Multiconstraint adaptive three-dimensional guidance law using convex optimization

The traditional guidance law only guarantees the accuracy of attacking a target. However, the look angle and acceleration constraints are indispensable in applications. A new adaptive three-dimensional proportional navigation(PN) guidance law is proposed based on convex optimization. Decomposition of the three-dimensional space is carried out to establish threedimensional kinematic engagements. The constraints and the performance index are disposed by using the convex optimization method. PN guidance gains can be obtained by solving the optimization problem. This solution is more rapid and programmatic than the traditional method and provides a foundation for future online guidance methods, which is of great value for engineering applications.

Logic-based Reset Adaptation Design for Improving Transient Performance of Nonlinear Systems

In this paper, logic-based switching and resetting principles are presented to devise adaptive control laws for a class of uncertain nonlinear systems in order to ensure both the transient bound and the asymptotical convergence of the state. A novel supervisor is constructed to decide when to reset the estimation parameter with the pre-given estimation value. A benchmark example is presented to demonstrate the effectiveness of the approach.

Improving the Control Energy in Model Reference Adaptive Controllers Using Fractional Adaptive Laws

This paper presents the analysis of the control energy consumed in model reference adaptive control(MRAC)schemes using fractional adaptive laws, through simulation studies. The analysis is focused on the energy spent in the control signal represented by means of the integral of the squared control input(ISI). Also, the behavior of the integral of the squared control error(ISE) is included in the analysis.The orders of the adaptive laws were selected by particle swarm optimization(PSO), using an objective function including the ISI and the ISE, with different weighting factors. The results show that, when ISI index is taken into account in the optimization process to determine the orders of adaptive laws,the resulting values are fractional, indicating that control energy of the scheme might be better managed if fractional adaptive laws are used.

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

High-speed train cooperativecontrol based on fractional-ordersliding mode adaptive algorithm

Purpose–This study aims to propose an adaptive fractional-order sliding mode controller to solve the problem of train speed tracking control and position interval control under disturbance environment in moving block system,so as to improve the tracking efficiency and collision avoidance performance.Design/methodology/approach–The mathematical model of information interaction between trains is established based on algebraic graph theory,so that the train can obtain the state information of adjacent trains,and then realize the distributed cooperative control of each train.In the controller design,the sliding mode control and fractional calculus are combined to avoid the discontinuous switching phenomenon,so as to suppress the chattering of sliding mode control,and a parameter adaptive law is constructed to approximate the time-varying operating resistance coefficient.Findings–The simulation results show that compared with proportional integral derivative(PID)control and ordinary sliding mode control,the control accuracy of the proposed algorithm in terms of speed is,respectively,improved by 25%and 75%.The error frequency and fluctuation range of the proposed algorithm are reduced in the position error control,the error value tends to 0,and the operation trend tends to be consistent.Therefore,the control method can improve the control accuracy of the system and prove that it has strong immunity.Originality/value–The algorithm can reduce the influence of external interference in the actual operating environment,realize efficient and stable tracking of trains,and ensure the safety of train control.

Adaptive fuzzy sliding mode control for robotic airship with model uncertainty and external disturbance

An adaptive fuzzy sliding mode control （AFSMC） ap- proach is proposed for a robotic airship. First, the mathematical model of an airship is derived in the form of a nonlinear control system. Second, an AFSMC approach is proposed to design the attitude control system of airship, and the global stability of the closed-loop system is proved by using the Lyapunov stability theorem. Finally, simulation results verify the effectiveness and robustness of the proposed control approach in the presence of model uncertainties and external disturbances.

Intelligent technology-based control of motion and vibration using MR dampers

Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.In this study,two control strategies are proposed for protecting buildings against dynamic hazards,such as severe earthquakes and strong winds,using one of the most promising semiactive control devices,the magnetorheological (MR) damper.The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper.These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms.The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms.The second control strategy involves the design of a fuzzy controller and an adaptation law.The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn't require a prior knowledge of the combined building-damper system,this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Adaptive fuzzy synchronization for a class of fractional-order neural networks

In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.

