Shuai Sui

Fuzzy Adaptive Output Feedback Control of MIMO Nonlinear Systems With Partial Tracking Errors Constrained

In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems

This paper investigates an adaptive fuzzy tracking control design problem for single-input and single-output uncertain nonstrict feedback nonlinear systems. For the cases of the states measurable and the states immeasurable, fuzzy logic systems are separately adopted to approximate the unknown nonlinear functions or model the uncertain nonlinear systems. In the unified framework of adaptive backstepping control design, both adaptive fuzzy state feedback and observer-based output feedback control design schemes are proposed. The stability of the closed-loop systems is proved by using Lyapunov function theory. The simulation examples are provided to confirm the effectiveness of the proposed control methods.

Adaptive Fuzzy Decentralized Output Stabilization for Stochastic Nonlinear Large-Scale Systems With Unknown Control Directions

In this paper, an adaptive decentralized fuzzy output feedback stabilization problem is investigated for a class of uncertain stochastic nonlinear large-scale systems. The addressed stochastic nonlinear systems contain unknown nonlinear functions, unknown control direction, and without the measurements of the states. Fuzzy logic systems are used to identify the unknown nonlinear functions, and a fuzzy state filter observer is designed to estimate the unmeasured states. To solve the problem of the unknown control direction in decentralized control design, Nussbaum-type functions are introduced and new property on Nussbaum-type function is proved. Based on the backstepping recursive design technique and the established Nussbaum function property, a new robust stabilization control approach is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability, and the observer errors and system output converge to a small neighborhood of the origin. A simulation example is provided to show the effectiveness of the proposed approach.

Adaptive Fuzzy Output Feedback Control for Switched Nonstrict-Feedback Nonlinear Systems With Input Nonlinearities

This paper studies adaptive fuzzy output feedback tracking control problem for nonstrict-feedback switched nonlinear systems. The switched systems under consideration contain unknown nonlinearities, unmeasured states, and unknown deadzones. Fuzzy logic systems are utilized to approximate the unknown nonlinearities, and a switched fuzzy state observer is designed, and thus, the immeasurable states are estimated via it. In the framework of observer-based output feedback control, and by using the certainty equivalence deadzone inverse, a novel adaptive fuzzy output feedback control design method with the parameters adaptation laws is developed. The stability of the closed-loop system and the convergence of the tracking error are proved based on Lyapunov function and the average dwell-time methods. Two simulation examples are provided to check the effectiveness of the proposed approach.

Observer-based fuzzy adaptive prescribed performance tracking control for nonlinear stochastic systems with input saturation

In this paper, the problem of prescribed performance adaptive fuzzy output feedback control is investigated for a class of single-input and single-output nonlinear stochastic systems with input saturation and unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, the input saturation is approximated by a smooth function, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. The simulation example and the comparative results are provided to show the effectiveness of the proposed control approach.

Adaptive fuzzy backstepping output feedback tracking control of MIMO stochastic pure-feedback nonlinear systems with input saturation

Abstract In this paper, the adaptive fuzzy backstepping output feedback tracking control problem is considered for a class of uncertain stochastic multi-input and multi-output (MIMO) nonlinear systems in pure-feedback form. The stochastic MIMO nonlinear systems under study have unknown nonlinear functions, input saturation and immeasurable states. By using fuzzy logic systems to identify the uncertain nonlinear system, and a smooth function to approximate the input saturation, a fuzzy state observer is designed and the estimations of the immeasurable states are obtained. Based on the backstepping recursive design technique, an adaptive fuzzy output feedback tracking control approach is developed. It is shown that the proposed control approach guarantees that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in mean-square in the sense of probability, and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach.

Adaptive fuzzy decentralized tracking fault-tolerant control for stochastic nonlinear large-scale systems with unmodeled dynamics

Abstract This paper studies the adaptive fuzzy decentralized tracking fault-tolerant control design problem for a class of unknown stochastic nonlinear strict-feedback systems with actuator faults. The stochastic systems under study have the characterizations of unknown functions, unmodeled dynamics and without the direct measurements of state variables. Fuzzy logic systems are employed to identify the unknown stochastic nonlinear systems, and a fuzzy state observer is established for estimating the immeasurable states. The dynamic surface control (DSC) design approach based on the backstepping technique is presented to design adaptive decentralized tracking fault-tolerant controller. It is proved that proposed control approach guarantees that all the variables of the closed-loop system are bounded in probability, and also that the tracking errors converge to an adjustable neighborhood of the origin regardless of actuator faults and unmodeled dynamics. The simulation results are provided to illustrate the effectiveness of the proposed control approach.

Fuzzy adaptive quantized output feedback tracking control for switched nonlinear systems with input quantization

In this paper, the problem of adaptive fuzzy quantized output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict feedback form. The considered switched systems contain unknown nonlinearities, hysteretic quantized input and without requiring the system states being available for measurement. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a switched fuzzy state observer is designed to estimate the unmeasured states. The hysteretic quantized input is implemented to avoid the oscillation caused by logarithmic quantizer and decomposed into two bounded nonlinear functions. Based on the estimated states and using the backstepping design principle, an adaptive fuzzy quantized output feedback control scheme is developed. It is proved that whole adaptive fuzzy quantized control scheme can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.