Adaptive Fuzzy Control Scheme Research Articles

Fast finite‐time adaptive fuzzy control for quantized stochastic uncertain nonlinear systems

SummaryThe issue of fast finite‐time adaptive control is studied for quantized stochastic nonlinear systems. Unlike the existing works about fast finite‐time control, the input signals are quantized, and the stochastic disturbances and nonlinear functions are unknown. According to universal approximation capacity of fuzzy logic system, combined with backstepping technique, a novel fast finite‐time adaptive fuzzy control strategy of quantized stochastic nonlinear system is proposed. The nonlinear decomposition method is introduced to set up the relationship among the control signals and the quantization signals, which overcomes the technical difficulties result from the piecewise quantization input. The proposed tactics can assure the tracking error situate in a neighborhood of the origin point and the closed‐loop system signals keep bounded. Finally, an algorithm simulation is conducted to test the validity of the method.

Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation

The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.

Observer-based finite-time adaptive fuzzy back-stepping control for MIMO coupled nonlinear systems.

An attempt is made in this paper to devise a finite-time adaptive fuzzy back-stepping control scheme for a class of multi-input and multi-output (MIMO) coupled nonlinear systems with immeasurable states. In view of the uncertainty of the system, adaptive fuzzy logic systems (AFLSs) are used to approach the uncertainty of the system, and the unmeasured states of the system are estimated by the finite-time extend state observers (FT-ESOs), where the state of the observer is a sphere around the state of the system. The accuracy and efficiency of the control effect are ensured by combining the back-stepping and finite-time theory. It is proved that all the states of the closed-loop adaptive control system are semi-global practical finite-time stability (SGPFS) by the finite-time Lyapunov stability theorem, and the tracking errors of the system states converge to a tiny neighborhood of the origin in a finite time. The validity of this scheme is demonstrated by a simulation.

Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

A reinforcement learning-based adaptive optimal fuzzy controller is proposed for maximum power point tracking (MPPT) control of a variable-speed permanent magnet synchronous generator-based wind energy generation system. The algorithm consists of a critic, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy estimator. The critic is built based on an adaptive neuro-fuzzy inference system (ANFIS) network instead of the neural network as normal to reduce the computation. The error between the system output and the estimator output is used as the input of the critic. In addition, the critic is used to calculate the update law for the parameters of the adaptive optimal fuzzy controller and adaptive optimal fuzzy estimator based on minimizing the input error function. Moreover, the proposed control scheme is output feedback instead of state feedback, which does not require a system model as well as system parameters, so the system is robust to uncertainties and external disturbances. Besides, the stabilization proof is accomplished by using the Lyapunov stability theorem for the closed-loop system and the convergence of the update law. Finally, the effectiveness of the proposed reinforcement learning-based adaptive optimal fuzzy control scheme is verified through simulation with various scenarios such as step wind speed, random wind speed, and system parameter variations. Also, the comparisons with other control schemes in the state-of-art (neural network reinforcement learning based adaptive optimal fuzzy controller, PI controller) are executed to demonstrate the advantages of the proposed control scheme.

Filtering-Error Constrained Angle Tracking Adaptive Learning Fuzzy Control for Pneumatic Artificial Muscle Systems Under Nonzero Initial Errors

In this paper, a filtering-error constrained adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism. The adaptive learning controller is designed by a novel barrier Lyapunov function, and the filtering error of pneumatic artificial muscle system is ensured to be constrained during each iteration. The initial position problem of iterative learning control is solved by utilizing time-varying boundary layer method. Fuzzy logic system is applied to approximate the unknown nonparametric uncertainties in the pneumatic artificial muscle system, whose optimal weight is estimated by using difference learning approach. The approximation error of fuzzy logic system is tackled by robust control strategy. Simulation results show the effectiveness of the propose angle tracking adaptive learning fuzzy control scheme.

