Adaptive Fuzzy Tracking Control Research Articles

Adaptive Fuzzy Tracking Control for Strict-Feedback Markov Jumping Nonlinear Systems With Actuator Failures and Unmodeled Dynamics.

In this paper, an adaptive fuzzy tracking controller is developed for a class of strict-feedback Markovian jumping systems subjected to multisource uncertainties. The unpredictable actuator failures, the unknown nonlinearities, and the unmodeled dynamics are simultaneously taken into consideration, which evolve according to the Markov chain. It is noted that the elements in the transition rate matrix of the Markov chain are not fully available. In virtue of the norm estimation approach, the challenges caused by the complex multiple uncertainties and actuator failures are effectively handled. Furthermore, to compensate for the unavailable switching nonlinearities, the fuzzy logic systems are employed as online approximators. As a result, a novel adaptive fuzzy fault-tolerant tracking control structure is constructed. The sufficient condition is provided to guarantee that the studied system is stochastically stable. Finally, a number of illustrative examples are employed to demonstrate the effectiveness of the proposed methodology.

Event-triggered adaptive fuzzy tracking control of nonlinear MIMO systems

ABSTRACTThis paper addresses the problem of an adaptive fuzzy event-triggered control (ETC) for uncertain multi-input and multi-output nonlinear systems. To reduce the communication burden of the network control systems, a novel state-dependent event-triggering condition is designed to decide when to update the controllers. By combining the backstepping and event-trigged techniques, the adaptive fuzzy ETC strategies are developed and the resulting closed-loop system is semi-global bounded. Finally, the analytical results are substantiated using simulation studies.

Observer-Based Fuzzy Adaptive Sensor Fault Compensation for Uncertain Nonlinear Strict-Feedback Systems

This paper investigates the adaptive fuzzy tracking control problem for a class of uncertain nonlinear strict-feedback systems under sensor faults and unmeasured states. To address the challenges incurred by the partial loss of effectiveness of sensors, a novel observer-based adaptive fault-tolerant controller is designed. Different from the existing state observer design methods, the new state observer contains an adaptive fault compensation mechanism. By employing a cubic absolute-value Lyapunov function, it is proved that the proposed fault-tolerant controller guarantees that all the signals in the closed-loop system are bounded and the tracking performance of the system can be guaranteed even if the sensor fault occurs. Simulation results verify the effectiveness of the proposed method.

Observer-Based Composite Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Actuator Failures

This paper studies the observer-based adaptive fuzzy tracking control problem for a general class of multi-input-single-output nonstrict-feedback systems subject to unmeasured states and actuator failures. For actuator failures, both cases of lock-in-place and loss of effectiveness are synchronously considered. To handle the unknown nonlinear functions, fuzzy logic systems are employed. By constructing a fuzzy observer and a serial-parallel estimation model, the unmeasured states are estimated and the accuracy of approximating the unknown functions is improved. Moreover, taking into account the prediction error between the fuzzy observer and the serial-parallel estimation model, a novel composite fuzzy output-feedback control scheme is developed. Unlike some existing control schemes for systems with actuator failures, the developed control scheme allows one to avoid the problem of “explosion of complexity” and improve the approximation performance. It is proved that all signals in the system are bounded and the tracking error converges to a small neighborhood of the origin by choosing appropriate parameters. Finally, the effectiveness of the proposed method is confirmed via simulation examples with actuator failures.

Adaptive Fuzzy Tracking Control Based Barrier Functions of Uncertain Nonlinear MIMO Systems With Full-State Constraints and Applications to Chemical Process

An adaptive control approach based on the fuzzy systems for a class of uncertain nonlinear multi-input multi-output (MIMO) systems is presented in this paper. This class of systems is in the nested multiple coupling structure and their states are constrained in the corresponding compact sets. The properties of the system structure are inevitable to bring about a complicated design and a difficult task. The fuzzy logic systems are employed to approximate the unknown functions of systems, and the decoupling backstepping way is proposed to design the stability controller and adaptation laws. Barrier Lyapunov functions (BLFs) are constructed in the backstepping design to guarantee that the constraint bounds are not violated. Based on Lyapunov analysis in barrier form, we can prove the stability of the closed-loop system. Two simulation examples are viewed to verify the feasibility of the approach.