Adaptive Sliding-Mode Control of an Automotive Electronic Throttle in the Presence of Input Saturation Constraint

In modern vehicles, electronic throttle(ET) has been widely utilized to control the airflow into gasoline engine. To solve the control difficulties with an ET, such as strong nonlinearity,unknown model parameters and input saturation constraints,an adaptive sliding-mode tracking control strategy for an ET is presented. Compared with the existing control strategies for an ET, input saturation constraints and parameter uncertainties are adequately considered in the proposed control strategy. At first, the nonlinear dynamic model for control of an ET is described. According to the dynamical model, the nonlinear adaptive sliding-mode tracking control method is presented,where parameter adaptive laws and auxiliary design system are employed. Parameter adaptive law is given to estimate the unknown parameter with an ET. An auxiliary system is designed,and its state is utilized in the tracking control method to handle the input saturation. Stability proof and analysis of the adaptive sliding-mode control method is performed by using Lyapunov stability theory. Finally, the reliability and feasibility of the proposed control strategy are evaluated by computer simulation.Simulation research shows that the proposed sliding-mode control strategy can provide good control performance for an ET.

Adaptive synchronization of chaos in permanent magnet synchronous motors based on passivity theory

An adaptive synchronization control method is proposed for chaotic permanent magnet synchronous motors based on the property of a passive system. We prove that the controller makes the synchronization error system between the driving and the response systems not only passive but also asymptotically stable. The simulation results show that the proposed method is effective and robust against uncertainties in the systemic parameters.

Generalized projective synchronization of fractional-order complex networks with nonidentical nodes

This paper investigates the synchronization problem of fractional-order complex networks with nonidentical nodes. The generalized projective synchronization criterion of fractional-order complex networks with order 0 〈 q 〈 1 is obtained based on the stability theory of the fractional-order system. The control method which combines active control with pinning control is then suggested to obtain the controllers. Furthermore, the adaptive strategy is applied to tune the control gains and coupling strength. Corresponding numerical simulations are performed to verify and illustrate the theoretical results.

Fast self-adapting high-order sliding mode control for a class of uncertain nonlinear systems

A fast self-adapting high-order sliding mode(FSHOSM)controller is designed for a class of nonlinear systems with unknown uncertainties.As for uncertainty-free nonlinear system,a new switching condition is introduced into the standard geometric homogeneity.Different from the existing geometric homogeneity method,both state variables and their derivatives are considered to bring a reasonable effective switching condition.As a result,a faster convergence rate of state variables is achieved.Furthermore,based on the integral sliding mode(ISM)and above geometric homogeneity,a self-adapting high-order sliding mode(HOSM)control law is proposed for a class of nonlinear systems with uncertainties.The resulting controller allows the closed-loop system to conduct with the expected properties of strong robustness and fast convergence.Stable analysis of the nonlinear system is also proved based on the Lyapunov approach.The effectiveness of the resulting controller is verified by several simulation results.

Robust fault diagnosis for a class of nonlinear systems

Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.

Enhanced adaptive nonlinear extended state observer for pure feedback systems with matched and mismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.

Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

Quadrotors play a significant role in our lives and are transforming our lives.Transporting cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control field.Nonetheless,the load swing and unpredictability pose significant challenges to the quadrotor's stability.In this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is presented.To facilitate the controller design,the outer position dynamics are restructured in the form of cascades.Then,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's stability.To improve robustness,an adaptive law is used to eliminate the effects of uncertain cable length.Lastly,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual force.It is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length uncertainties.Comparative results are provided to demonstrate the efficacy and durability of the proposed method.

