Liwei An

Prescribed Performance Switched Adaptive Dynamic Surface Control of Switched Nonlinear Systems With Average Dwell Time

In this paper, the problem of adaptive fuzzy tracking control is investigated for a class of switched nonlinear systems. A new switched adaptive output feedback control method is presented where the changes of plant can be considered explicitly. Mode-dependent fuzzy logic systems are employed to approximate the switching nonlinear functions in system. To reduce the conservativeness caused by adoption of a common adaptive law for all subsystems, the switching optimal weight vectors are directly estimated via switched adaptive laws at each step of backstepping. However, the difficulties are how to ensure the estimation performance subject to persistent switchings, and how to design common virtual controls based on switching parameters. Further, based on the estimation of optimal weight vectors, the switched fuzzy state observer can be also applied to obtain the unmeasured states. By designing a novel Lyapunov function, the improved dynamic surface control incorporated by prescribed performance technique guarantees all the total state tracking errors, not partial ones, within predefined performance bounds. Simulation results are provided to demonstrate the effectiveness of the proposed 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.

Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks

Cyber-physical systems (CPSs) are naturally highly interconnected and complexly nonlinear. This paper investigates the problem of decentralized adaptive output feedback control for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear uncertain strict-feedback interconnected systems. When a DoS attack is active, all the state variables become unavailable and standard backstepping cannot be applied. To overcome this difficulty, a switching-type adaptive state estimator is constructed. Based on an improved average dwell time method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived in term of solving a set of linear matrix inequalities. The proposed controller guarantees that all closed-loop signals remain bounded, while the error signals converge to a small neighborhood of the origin. As an illustrative example, the proposed control scheme is applied to a power network system.

Adaptive exact tracking control for a class of uncertain nonlinear switched systems with arbitrary switchings

Abstract This paper investigates the exact tracking control problem for a class of uncertain nonlinear switched systems with arbitrary switchings. Two mode-dependent adaptive backstepping state feedback control schemes are presented, where unknown switching parameters are directly estimated via switched adaptive laws. To avoid the loss of adaptive information due to persistent switching of the plant, the adaptive parameters are reset at the switching instants by exploiting the previous estimation information, which is called the trajectory initialization method. By constructing a novel common Lyapunov function, it is proven that the designed controller ensures the exact tracking of the closed-loop system. To improve the transient performance, especially at switching instants, the prescribed performance technique is incorporated into the first scheme without destroying the asymptotic behavior. It is shown that all the state tracking errors remain within the predefined performance bounds. Finally, simulation results on a mass-spring-damper system are provided to demonstrate the effectiveness of the proposed approaches.

Adaptive Fault-Tolerant Control for Nonlinear Systems With Multiple Sensor Faults and Unknown Control Directions

This paper investigates the problem of adaptive fault-tolerant control for a class of nonlinear parametric strict-feedback systems with multiple unknown control directions. Multiple sensor faults are first considered such that all real state variables are unavailable. Then, a constructive design method for the problem is set up by exploiting a parameter separation and regrouping technique. To circumvent the main obstacle caused by the coupling effects of multiple unknown control directions and sensor faults, a region-dependent segmentation analysis method is proposed. It is proven that the closed-loop system is globally exponentially stable. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

LMI-based adaptive reliable H∞ static output feedback control against switched actuator failures

ABSTRACT This paper investigates the H∞ static output feedback (SOF) control problem for switched linear system under arbitrary switching, where the actuator failure models are considered to depend on switching signal. An active reliable control scheme is developed by combination of linear matrix inequality (LMI) method and adaptive mechanism. First, by exploiting variable substitution and Finslers lemma, new LMI conditions are given for designing the SOF controller. Compared to the existing results, the proposed design conditions are more relaxed and can be applied to a wider class of no-fault linear systems. Then a novel adaptive mechanism is established, where the inverses of switched failure scaling factors are estimated online to accommodate the effects of actuator failure on systems. Two main difficulties arise: first is how to design the switched adaptive laws to prevent the missing of estimating information due to switching; second is how to construct a common Lyapunov function based on a switched estimate error term. It is shown that the new method can give less conservative results than that for the traditional control design with fixed gain matrices. Finally, simulation results on the HiMAT aircraft are given to show the effectiveness of the proposed approaches.

Decentralized adaptive fuzzy control for nonlinear large-scale systems with random packet dropouts, sensor delays and nonlinearities

Abstract This paper investigates the adaptive fuzzy decentralized control problem for a class of uncertain nonlinear large-scale systems. Different from the conventional backstepping-based adaptive output feedback control technique, the output measurements are imperfect and considered to suffer random packet dropouts (RPDs), random sensor delays (RSDs) and random sensor nonlinearities (RSNs), which result typically from a network environment such as sensor networks. A novel sensor model is established for describing the random phenomena within a unified representation by introducing a multi-Markovian variable. Based on this sensor model, two main difficulties arise: first is that the system output cannot be used directly for controller design due to the randomly occurring phenomena; second is how to deal with the complex nonlinear stochastic terms with unknown interconnections, unknown time-varying delays and unknown sensor nonlinearities entangled together. With the help of new coordinate transformations and mean value theorem, by using appropriate Lyapunov–Krasovskii functionals, a new adaptive decentralized memoryless output feedback controller is designed. It is proved that the constructed controller ensures the boundedness in probability of all the closed-loop signals in presence of RPDs, RSDs and RSNs. The simulation results are presented to show the effectiveness of the proposed scheme.