Research on path tracking of intelligent vehicle based on optimal deviation control

ABSTRACTAiming at the problem that the transformer substation intelligent detection of vehicles in the process of moving away from the planning path, based on the four-wheel model vehicle, this paper proposes a fuzzy adaptive PID path tracking control method based on optimal lateral deviation. Firstly, the kinematic model and the equation of the linear 2-DOF of the vehicle are established by taking the front wheel steering angle of the model vehicle as the input of the control system. Secondly, the linear quadratic regulator (LQR) is introduced to minimize the transverse tracking error between the model vehicle and the expected path. Finally, the control parameters of the fuzzy PID are updated with the lateral deviation and the variation rate of the deviation, and the front wheel steering angle of the Vehicle is adjusted adaptively to realize the stable tracking of the desired path. The simulation results show that the tracking error of the proposed path tracking control method is concentrated between -0.08m and +0.08m, and the desired path can be better tracked.

Second-Order Sliding Mode Disturbance Observer-Based Adaptive Fuzzy Tracking Control for Near-Space Vehicles with Prescribed Tracking Performance

An adaptive fuzzy fault-tolerant tracking controller is developed for Near-Space Vehicles (NSVs) suffering from quickly varying uncertainties and actuator faults. For the purpose of estimating and compensating the mismatched external disturbances and modeling errors, a second-order sliding mode disturbance observer (SOSMDO) is constructed. By introducing the norm estimation approach, the negative effects of the quickly varying multiple matched disturbances can be handled. Meanwhile, a hierarchical fuzzy system (HFS) is employed to approximate and compensate the unknown nonlinearities. Several performance functions are introduced and the original system is transformed into one incorporating the desired performance criteria. Then, an adaptive fuzzy tracking control structure is established for the transformed system, and the predefined transient tracking performance can be guaranteed. The rigorous stability of the closed-loop system is proved by using the Lyapunov method. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Fuzzy adaptive non-affine attitude tracking control for a generic hypersonic flight vehicle

A fuzzy adaptive non-affine attitude tracking control method is proposed for a generic hypersonic flight vehicle (HFV). Due to the complexities of the hypersonic flows and nonlinear dynamics, the aerodynamic coefficients of the HFV are dependent not only on the attack of angle, sideslip angle, angular rates, and Mach number, but also on the deflection angles of the control surfaces. This cause the attitude tracking control problem becomes a non-affine multi-input–multi-output (MIMO) one. By analyzing the characteristics of the aerodynamic coefficients, it can be found that the non-affine terms have a great influence on the attitude dynamics and should not be ignored. Then, a non-affine MIMO attitude tracking controller is designed using fuzzy sliding mode adaptive techniques with consideration of the non-affine nonlinear aerodynamic coefficients. The proposed controller has good practicability because it does not need to know the exact bound values of the uncertainties, unmodeled dynamics, and external disturbances. Finally, simulation results show that the attitude angles can track the desired signals robustly and asymptotically under the control.

Adaptive Tracking Control for a Class of Switched Nonlinear Systems Under Asynchronous Switching

This paper investigates the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems under asynchronous switching. Inspired by the discretized Lyapunov function technique, a class of time-schedule Lyapunov functions (TSLFs) is first proposed to analyze the stability under asynchronous switching. Then, based on the proposed TSLF, novel observer and adaptive laws with time-schedule observer gain and design parameters are proposed to deal with asynchronous switching. Moreover, a switched command filter with the virtual control functions as input is proposed to avoid “the explosion of complexity” and tracking error jumps, which make the stability analysis difficult. It is shown that under the proposed controller, all the signals of the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero in spite of asynchronous switching. Finally, the effectiveness of the proposed method is illustrated through two illustrative examples.

Decentralized adaptive fuzzy tracking control for a class of uncertain large-scale systems with actuator nonlinearities

This paper studies the problem of decentralized adaptive tracking control for a class of nonlinear large-scale systems with unmeasurable states and actuator nonlinearities. For each subsystem, a novel decentralized disturbance rejection filter is designed to estimate both system states and external disturbances. The decentralized controller with adaptive laws is designed based on back-stepping techniques. It is proved that all the signals of the resulting closed-loop system are bounded, and the output tracking errors of subsystems converge to a desired neighborhood of origin. Simulation results illustrate the effectiveness of the method proposed in this paper.

