A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential forms.Nevertheless,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential stabilization.In comparison with autonomous cases,non-autonomous systems are of great interest and represent an important challenge.Accordingly,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are adopted.It should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Group Hybrid Coordination Control of Multi-Agent Systems With Time-Delays and Additive Noises

A new kind of group coordination control problemgroup hybrid coordination control is investigated in this paper.The group hybrid coordination control means that in a whole multi-agent system(MAS)that consists of two subgroups with communications between them,agents in the two subgroups achieve consensus and containment,respectively.For MASs with both time-delays and additive noises,two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case,respectively.By developing a new analysis idea,some sufficient conditions and necessary conditions related to the communication intensity betw een the two subgroups are obtained for the following two types of group hybrid coordination behavior:1)Agents in one subgroup and in another subgroup achieve weak consensus and containment,respectively;2)Agents in one subgroup and in another subgroup achieve strong consensus and containment,respectively.It is revealed that the decay of the communication impact betw een the two subgroups is necessary for the consensus-oriented case.Finally,the validity of the group control results is verified by several simulation examples.

Coherent manipulation of a tunable hybrid qubit via microwave control

Hybrid qubits enable the hybridization of charge and spin degrees of freedom,which provides a way to realize both a relatively long coherence time and rapid qubit manipulation.Here,we use microwave driving to demonstrate the coherent operation of a tunable hybrid qubit,including X-rotation,Z-rotation,and rotation around an arbitrary axis in the X-Y panel of the Bloch sphere.Moreover,the coherence properties of the qubit and its tunability are studied.The measured coherence time of the X-rotation reaches~14.3 ns.While for the Z-rotation,the maximum decoherence time is~5.8 ns due to the larger sensitivity to noise.By employing the Hahn echo sequence to mitigate the influence of the low-frequency noise,we have improved the qubit coherence time from~5.8 ns to~15.0 ns.Our results contribute to a further understanding of the hybrid qubit and a step towards achieving high-fidelity qubit gates in the hybrid qubit.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

Highly Efficient and Stable Hybrid White Organic Light Emitting Diodes with Controllable Exciton Behavior by a Mixed Bipolar Interlayer

Highly efficient and stable hybrid white organic light-emitting diodes （HWOLEDs） with a mixed bipolar interlayer between fluorescent blue and phosphorescent yellow emitting layers are demonstrated. The bipolar interlayer is a mixture of p-type diphenyl （l0-phenyl-lOH-spiro [acridine-9,9＇-fluoren]-3Lyl） phosphine oxide and n-type 2＇,2- （1,3,5-benzinetriyl）-tris（1-phenyl-l-H-benzimidazole）. The electroluminance and Commission Internationale de l＇Eclairage （CIE1931） coordinates＇ characteristics can be modulated easily by adjusting the ratio of the hole- predominated material to the electron-predominated material in the interlayer. The hybrid WOLED with a p-type：n-type ratio of 1：3 shows a maximum current efficiency and power efficiency of 61.1 ed/A and 55.8 lm/W, respectively, with warm white CIE coordinates of （0.34, 0.43）. The excellent efficiency and adaptive CIE coordi- nates are attributed to the mixed interlayer with improved charge carrier balance, optimized exciton distribution, and enhanced harvesting of singlet and triplet excitons.

Fuzzy Hybrid Control of Vibration Attitude of Full Car via Magneto-rheological Suspensions

A magneto-rheological（MR） semi-active suspension system with the controllable damping forces has received more attention in reducing the vibration of a vehicle. However, many control strategies only discussed one or two vibration states of the vehicle based on a quarter-car model or a half vehicle model via MR suspensions. They cannot provide a satisfying whole-vehicle performance on a road test. Hence, a full car vibration model via an MR suspension system is proposed. To reduce the heave, pitch and roll motion of the vehicle body and the vertical vibration of four wheels, a fuzzy hybrid controller for vibration attitude of full car via MR suspensions is proposed. First, a skyhook-fuzzy control scheme is designed to reduce the heave, roll and pitch motion of the vehicle body. Second, a revised ground hook control strategy is adopted to decrease the vertical vibration of the wheels. Finally, a hybrid control scheme based on a fuzzy reasoning method is proposed to tune the hybrid damping parameter, which is suitable for coordination the attitude of the vehicle body and the wheels. A test and control system for the vibration attitude of full car is set up. It is implemented on a car equipped with four MR suspensions. The results on random highway and rough road indicate that the fuzzy hybrid controller can decrease the vibration accelerations of the vehicle body and the wheels to 65%-80% and 80%-90%, respectively. It reduces the automotive vibrations of heave, roll and pitch more effectively than a passive suspension and an MR suspension with a traditional hybrid control scheme so that it achieves better ride comfort and road holding concurrently. This paper proposes a new fuzzy hybrid control（FHC） method for reducing vibration attitude of full car via MR suspensions and develops a road test to evaluate the FHC.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

