,we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones.Next,we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector.Finally,simulation results illustrate that the Pareto optimal trade-off can be achieved,and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.
基金supported by the China Sichuan Science and Technology Program under Grant 2019YFG0299the Fundamental Research Funds of China West Normal University under Grant 19B045the Research Foundation for Talents of China Normal University under Grant 17YC163。
文摘Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects.To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes,a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research.The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block,imbedding the SPP block into the detection network,cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task,and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes.A Chinese vehicle license plate image dataset was built for training and testing the improved network,and the location and class of the license plates in each image were accurately labeled.The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates,almost images have multiple license plates with different sizes.Experiments demonstrated that the multilicense plate detection method obtained 83.4%mAP,98.88%precision,98.17%recall,98.52 F1 score,89.196 BFLOPS and 22 FPS on the test dataset,and whole performance was better than the other five compared networks including YOLOv3,SSD,Faster-RCNN,EfficientDet and RetinaNet.
基金supported by the Natural Science Foundation of Guangdong Province under Grant 2020A1515010773the Key-Area Research and Development Program of Guangdong Province under Grant 2019B090912001.
文摘The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy vehicles.The global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in the development of PHEVs.This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs.The algorithm combines the Radau Pseudospectral Knotting Method(RPKM)and the Nondominated Sorting Genetic Algorithm(NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle.The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM.The RPKM results serve as the fitness values in iteration through the NSGA-II method.The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87%improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM.The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality,with the added benefits of faster and more uniform solutions.