Event-triggered adaptive fuzzy fault-tolerant control for autonomous underwater vehicles with prescribed tracking performance

In this paper, we solve the trajectory tracking control problem of autonomous underwater vehicles (AUVs) with actuator faults and model uncertainties. In order to achieve the output tracking with prescribed transient performance, we develop a novel tan-type barrier Lyapunov function (BLF)-based funnel tracking control strategy to constrain the position tracking error of the AUV within prespecified performance funnels. Furthermore, to maintain the predesigned transient tracking performance of the AUV when the actuator fault occurs, an event-triggered adaptive fuzzy fault-tolerant control (FTC) scheme is proposed to compensate actuator faults. Meanwhile, to handle the uncertainties caused by the system model, fuzzy logic systems (FLSs) are adopted to approximate the unknown hydrodynamic parameters and actuator fault parameters. Different from the existing results, the communication burden and the excess wear from the controller to the actuators are reduced utilising the event-triggered mechanism. By using the theory of Lyapunov stability, it is proven that all the signals of the closed-loop system are semiglobally uniformly bounded. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

Adaptive Fuzzy Backstepping Tracking Control for Flexible Robotic Manipulator

In this paper, an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system. The considered system contains unknown nonlinear function and actuator saturation. Fuzzy logic systems (FLSs) and a smooth function are used to approximate the unknown nonlinearities and the actuator saturation, respectively. By combining the command-filter technique with the backstepping design algorithm, a novel adaptive fuzzy tracking backstepping control method is developed. It is proved that the adaptive fuzzy control scheme can guarantee that all the variables in the closed-loop system are bounded, and the system output can track the given reference signal as close as possible. Simulation results are provided to illustrate the effectiveness of the proposed approach.

Robust output regulation for unknown nonlinear strict‐feedback systems with input delay

AbstractThis article investigates the robust output regulation problem for unknown nonlinear strict‐feedback systems with input delay. Nonlinear fuzzy internal model and adaptive fuzzy backstepping control are integrated to obtain a feedforward‐feedback composite controller. As a distinctive feature, Pade approximation technique is introduced to deal with the input delay of nonlinear output regulation problem for the first time. The process of control strategy can be divided into two steps. In the first step, a novel nonlinear fuzzy internal model is designed to get the feedforward compensator. In the second step, an adaptive fuzzy backstepping control scheme is proposed to get the feedback controller, which can guarantee semiglobally uniformly ultimately bounded of the closed‐loop system. Simulation results of an interconnected power system show the validity of the theoretical results.

Robust Adaptive Fuzzy Control via State-Dependent Function for Nonlinear Stochastic Large-Scale Systems Subject to Dead Zones

This paper concentrates on the adaptive fuzzy control problem for stochastic nonlinear large-scale systems with constraints and unknown dead zones. By introducing the state-dependent function, the constrained closed-loop system is transformed into a brand-new system without constraints, which can realize the same control objective. Then, fuzzy logic systems (FLSs) are used to identify the unknown nonlinear functions, the dead zone inverse technique is utilized to compensate for the dead zone effect, and a robust adaptive fuzzy control scheme is developed under the backstepping frame. Based on the Lyapunov stability theory, it is proved ultimately that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. Finally, an example based on an actual system is given to verify the effectiveness of the proposed control scheme.

Event-triggered fuzzy adaptive control of nonlinear switched systems with predefined accuracy and mismatched switching

A fuzzy adaptive switching event-triggered control strategy is obtained for a class of uncertain nonlinear switched systems with predefined accuracy. Since the switching may occur between two adjacent sampling instants and may lead to trigger a new event, it raises the mismatched switching and brings severe challenges to avoid the Zeno behavior of the subsystem controllers. The formidable problems are successfully addressed by a new switching event-triggering mechanism without restrictive assumptions. With the proposed fuzzy adaptive switching control approach, the adverse effects of mismatched switching between the subsystem and the controller are addressed, and no subsystem controllers exhibit the Zeno behavior. Furthermore, by using a projection operator-based adaptation strategy and constructing a class of continuous functions, the convergence accuracy of the switched system appears explicitly in design parameters. It allows designers to make decisions in advance based on the requirement of convergence accuracy. Finally, theoretical results are verified by two illustrative examples.

