Search

Predefined performance control (PPC) has been applied to many engineering fields, such as unmanned aerial vehicle, emergency braking, and so on. Note that above engineering applications have the characteristic of hysteresis, which results in degraded performance for most existing PPC schemes. This paper investigates the problem of PPC for stochastic nonlinear systems with hysteresis input. Different from existing PPC schemes, an improved finite-time performance function is introduced to select the desired convergence time flexibly. By incorporating an intermediate parameter into the controller, a novel control design framework is presented to adaptively compensate hysteresis input. Besides, the unknown nonlinear dynamics of the systems are approximated by fuzzy logic systems, and the computational complexity problem is reduced by using dynamic surface control technique. Then, the proposed control scheme ensures that tracking error converges to the specific region, which can be set arbitrarily within the physical limitations. Finally, two examples are provided to demonstrate the validity of proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the issue of finite‐time prescribed performance control (PPC) is addressed for stochastic nonlinear systems with the effect of dead‐zone. Fuzzy logic systems are utilized for identifying unknown nonlinear characteristics. A model consisting of linear and disturbance‐like terms is constructed to approximate the nonlinearity of dead‐zone. Meanwhile, a state observer is employed to estimate unmeasured states. To improve the convergence rate, a finite‐time performance function is introduced, which transforms the tracking error of original system to error of converted system, thus prescribing the transient and steady‐state performance of the system. Eventually, based on backstepping technique, a finite‐time PPC strategy is proposed, which ensures the boundedness of all signals in closed‐loop system. Simultaneously, the tracking error converges to a prescribed region in finite‐time. Finally, two simulation examples are provided to verify the effectiveness of presented control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive fuzzy fixed-time control strategy is proposed for stochastic nonstrict nonlinear systems with Bouc–Wen type of hysteresis input and unmeasured state variables. The Bouc–Wen model is recognized as one of the most intractable mechanical hysteresis models. The established state observer is used to assess unmeasurable states, and the fuzzy logic systems are utilized to approximate the uncertain nonlinear functions in the system. Based on the state observer and backstepping method, a fixed-time controller is recursively designed to ensure that all closed-loop signals of the system are bounded and the tracking error converges to a small neighborhood of the origin within a fixed-time. Finally, the effectiveness of the control strategy is verified by two simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, the finite‐time adaptive fuzzy tracking control issue for stochastic nonlinear systems with time‐delay via output‐feedback is studied. Firstly, a state observer and fuzzy logic systems with infinite approximation capability are utilized to estimate the unmeasured states and identify unknown nonlinear characteristics, respectively. Then, a finite‐time adaptive fuzzy tracking controller is devised by the backstepping technology. It is shown that the presented control scheme can ensure the finite‐time square stability of the stochastic nonlinear systems with time‐delay, that is, the output tracking error converges to a sufficiently small neighborhood around the origin in finite‐time, and all signals of the closed‐loop system are bounded. Finally, the effectiveness of the presented control strategy is illustrated via two simulation examples. [ABSTRACT FROM AUTHOR]

This paper studies fixed-time tracking problems for stochastic nonlinear systems in strict-feedback form. Different from previous results, the practical fixed-time bounded theorem for stochastic nonlinear systems is given. The unknown functions of the stochastic nonlinear systems are approximated by the Fuzzy logic system (FLS) which has a universal approximation. Then by using a back-stepping method, a novel adaptive fuzzy fixed-time controller is designed for stochastic nonlinear systems based on the fixed-time bounded theorem. The states of the stochastic nonlinear systems are guaranteed to converge into an equilibrium point contained compact set semi-globally in fixed-time by the designed controller. Finally, a numerical example and a vehicle tracking model example are provided to illustrate the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the tracking problem for stochastic nonlinear system with actuator fault and full states constraints. The adaptive laws for nonlinear uncertainties and the estimation of lose effectiveness are combined during the process of controller design. Fuzzy logic systems is utilized to approximate the unknown nonlinear functions. Quartic barrier Lyapunov function is introduced to guarantee the constraints for stochastic system are not violate. Fuzzy observer is designed to handle with the unmeasured states. Lyapunov finite-time stability theorem is used to guarantee the finite-time convergence performance. Then, output feedback-based adaptive fuzzy finite-time fault-tolerant controller is designed. Even the system subject to actuator faults, all the state variables of the nonlinear stochastic system are semi-global ultimately bounded in probability. Compare with the existing result, the control strategy proposed in this paper is more complex and have more potential applications. Finally, two examples are given to verify the validity of the designed control strategy. [ABSTRACT FROM AUTHOR]

In this article, the problem of adaptive fuzzy control for stochastic high-order nonlinear systems with full-state constraints of the strict-feedback structure was investigated. The unknown nonlinear functions are approximated by using fuzzy logic systems (FLSs) at each step. By introducing the barrier Lyapunov functional candidate, a novel adaptive fuzzy backstepping control strategy is proposed to solve the control problem of stochastic nonlinear systems with full-state constraints. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

During the actual flight of unmanned helicopters, there are widespread output constraints, system uncertainties and external disturbances, which bring significant threats to their safe flight. To keep the output constraints from violating predefined boundaries, the barrier Lyapunov function (BLF) is utilized in this paper. The system uncertainties are estimated by the fuzzy logic system (FLS). The compound disturbances composed of the external disturbances and the approximation errors caused by FLS are approximated by the fixed-time disturbance observers (FTDOs). According to the analysis of FTDOs and fixed-time stability theory, the observer errors and state errors converge to a region of the origin in fixed-time, and the appropriate parameters of FTDOs and controller are designed to ensure that the disturbance convergence time is faster than the one of the proposed controller, which further improves the stability of the system. Moreover, the convergence time of the FTDOs FTDOs and the proposed approach only depends on the design parameters under any initial conditions with constrained outputs. The performance of the proposed control approach is verified through the simulation results. [ABSTRACT FROM AUTHOR]