Improved Hybrid Robust Control Method for the Electromechanical Actuator in Aircrafts

In the flight process of aircrafts, their electromechanical actuators（EMA） must have the ability of enduring uncertainties caused by factors such as load disturbance, the variation of work temperature and the EMA＇s nonlinearity. At present, in order to increase the EMA＇s robustness on the uncertainties, the H, control method has been applied in aircrafts. The major problems with standard H∞ control lie in the large overshoot of step response and the high orders of the controller. For the purpose of addressing the two problems, this paper investigates several kinds of robust control strategies of the EMA. A mathematical model of the EMA is first built, and then with MATLAB software a H∞ controller and an improved hybrid robust controller composed of a reduced order H∞controller and a lead compensator are designed. In order to make a scientific comparison of the control effects of H∞ controller, hybrid controller and classic proportion-integral-differential（PID） controller, a simulation research is made in respect of the open loop frequency response and the closed loop step response of the three controllers. For comparing the robustness of the three controllers, the load torque is entered as a disturbance and the disturbance response of error and control input are thus obtained. The experiments with the three controllers are also conducted. Through giving the EMA a command and a disturbance torque successively, the transient response and disturbing process of EMA are recorded. The simulation and experiment results show that with the help of the hybrid controller, the EMA not only guarantees good dynamic characteristics, but also has strong robustness of disturbance rejection. Therefore, the excogitated H∞ hybrid control method effectively solves the problem of large overshoot in dynamic response, and moderately meets the requirement of overcoming the uncertainties in the EMA of aircrafts.

Modeling and Control of Hybrid Machine Systems—a Five-bar Mechanism Case

A hybrid machine （HM） as a typical mechatronic device, is a useful tool to generate smooth motion, and combines the motions of a large constant speed motor with a small servo motor by means of a mechnical linkage mechanism, in order to provide a powerful programmable drive system. To achieve design objectives, a control system is required. To design a better control system and analyze the performance of an HM, a dynamic model is necessary. This paper first develops a dynamic model of an HM with a five-bar mechanism using a Lagrangian formulation. Then, several important properties which are very useful in system analysis, and control system design, are presented. Based on the developed dynamic model, two control approaches, computed torque, and combined computed torque and slide mode control, are adopted to control the HM system. Simulation results demonstrate the control performance and limitations of each control approach.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Mathematical Model and Its Hybrid Dynamic Mechanism in Intelligent Control of Ironmaking

A hybrid dynamic model was proposed, which considered both the hydrokinetic and the chaotic properties of the blast furnace ironmaking process; and great emphasis was put on its mechanism. The new model took the high complexity of the blast furnace as well as the effects of main parameters of the model into account, and the predicted results were in very good agreement with actual data.

Hybrid Neural Network Model for RH Vacuum Refining Process Control

A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

Model-Based Fault Tolerant Control for Hybrid Dynamic Systems with Sensor Faults

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.

Control performance comparison between tuned liquid damper and tuned liquid column damper using real-time hybrid simulation

Tuned liquid damper (TLD) and tuned liquid column damper (TLCD) are two types of passive control devices that are widely used in structural control. In this study, a real-time hybrid simulation (RTHS) technique is employed to investigate the diff erence in control performance between TLD and TLCD. A series of RTHSs is presented with the premise of the same liquid length, mass ratio, and structural parameters. Herein, TLD and TLCD are physically experimented, and controlled structures are numerically simulated. Then, parametric studies are performed to further evaluate the diff erent performance between TLD and TLCD. Experimental results demonstrate that TLD is more eff ective than TLCD under diff erent amplitude excitations.

Predictive control for mechanical system with backlash based on hybrid model

The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid system.For eliminating the bad effect of backlash, a piecewise affine（PWA） model of the mechanical servo system with backlash is built.The optimal control of constrained PWA system is obtained by taking advantage of model predictive control（MPC） method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive controller.Furthermore, a piecewise quadratic（PWQ） function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming problem.In the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with backlash.The error meets the demands of real system.Further, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.

HYBRID FUZZY CONTROL FOR ELECTRO-HYDRAULIC ACTIVE DAMPING SUSPENSION

A new control scheme, the hybrid fuzzy control method, for active dampingsuspension system is presented. The scheme is the result of effective combination of the statisticaloptimal control method based on the statistical property of suspension system, with the bang-bangcontrol method based on the real-time characteristics of suspension system. Computer simulations areperformed to compare the effectiveness of hybrid fuzzy control scheme with that of optimal dampingcontrol, bang-bang control, and passive suspension. It takes the effects of time-variant factorsinto full account. The superiority of the proposed hybrid fuzzy control scheme for active dampingsuspension to the passive suspension is verified in the experiment study.