Robust Fuzzy Adaptive Control with MRAC Configuration for a Class of Fractional Order Uncertain Linear Systems

This paper investigates a novel robust fractional adaptive control design for a class of fractional-order uncertain linear systems. Based on the Model Reference Adaptive Control (MRAC) configuration, the objective of the proposed controller is to ensure the output of the controlled plant to track the output of a given reference model system, while maintaining the overall closed-loop stability despite external disturbances and model uncertainties. An adaptive fuzzy logic controller is employed to eliminate unknown dynamics and disturbance. Lyapunov stability analysis demonstrates and verifies the desired fractional adaptive control system stability and tracking performance. Numerical simulation results illustrate the efficiency of the proposed adaptive fuzzy control strategy to deal with uncertain and disturbed fractional-order linear systems.

Multi-VSM based fuzzy adaptive cooperative control strategy for MVDC traction power supply system

This paper studies the control of a medium-voltage DC (MVDC) traction power supply system for rail transit (RT). In order to optimize the ability of DC voltage fluctuation and frequency regulation of rail trains, a fuzzy adaptive cooperative secondary control strategy based on improved virtual synchronous machine (VSM) is proposed. Firstly, the improved VSM control strategy is adopted in the traction substations (TSSs), which makes the RT trains have the external characteristics of synchronous generator and have certain inertia and damping support capacity, so as to improve the ability of the RT traction system to cope with traction network transient changes. Secondly, the virtual inertia and virtual damping can be dynamically adjusted by the system frequency deviating from the nominal steady-state frequency, and the dynamic fuzzy adjustment can be carried out by the fuzzy logic system to slow down the frequency fluctuation. Then, due to the different power supply distances and parameter settings between multiple TSSs, cooperative control is used to coordinate local TSS perceptions of information between adjacent TSS to achieve consistency of frequency response of multiple TSSs under different parameters. Besides, a time-varying switching topology handoff method is considered to select the optimal communication topology between adjacent base stations. Finally, simulation results verify the effectiveness of the proposed control strategy.

Singularity-Free Fixed-Time Fuzzy Control for Robotic Systems With User-Defined Performance

In this article, the singularity-free adaptive fuzzy fixed-time control problem is studied for an uncertain n-link robotic system with the position tracking error constraint. The controlled robotic system can be described as a multiple-input-multiple-output system.To implement the user-defined performance, an improved error conversion mechanism based on performance functions is presented such that the converted error is limited to an interval greater than zero, and an appropriate barrier Lyapunov function (BLF) is constructed to avoid the breach of position tracking error constraint. The fuzzy approximator is utilized to estimate the unknown functions. The significance and challenges of this article are to establish a new error conversion mechanism and design corresponding BLF that can be integrated into fixed-time control design to present a singularity-free adaptive fuzzy fixed-time control scheme. Benefits of the proposed adaptive fixed-time controller in comparison to the current approaches are that it cannot cause the singularity issue appearing in backstepping-based fixed-time control design and ensures quick transient response. Combining with Lyapunov stability theory, the boundedness of the closed-loop signals is ensured, and the position tracking error can be constrained in the user-defined performance boundaries. Finally, simulation results demonstrate the feasibility of the proposed control strategy.

Fuzzy‐based adaptive event‐triggered control for switched stochastic nonlinear systems with state constraints

AbstractIn this paper, a fuzzy‐based control strategy is devised for switched stochastic nonlinear systems with state constraints. All states are guaranteed to maintain the prescribed regions whereby barrier Lyapunov function. To mitigate the data transmission and save resource, the event‐triggered mechanism (ETM) is exploited to update control signal. To further deal with system uncertainties, the fuzzy logic systems are taken as function approximators. By combining the backstepping technique with event‐triggered mechanism, the adaptive fuzzy control (AFC) strategy is recursively established. In virtue of Lyapunov theory and average dwell time method (ADT), the devised AFC method can guarantee that the entire closed‐loop systems are uniformly bounded and the prescribed state constraints are not violated. Finally, the validity of the developed method is illustrated via a simulation example.

