Control For Stochastic Nonlinear Systems Research Articles

Vent-Triggered Adaptive Stabilization Control of Stochastic Nonlinear Systems with Unmodeled Dynamics

Vent-Triggered Adaptive Stabilization Control of Stochastic Nonlinear Systems with Unmodeled Dynamics

Event-triggered-based fixed-time adaptive prescribed tracking control for stochastic nonlinear systems with actuator faults

In this paper, the event-triggered fixed-time control scheme is developed for a class of stochastic nonlinear systems with prescribed boundary constraints and actuator faults. For the controlled systems with unknown nonlinear functions, the neural networks are employed to reestablish the system model. Based on the event-triggered control technology, an original adaptive fixed-time control strategy with prescribed performance and the fault state is proposed by using the backstepping technique. Under the developed adaptive controller, the tracking error satisfies the predefined boundary functions and all the closed-loop system signals are bounded in probability in a fixed time, and the convergence time is irrelevant to the initial states of the system. The practicability of the designed controller is illustrated by a simulation example.

Adaptive tracking control for stochastic nonlinear systems with unknown virtual control coefficients

AbstractThis article studies the adaptive tracking control problem for stochastic nonlinear systems with unknown virtual control coefficients (UVCCs) being functions of system states. Without using the Nussbaum gain technique (NGT), a new method is introduced to deal with UVCCs. The novel adaptive laws are constructed to compensate parameter uncertainties and UVCCs. Using backstepping method, an adaptive controller is designed, which can ensure that the boundedness in probability of all the signals in the closed‐loop system. In the end, a numerical simulation example and a practical example are given to validate the effectiveness and superiority of the proposed controller.

Finite‐time adaptive fuzzy command filtering control for stochastic nonlinear systems with input quantization

SummaryThis article focuses on the finite‐time adaptive fuzzy control problem based on command filtering for stochastic nonlinear systems subject to input quantization. Fuzzy logic systems are employed to estimate unknown nonlinearities. In the control design, the hysteretic quantized input is decomposed into two bounded nonlinear functions, which solves the chattering problem. Meanwhile, an adaptive fuzzy controller is presented by the combination of command filter technique and backstepping control, which eliminates the computational complexity existing in traditional backstepping design. Under the proposed adaptive mechanism, all the closed‐loop signals remain bounded while the desired system performance can be realized within finite time. The main significance of this work is that (1) the filtering error can be solved on the basis of the designed compensating signals; (2) the requirement of adaptive parameters is decreased to only one, which simplifies the controller design process and may improve the control performance. Two simulation examples are used to validity of the developed scheme.

Prescribed-Time Control of Stochastic Nonlinear Systems with Reduced Control Effort

Prescribed-Time Control of Stochastic Nonlinear Systems with Reduced Control Effort

Performance‐based optimal control for stochastic nonlinear systems with unknown dead‐zone

AbstractThis article studies the problem of performance‐guaranteed adaptive fuzzy optimal compensator control for stochastic affine nonlinear systems with dead‐zone and unknown nonlinear dynamics. First, by using the error transformation techniques, the original system is transformed into an equivalent unconstraint system. Then, a feedforward fuzzy compensator is constructed to offset the influence raised by unknown dead‐zone. By designing identifier‐critic‐actor construction reinforcement learning, an adaptive fuzzy optimal performance constraint compensate control algorithm is presented. The developed control scheme can ensure that all signals of the controlled system are uniformly ultimately bounded, and the output can track the reference signal with a prescribed accuracy. Finally, simulation results are given to illustrate the effectiveness of the developed control algorithm and theorem.

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

In this paper, the problem of adaptive finite-time multi-dimensional Taylor network (MTN) control for a class of stochastic nonlinear systems is investigated. By combining the MTN-based approximate method and adaptive backstepping technique, a novel adaptive finite-time MTN control scheme is proposed. In this scheme, the MTNs are used to approximate the unknown nonlinear functions of the systems. The finite-time Lyapunov stability theory is utilized to prove the stability of the close-loop system. The proposed scheme can ensure that all signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood of the origin in a finite time. Three simulation examples are presented to show the effectiveness of the control scheme. It should be pointed that the adaptive MTN controller proposed in this paper has the advantages of fast computational speed and good real-time performance thanks to the simple structure of the MTN.