Adaptive Output Fuzzy Fault Accommodation for a Class of Uncertain Nonlinear Systems With Multiple Time Delays

This paper is concerned with the problem of robust adaptive output fuzzy tracking control for a class of uncertain nonlinear time-delayed systems with actuator failures, which include loss of effectiveness, outage, and stuck modes. First, a novel disturbance rejection filter is designed to estimate the unmeasured states and the external disturbances. Then, by using fuzzy logic systems to approximate the suitable nonlinear compound functions, the corresponding adaptive fuzzy fault-tolerant controller with the adjustable parameter updated laws is established based on back-stepping method. Furthermore, it is shown that all the closed-loop signals are bounded, and the system output tracking error can converge to a small neighborhood in the presence of parameter uncertainty, mismatched disturbance, multiple time delays, and actuator faults. Finally, simulation results are provided to illustrate the efficiency of the proposed adaptive fuzzy tracking control scheme.

Fuzzy Tracking Control for Switched Uncertain Nonlinear Systems With Unstable Inverse Dynamics

This paper investigates the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems with unstable inverse dynamics via time-dependent switching. Adaptive backstepping technique and fast average dwell time switching are exploited to set up the adaptive fuzzy control scheme for the systems under consideration. It is shown that the designed controllers and switching signals can ensure that all the signals remain bounded and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed design method.

Switched Adaptive Fuzzy Tracking Control for a Class of Switched Nonlinear Systems Under Arbitrary Switching

This paper deals with the problem of adaptive tracking control for a class of switched uncertain nonlinear systems under arbitrary switching. First, combing fuzzy approximation and dynamic surface control (DSC), a switched adaptive state-feedback control scheme is proposed based on directly tuning the estimation of the switching ideal weight vectors in fuzzy logic systems. Switched adaptive laws and switched first-order filters in DSC are designed at each step in the backstepping to reduce the conservativeness caused by adoption of common adaptive laws and filters for each subsystem. By constructing a novel common Lyapunov function, the boundedness of the closed-loop system is ensured, while the tracking error converges to a small neighborhood of the origin. Next, based on the estimation of ideal weight vectors, the proposed adaptive control scheme is extended to the output-feedback case where a switched fuzzy observer is first proposed to estimate the unmeasured states. The main advantage of the developed adaptive control schemes is that the changes of plant can be considered explicitly due to switching, which contributes to less conservativeness of the designed controllers. Finally, two simulation results illustrate the effectiveness of the proposed schemes.

Observer‐based robust adaptive T2 fuzzy tracking control for flexible air‐breathing hypersonic vehicles

This study presents an observer-based robust adaptive type-2 (T2) fuzzy tracking control scheme for the longitudinal dynamics of flexible air-breathing hypersonic vehicles. The control scheme is composed of three parts, namely an uncertain dynamics approximator, a reduced-order adaptive state observer, and command filter-based backstepping altitude and velocity controllers. First, the approximator is implemented by utilising interval T2 fuzzy logic systems for approximating unknown vehicle dynamics in the control scheme. Then, the reduced-order adaptive state observer is designed by the combination of the approximator and output state feedback techniques, which guarantee the estimation of unmeasurable states as well as uncertain dynamics identification. By using the linear matrix inequalities technique, a new condition for obtaining the gains of the state observer is presented. Next, on the basis of the designed approximator and state observer, the altitude and velocity tracking controllers are constructed by command-filtered backstepping control approach which avoids the explosion of complexity in conventional backstepping design. Thorough stability analysis of the closed-loop systems is conducted. Nominal and comparison simulation studies considering parametric uncertainty, incomplete state measurement, and measurement noises are carried out, illustrating the effectiveness of the proposed control scheme.

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Time-Varying Input Delays Using the Quadratic Functions

In this paper, for the stochastic nonlinear systems the adaptive fuzzy tracking controllers are constructed by using the fuzzy logic systems (FLS) and the classical quadratic functions. Compared with the existing results for adaptive fuzzy control, the stochastic nonlinear systems investigated in this paper are much more complex since the systems not only have distributed state time-varying delays in the noise jamming intensity terms but also have the time-varying delays in the input signals. During the controller design procedure, through appropriate assumptions and a state transformation the system with time-varying input delay can be easily transformed into a system without input delay. The other main advantage is that quadratic functions are used as Lyapunov functions to analyze the stability of systems, other than the fourth moment approach proposed by H. Deng and M. Krstic, and the hyperbolic tangent functions are introduced to deal with the Hessian terms. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error can converge to a small residual set around the origin in the mean square sense.