Adaptive Fuzzy Actuator Failure Compensation Control for a Class of MIMO Nonlinear Systems

In this paper, an adaptive fuzzy control scheme is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with uncertain actuator failures. Within this scheme, a fixed grouping of redundant actuators and a proportional actuation scheme for each group are considered. Afterward, a fuzzy system is used for the online approximation of a nominal control vector. The parameter adaptation laws for the nominal controller are driven by the prediction error, which is defined as the difference between the unknown nominal controller and the fuzzy controller. The proposed controller can compensate for both total and partial loss of effectiveness failure types. Closed-loop system stability and convergence properties of the tracking errors are proved based on Lyapunov theory. A piecewise analysis is carried out to prove that these properties hold despite the presence of parameter jumps caused by abrupt failures. A simulation study is carried out on a redundant joint two-link robot manipulator with joint failures at each link. Simulation results show the effectiveness of the proposed control scheme.

Fixed‐time cooperative control for robotic manipulators with motion constraints using unified transformation function

AbstractThis article investigates the fixed‐time consensus tracking control problem for multiagent systems composed of uncertain l‐link robotic manipulators with position and velocity constraints. Fuzzy logic systems are employed to model the uncertain dynamic of the robotic manipulators. In order to cope with the asymmetric yet time‐varying constraints, a nonlinear transformation function is introduced. With the help of the nonlinear transformation function, the original constrained manipulator systems are converted into the equivalent systems without constraint. As long as the boundedness of the state variables in the transformed systems is ensured, the constraint conditions of the original systems are not violated. Then, a fixed‐time adaptive fuzzy control scheme is developed. Under the proposed control method, the consensus tracking error can converge into a small neighborhood of origin in fixed‐time, and all the signals in the closed‐loop systems are bounded. Meanwhile, the motion constraints are not violated all the time. Specially, in view of the property of nonlinear transformation function, it is not necessary to change the control structure when the systems are subjected to no state constraint. Finally, a simulation is presented to illustrate the feasibility and effectiveness of implementing the proposed control algorithms.

Small‐gain technique‐based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances

SummaryThis article studies the adaptive tracking control problem for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances. First, a fuzzy state observer is established to estimate unmeasurable states. To overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, the command filter with a compensation mechanism is applied to the controller design procedure. Meanwhile, with the help of the fuzzy logic systems and the backstepping technique, an adaptive fuzzy control scheme is proposed, which guarantees that all signals in the closed‐loop systems are bounded, and the tracking error can converge to a small region around the origin. Furthermore, the stability of the systems is proven to be input‐to‐state practically stable based on the small‐gain theorem. Finally, a simulation example verifies the effectiveness of the proposed control approach.

Adaptive Fuzzy Output-Feedback Control Design for a Class of p-Norm Stochastic Nonlinear Systems With Output Constraints

This paper considers the control problem of p-norm stochastic nonlinear systems with output constraints, while the system nonlinearities are completely unknown and the system states are unavailable except the output. A nonlinear observer is constructed to estimate the unmeasurable states. Then, based on the constructed observer and a tan-type barrier Lyapunov function (BLF), an adaptive fuzzy output-feedback control strategy is developed by combining the technique of adding a power integrator with the fuzzy logic systems (FLSs). The proposed scheme enables that all the signals of the considered closed-loop systems are bounded in probability while the prespecified output constraint is not violated. Finally, a numerical example verifies the validation of the proposed scheme.

Fixed-time adaptive fuzzy control for a class of non-strict-feedback uncertain non-linear systems

This paper addresses the fixed-time adaptive fuzzy control problem for a class of uncertain non-linear systems in non-strict-feedback form. A novel fixed-time adaptive fuzzy backstepping control (BC) scheme is proposed by integrating the fuzzy logic systems and fixed-time control technique into each step of the BC design. Rigorous theoretical analysis for the practical fixed-time stability of the resulting closed-loop system is provided. The proposed controller can guarantee that the tracking errors of the considered systems converge to the small regions about the origin in fixed time with a relatively simple structure. To the best of the authors’ knowledge, there are only really limited existing controllers that can achieve such excellent performance under the same conditions. Finally, two simulation examples illustrate the effectiveness and superiority of the proposed control scheme.