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Nonstrict-Feedback and Dead Zone

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Nonstrict-Feedback and Dead Zone

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

In this article, the problem of adaptive multi-dimensional Taylor network control for strict-feedback nonlinear stochastic systems with time-delay is investigated. To overcome the control degradation resulting from the delay terms, the appropriate integral-type Lyapunov–Krasovskii functions are introduced. A novel adaptive multi-dimensional Taylor network control scheme is provided via backstepping technique. The proposed adaptive multi-dimensional Taylor network controller can ensure that all signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood of the origin. Three simulation examples are given to demonstrate the effectiveness of the proposed control scheme.

Fuzzy Observer-Based H 2 / H ∞ Output-Feedback Control for Stochastic Nonlinear Systems with Multiplicative Noise

A design method is established for the mixed H 2 / H ∞ output-feedback control of stochastic nonlinear systems with multiplicative noise. Firstly, using T-S fuzzy rules, we obtain a fuzzy model to approximate the original nonlinear system. Then, by Schur’s complement, the suboptimal H 2 / H ∞ output-feedback control design is transformed into a two-step convex optimization problem. A numerical example is given to show the effectiveness of the proposed method.

Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

This article studies a tracking control problem for a class of stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Firstly, the Gauss Error Function is introduced to solve the difficulty arising from the saturation nonlinearity. Meanwhile, to overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter with a compensation mechanism is developed. Combining the neural networks' approximation ability and the backstepping technique, an adaptive neural finite-time control strategy is proposed for the considered system by constructing the time-varying barrier Lyapunov function. Under the proposed control strategy, it is guaranteed that all signals are bounded in a sense of mean square, the output of the system can track the reference signal within a finite time and all states will not violate the constraints. Furthermore, the stability of the closed-loop system is analysed based on the stochastic finite-time stability theory. Finally, a simulation example verifies the effectiveness of the proposed control strategy.

Fuzzy adaptive finite-time output feedback control of stochastic nonlinear systems

An adaptive finite-time approach to the feedback control of stochastic nonlinear systems is presented. The fuzzy logic system (FLS) and a state observer are used to estimate the uncertain function and unmeasured state of the controlled system, respectively. A dynamic surface control (DSC) scheme is employed to deal with the “computational explosion” problem, which is inherent in traditional backstepping methods since the repetitive calculation of the derivatives of virtual control signals is avoided. A new output feedback controller is developed to guarantee that all the signals of the controlled system are bounded within a finite time range and the tracking deviation can converge to an arbitrarily small residual set within finite time. Simulations confirm the analytical and theoretical results of the presented algorithm.

Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming

For nonlinear Itô-type stochastic systems, the problem of event-triggered optimal control (ETOC) is studied in this paper, and the adaptive dynamic programming (ADP) approach is explored to implement it. The value function of the Hamilton–Jacobi–Bellman(HJB) equation is approximated by applying critical neural network (CNN). Moreover, a new event-triggering scheme is proposed, which can be used to design ETOC directly via the solution of HJB equation. By utilizing the Lyapunov direct method, it can be proved that the ETOC based on ADP approach can ensure that the CNN weight errors and states of system are semi-globally uniformly ultimately bounded in probability. Furthermore, an upper bound is given on predetermined cost function. Specifically, there has been no published literature on the ETOC for nonlinear Itô-type stochastic systems via the ADP method. This work is the first attempt to fill the gap in this subject. Finally, the effectiveness of the proposed method is illustrated through two numerical examples.

Approximately Optimal Control of Nonlinear Dynamic Stochastic Problems with Learning: The OPTCON Algorithm

OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to economic models. It delivers approximate numerical solutions to optimum control (dynamic optimization) problems with a quadratic objective function for nonlinear economic models with additive and multiplicative (parameter) uncertainties. The algorithm was first programmed in C# and then in MATLAB. It allows for deterministic and stochastic control, the latter with open loop (OPTCON1), passive learning (open-loop feedback, OPTCON2), and active learning (closed-loop, dual, or adaptive control, OPTCON3) information patterns. The mathematical aspects of the algorithm with open-loop feedback and closed-loop information patterns are presented in more detail in this paper.

Approximate Controllability for Time-dependent Impulsive Neutral Stochastic Partial Differential Equations with Fractional Brownian Motion and Poisson Jumps

In this paper, we investigate the approximate controllability for time-dependent impulsive neutral stochastic partial differential equations with fractional Brownian motion and Poisson jumps in Hilbert space. The results are obtained by using semigroup theory, stochastic analysis, and fixed point approach, we derive a new set of sufficient conditions for the approximate controllability of nonlinear stochastic system under the assumption that the corresponding linear system is approximately controllable. Finally, an example is provided to illustrate our results.

