Observer-based Adaptive Fuzzy Tracking Control Research Articles

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Fixed-time observer-based adaptive fuzzy tracking control for Mecanum-wheel mobile robots with guaranteed transient performance

Fixed-time observer-based adaptive fuzzy tracking control for Mecanum-wheel mobile robots with guaranteed transient performance

Reduced-Order Observer-Based Adaptive Fuzzy Tracking Control Scheme of Stochastic Switched Nonlinear Systems

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples.

Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The fuzzy logic system is used to design the approximator, which deals with uncertain and continuous functions in the process of backstepping design. The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint, but also mixes the states and errors to directly constrain the state, reducing the conservativeness of the constraint satisfaction condition. Considering that the states in most nonlinear systems are immeasurable, a fuzzy adaptive states observer is constructed to estimate the unknown states. Combined with adaptive backstepping technique, an adaptive fuzzy output feedback control method is proposed. The proposed control method ensures that all signals in the closed-loop system are bounded, and that the tracking error converges to a bounded tight set without violating the full state constraint. The simulation results prove the effectiveness of the proposed control scheme.

Observer-Based Adaptive Fuzzy Tracking Control for a Class of Strict-Feedback Systems with Event-Triggered Strategy and Tan-type Barrier Lyapunov Function

In this paper, an observer-based adaptive fuzzy tracking control is considered for a class of strict-feedback nonlinear systems. The nonlinear functions and unmeasured states in the system are estimated by means of fuzzy logic systems and fuzzy state observers, respectively. In addition, the tan-type barrier Lyapunov specifies that the tracking error is limited to a given area. An improved event-triggered technique is introduced in the controller design to save the network resources and avoid the Zeno-behavior. It is proven that the proposed controller can ensure that all the signals of the closed-loop system are uniformly and ultimately bounded. Finally, the simulation results verify the feasibility of the design scheme.

Observer-Based Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Multi-Agent Systems with Dead-Zone Input

This paper studies observer-based adaptive fuzzy leader-following tracking control for high-order stochastic nonlinear multi-agent systems with unknown dead-zone input. First, in order to deal with unmeasurable state variables, a reduced-order observer is established for each follower, which could make the computational burden of the systems reduced. Second, the fuzzy logic systems are introduced to approximate the stochastic disturbances and the unknown functions. Then, an adaptive fuzzy tracking controller is designed for high-order multi-agent systems with unknown dead-zone input by using the backstepping approach. Furthermore, it can be proved that all the closed-loop signals are bounded in probability, and the consensus error of the stochastic multi-agent systems can converge to a small region of origin under the Lyapunov stability theory. Finally, a simulation example is given to show the effectiveness of the designed algorithm.

Observer-based adaptive fuzzy tracking control of MIMO switched nonlinear systems preceded by unknown backlash-like hysteresis

This paper considers the problem of adaptive fuzzy output feedback tracking control for a class of multi-input and multi-output (MIMO) switched nonlinear systems with hysteresis nonlinearities, where the hysteresis nonlinearities are described by a set of differential equations with unknown parameters. Fuzzy logic systems, as specialized function approximators, are employed to approximate the unknown nonlinear functions of the system under study. A MIMO switched fuzzy observer is constructed to tackle the problem of unmeasured states, and the dynamic surface control technique is introduced to avoid the appearance of repeated differentiations existing in most traditional backstepping designs. Based on the multiple Lyapunov function approach, an adaptive fuzzy output feedback controller is developed. The proposed control scheme can guarantee that all the signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) under a class of switching signals with average dwell time, and the outputs to track given reference signals exactly. Simulation results demonstrate the validity of the control scheme.