Decentralized adaptive sliding mode control of a space robot actuated by control moment gyroscopes

An adaptive sliding mode control （ASMC） law is proposed in decentralized scheme for trajectory tracking control of a new concept space robot. Each joint of the system is a free ball joint capable of rotating with three degrees of freedom （DOF）. A cluster of control moment gyroscopes （CMGs） is mounted on each link and the base to actuate the system. The modified Rodrigues parameters （MRPs） are employed to describe the angular displacements, and the equations of motion are derived using Kane＇s equations. The controller for each link or the base is designed sep- arately in decentralized scheme. The unknown disturbances, inertia parameter uncertainties and nonlinear uncertainties are classified as a ＂lumped＂ matched uncertainty with unknown upper bound, and a continuous sliding mode control （SMC） law is proposed, in which the control gain is tuned by the improved adaptation laws for the upper bound on norm of the uncertainty. A gen- eral amplification function is designed and incorporated in the adaptation laws to reduce the control error without conspicuously increasing the magnitude of the control input. Uniformly ultimate boundedness of the closed loop system is proved by Lyapunov＇s method. Simulation results based on a three-link system verify the effectiveness of the proposed controller.

Variational method based robust adaptive control for a guided spinning rocket

In this paper,a robust adaptive controller is designed for a guided spinning rocket,whose dynamics presents the characteristics of pitch-yaw cross coupling,fast time-varying aerodynamics parameters and wide flight envelop.First,a coupled nonlinear six-degree-of-freedom equation of motion for a guided spinning rocket is developed,and the lateral acceleration motion is modeled as a control plant with time-varying matched uncertainties and unmodeled dynamics.Then,a robust adaptive control method is proposed by combining Bregman divergence and variational method to achieve fast adaption and maintain bounded tracking.The stability of the resulting closed-loop system is proved,and the ultimate bound and convergence rate are also analyzed.Finally,numerical simulations are performed for a single operating point and the whole flight trajectory to show the robustness and adaptability of the proposed method with respect to timevarying uncertainties and unmodeled dynamics.

Adaptive Backstepping Sliding Mode Control of Uncertain Semi-Strict Nonlinear Systems and Application to Permanent Magnet Synchronous Motor

A disturbance observer(DOB)based-backstepping sliding mode control scheme is discussed for a class of semi-strict nonlinear system with unknown parameters and mismatched uncertainty.Firstly,adaptive technique and DOB are respectively applied to tackle the unknown parameters and mismatched uncertainty,where the DOB can effectively alleviate the chattering problem of sliding mode control(SMC).Then,exponential sliding mode surface is proposed to improve the convergence rate of the sliding mode state.The‘explosion of complexity’problem inherent in conventional backstepping control is overcome by designing the novel first-order filter.The stability of the closed-loop system is analyzed in the framework of Lyapunov stability theory,in which the tracking error converges to an arbitrarily small neighborhood around zero(ASNZ).At last,two examples are given to illustrate the effectiveness of the proposed control strategy.

Adaptive high-order sliding mode control based on quasi-time delay estimation for uncertain robot manipulator

This paper presents the design,and validation of a new adaptive control system based on quasi-time delay estimation(Q-TDE)augmented with new integral second-order terminal sliding mode control(ISOTSMC)for a manipulator robot with unknown dynamicuncertainty and disturbances.Contrary to the conventional[TDE,the proposed Q-TDE becomes sufficient to invoke a fixed artficial time delay and utilize the past data only of the control input to approximate the unknown system's dynamic uncertainties.The incorporating of new adaptive reachinglaw with ISOTSMCaugmented with Q-TDE policy ensures the continuous performance tracking of the robot manipulator's trajectories using output feedback.This combination may achieve high performance with a significant chattering reducing procedure.By utilizing the Lyapunov function theory,it can be demonstrated that the robot system is stable and all signals in closed-loop are converging in finite time.Consequently,Simulation and comparative studies with two degrees of freedom robot manipulator were carried out to validate the effectiveness of the designed control scheme.