An Investigation into Accuracy, Computation, and Robustness of Stochastic Linearization of Systems with Saturation Nonlinearities

Quasilinear Control (QLC) is a theory with a set of tools that can be used for the analysis and design of stochastic feedback control systems with nonlinear actuators and sensors. It is based on Stochastic Linearization (SL), a method of linearization that approximates a nonlinearity with an equivalent gain and a bias using statistical properties of its input. While the QLC literature has studied many problems in controls (e.g., performance analysis and controller design), important issues such as accuracy, robustness, and computation of SL have not been fully investigated. This paper fills this gap, with a special focus on feedback systems with saturating actuators, which are commonly encountered in feedback control systems and the QLC literature. Specifically, the accuracy of SL in approximating the saturated response is investigated. A mathematical relationship is established between the saturation authority and the worst-case open-loop accuracy, and the implications on closed-loop accuracy are described. To study the robustness of SL in practical situations, the sensitivities of the equivalent SL gain and bias to system parameters are investigated. Finally, several root-finding algorithms are compared in terms of their computational speed in solving the SL equations, and a novel change of coordinates is presented to improve the performance. The results provide valuable insights into and methods for addressing the computational aspects of SL that are critical for proper design and analysis of nonlinear stochastic feedback control systems.

Adaptive finite-time prescribed performance control for stochastic nonlinear systems with unknown virtual control coefficients

This paper is devoted to the adaptive finite-time prescribed performance control (FTPPC) for stochastic nonlinear systems with unknown virtual control coefficients (UVCCs), which are functions of system states. To eliminate the condition that the initial value of the performance function (PF) is bigger than the initial tracking error, a novel smooth shifting function, for the first time, is defined and embedded in FTPPC for the tracking error. New control laws are firstly proposed and employed to deal with UVCCs in the controller design, which are different from the Nussbaum gain technology in the existing papers. An adaptive FTPPC strategy is designed so that all of the signals in the closed-loop system are bounded in probability and the tracking error is restrained in a fixed bound after a preset finite time,even that the PF is smaller than the tracking error at the initial time instant.

Adaptive fuzzy finite‐time quantized control for stochastic nonlinear systems with full state constraints

SummaryThis article studies the adaptive fuzzy finite‐time quantized control problem of stochastic nonlinear nonstrict‐feedback systems with full state constraints. During the control design process, fuzzy logic systems are used to identify the unknown nonlinear functions, integral barrier Lyapunov functions are employed to solve the state constrained problem. In the frame of backstepping design, an adaptive fuzzy finite‐time quantized control scheme is developed. Based on the stochastic finite‐time Lyapunov stability theory, it can be guaranteed that the closed‐loop system is semiglobal finite‐time stable in probability, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to testify the effectiveness of the developed control scheme.

Adaptive fuzzy finite-time control of stochastic nonlinear systems with actuator faults

This paper considers the finite-time control problem for a class of nonlinear stochastic systems with actuator faults/failure. A fast convergence feedback control algorithm based on backstepping finite-time command filtering is proposed. Under the framework of adaptive feedback, the fuzzy logic system is used to deal with the uncertainties of the system. Taking into account the actuator faults of both loss of effectiveness and lock-in-place, an adaptive fuzzy controller is developed. Since there is no need to calculate the derivative of the virtual control signal, the presented scheme overcomes the “explosion of complexity” problem inherent in conventional methods. A compensation mechanism is also introduced to compensate for errors caused by the filter. The proposed method ensures not only that all signals of the closed-loop system are finite-time bounded, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results.

Fault diagnosis and fault-tolerant control for T-S fuzzy time-varying delay stochastic distribution systems with actuator and sensor faults

ABSTRACT The problem of fault diagnosis (FD) and fault-tolerant control (FTC) for a class of nonlinear time-varying delay stochastic distribution control (SDC) systems with actuator and sensor faults is investigated in this paper. The Takagi–Sugeno (T-S) model is used to approximate the nonlinear dynamics. The B-spline function is used to approximate the output probability density function (PDF) of the system. According to the output equivalence principle and Laplace transformation, an augmented state vector is designed to solve the time-varying delay problem. An augmented state adaptive diagnosis observer is proposed to estimate the system state, actuator and sensor faults simultaneously. A new fault-tolerant control algorithm is designed based on the PI control strategy to compensate sensor fault and actuator faults simultaneously. The sensor fault is compensated through the information obtained by the observer. The PI controller can compensate for the influence of the actuator fault, and the output PDF of system can still track the desired PDF when fault occurs. Simulation results are provided to show the effectiveness of the proposed method.