Observer-based adaptive fuzzy tracking control for a class of MIMO nonlinear systems with unknown dead zones and time-varying delays

ABSTRACTThe adaptive tracking control strategy is investigated for a class of multi-input and multi-output pure-feedback nonlinear delayed systems with unknown dead-zone inputs. This problem is challenging due to the existence of unknown dead zones, time-varying delays and unavoidable state variables. By constructing fuzzy approximators and state observers, the difficulties from unknown nonlinearities and unavailable state variables are surmounted, respectively. Lyapunov–Krasovskii functions are introduced to deal with the time-varying delays. The adaptive controllers are designed by a backstepping method and adaptive technique so that the closed-loop systems remain stable and the target signals can be tracked within a small error as well. At last, two examples are provided to show the effectiveness of the proposed scheme.

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.

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.

Observer-based adaptive fuzzy tracking control of nonlinear systems with time delay and input saturation

This paper studies the problem of adaptive output tracking control for a class of nonlinear systems subject to unknown time delay and input saturation. To address the delay and the input saturation, some reasonable assumptions and an auxiliary system are introduced. The fuzzy logic approach is employed to approximate the unknown functions in the system. An observer is designed to estimate the unavailable system states. The dynamic surface control (DSC) technique is utilized to avoid the problem of “explosion of complexity”. By combining the backstepping and DSC methods, a bounded control input is designed to ensure that all signals of the closed-loop system are bounded and the tracking error can fluctuate around the origin within a small neighborhood. Finally, two examples are presented to demonstrate the effectiveness and potential of the proposed new design techniques.

Reduced-order observer-based adaptive fuzzy tracking control for chaotic permanent magnet synchronous motors

This paper studies an adaptive fuzzy control method combined with reduced-order observer technology for the position tracking control of chaotic permanent magnet synchronous motor (PMSM) drive system. Fuzzy logic systems (FLSs) are introduced to solve the problem of nonlinear and unknown functions appeared in the PMSM drive system, reduced-order observer is used to calculate its angle speed. Meanwhile, adaptive backstepping mechanism is applied for the design procedure of controllers. The control technique developed in this paper can ensure that the tracking error falls into a small neighborhood of origin. Compared with the existing results, the proposed algorithm can solve the explosion of complexity issue and it does not require measuring the speed signal of motors and the number of adaptive parameters has been reduced to only one. Simulation results show that the chaos of PMSM can be successfully suppressed by the proposed method and the system can track the reference signals very well.

Robust tracking observer-based adaptive fuzzy control design for uncertain nonlinear MIMO systems with time delayed states

This paper addresses the problem of designing robust observer-based adaptive fuzzy tracking control scheme for a class of MIMO nonlinear systems with plant uncertainties, time delayed uncertainties, and external disturbances. A fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions and an adaptive fuzzy observer is introduced for state estimations. The proposed control law is based on indirect adaptive fuzzy control and uses two on-line estimations. This allows for the simultaneous inclusion of identifying gains of the delayed state uncertainties and training of the weights of the fuzzy system by introducing estimated error vectors. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with online update laws. The adaptive fuzzy tracking control using Variable Structure (VS) control technique is derived based on Lyapunov criterion and the Riccati-inequality to resolve system uncertainties, time delayed uncertainties, and external disturbances. This is done in such a way that all states of the system are bounded and the H∞ tracking performance is achieved. Finally, a two-connected inverted pendulums on carts system (Liu et al., 2011) [29] is used for simulation purposes and some comparisons are given to illustrate the validity and effectiveness of the proposed method.

Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems

This paper addresses the adaptive fuzzy tracking control problem for a class of uncertain nonlinear MIMO systems with the external disturbances. The adaptive fuzzy controllers are designed under the constraint that only system output is available for measurement. Then, it is needed to design a state observer to estimate the unmeasured states. In the observer design procedure, two prominent advantages are that it does not require the sign of the control gain coefficient to be known and only two parameters need to be adjusted on-line for each subsystem. By using Lyapunov analysis method, it is proven that all the signals in the closed-loop system are guaranteed to be bounded and the system outputs track the reference signals to a bounded compact set. The feasibility of the proposed approach is validated by using two simulation examples.