Adaptive Online Constructive Fuzzy Tracking Control for Unmanned Surface Vessel With Unknown Time-Varying Uncertainties

In this paper, a practical adaptive online constructive fuzzy control (AOCFC) algorithm is proposed to handle the trajectory tracking problem for the unmanned surface vessel under unknown time-varying uncertainties. In the AOCFC algorithm, the dynamic surface control technology combined with “logical virtual vessel” can program the rational reference route while avoiding the problem of “complexity explosion.” Besides, the proposed online constructive fuzzy approximator (OCFA) is devised to deal with the unknown time-varying uncertainties. The OCFA has two advantages: first, it can estimate the uncertainties without exact information of the dynamic model and external environmental disturbances; second, it employs decoupled distance measure and structure learning mechanism to dynamically and parsimoniously self-constructing fuzzy rules. At the same time, we have proved that the tracking errors of the closed-loop control system are uniformly ultimately bounded. Finally, the simulation result and comprehensive comparison demonstrate the proposed controller’s performance and effectiveness.

(1606-3017) ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF PERTURBED NONLINEARLY PARAMETERIZED SYSTEMS USING MINIMAL LEARNING PARAMETERS ALGORITHM

In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learning parameters (MLP) algorithm and the adaptive backstepping dynamic surface control (DSC) technique, a new adaptive fuzzy backstepping control scheme is developed. The main advantages of our approach include: (i) unlike the existing results which deal with the nonlinearlyparameterized functions by using the separation principle, the nonlinearly parameterized functions are lumped into the continuous functions which can be approximated by using the FLS, (ii) only one parameter needs to be adjusted online in controller design procedure, which reduces the online computation burden greatly, and our development is able to eliminate the problem of ''explosion of complexity inherent in the existing backstepping-based methods. It is proven thatthe proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound. Finally, two examples are used to show the effectiveness of the proposed approach.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems

This paper concerns an adaptive fuzzy tracking control problem for a class of switched uncertain nonlinear systems in strict-feedback form via the modified backstepping technique. The unknown nonlinear functions are approximated by the generalized fuzzy hyperbolic model (GFHM). It is shown that if the designed parameters in the controller and adaptive laws are appropriately selected, then all closed-loop signals are bounded and the stability of the system can be kept under average dwell time methods. In the end, simulation studies are presented to illustrate the effectiveness of the proposed method.

Nussbaum-Based Adaptive Fuzzy Tracking Control of Unmanned Surface Vehicles with Fully Unknown Dynamics and Complex Input Nonlinearities

In this paper, subject to both fully unknown dynamics and complex input nonlinearities including unknown control directions and dead zones, a Nussbaum-based adaptive fuzzy trajectory tracking control scheme of an unmanned surface vehicle is addressed by combining adaptive fuzzy backstepping technique with Nussbaum approach. The dead-zone input nonlinearity is firstly divided into input-dependent functions and time-varying input coefficients which can be treated as system uncertainties. Together with disturbances, unknown dynamics and uncertainties, the lumped nonlinearity is online approximated by employing an adaptive fuzzy approximator. Within the backstepping framework, a Nussbaum gain function is further designed to tackle unknown control directions, and thereby devising an adaptive fuzzy trajectory tracking control scheme which is constructed recursively to deal with complex input nonlinearities and fully unknown dynamics. Theoretical analysis reveals that all signals of the closed-loop tracking system are bounded and tracking errors can converge to an arbitrarily small neighborhood of zero. Simulation studies demonstrate the effectiveness and superiority of the proposed approach.

Finite-Time Adaptive Fuzzy Tracking Control for a Class of Nonlinear Systems with Unknown Hysteresis

An adaptive finite-time tracking control issue for a class of nonlinear systems with unknown hysteresis is considered in this manuscript. We construct a fuzzy adaptive controller on foundation of a backstepping method. We prove that all the signals in the system are semi-global uniformly finite-time bounded under the designed controller, even though unknown hysteresis in the actuator is considered . At last, the validity of the proposed control scheme is demonstrated by an example.