Control Of Uncertain Nonlinear Systems Research Articles

Performance recovery-based adaptive saturated fault-tolerant control of uncertain nonlinear systems with actuator switching and periodic disturbances

This paper studies an adaptive saturated fault-tolerant control problem for uncertain nonlinear systems with periodic disturbances. In contrast to previous fault-tolerant control methods, a case of deferred actuator switching is taken into account, which stands for a more common situation. By designing monitoring functions and reconfigurable controllers, tracking errors can be recovered to prescribed performance within a specified time after a fault is detected. Then, a low-computation control strategy is employed to address the problem of complexity explosion, and neural networks and Fourier series expansion are combined to model the uncertain nonlinear dynamics subject to time-varying periodic disturbances. The proposed scheme has a simpler structure and ensures the boundedness of all signals within the closed-loop system. Finally, to verify the effectiveness of the design scheme, two simulation examples are provided.

Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems

AbstractIn this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model.

Observer-Based Fuzzy Adaptive Predefined Time Control for Uncertain Nonlinear Systems With Full-State Error Constraints

Observer-Based Fuzzy Adaptive Predefined Time Control for Uncertain Nonlinear Systems With Full-State Error Constraints

Adaptive Neural Fixed-Time Control for Uncertain Nonlinear Systems

The article addresses the adaptive fixed-time system control problem subject to unknown nonlinear functions. By employing backstepping technique, fixed-time control, and neural networks (NNs), an adaptive fixed-time control approach is developed. NNs are utilized to package the unknown nonlinearities in the controlled system. The hyperbolic tangent functions are firstly utilized to solve the singular problem, which may exist in the derivation of virtual controller. Meanwhile, the jitter phenomenon is avoided. The developed controller design scheme not only assures the signals’ boundedness within fixed-time interval, but also makes the tracking error converge. Finally, on the aid of the simulation results, the effective of the designed control technique is shown.

A Multifilters Approach to Adaptive Event-Triggered Control of Uncertain Nonlinear Systems With Global Output Constraint.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

Adaptive Output Feedback Control for Uncertain Nonlinear Systems with Unknown Modeling Errors

AbstractIt is well known that unknown modeling errors cannot be avoided in practice. Such unmeasured uncertainties are usually denoted as unknown nonlinear functions that exist in every channel of the system equation. This paper aims to develop an output feedback adaptive control scheme by constructing state estimation filters to address such unknown modeling errors. In the controller design, these uncertainties caused by modeling errors will be accumulated to the last step for compensation. Unknown parameters existing in the upper bound functions of unknown nonlinear functions and system parameters are estimated synchronously based on tuning function approaches. It is shown that the designed output feedback controller can ensure the stability and tracking performance of the closed‐loop system, and the transient performance in terms of a truncated norm is also established.

Reinforcement learning-based optimal control of uncertain nonlinear systems

This paper considers the optimal control of a second-order nonlinear system with unknown dynamics. A new reinforcement learning based approach is proposed with the aid of direct adaptive control. By the new approach, actor-critic reinforcement learning algorithms are proposed with neural network approximation. Simulation results are presented to show the effectiveness of the proposed algorithms.

Adaptive prescribed‐time tracking control for uncertain nonlinear systems with full state constraints

SummaryThis paper investigates the adaptive tracking control in a prescribed‐time for a class of uncertain nonlinear systems with a full state constraint. Firstly, in order to restrict the state constraints, the barrier Lyapunov function is employed and parameter uncertainty is handled based on linear‐in‐the‐parameters (LIP). A prescribed‐time adaptive controller is then designed by backstepping to ensure that the closed‐loop system can reach zero in a prescribed finite time under any given initial conditions. The prescribed‐time is independent of the design of the parameters. Finally, two simulation examples verify the effectiveness and usability of the proposed method.

Adaptive fuzzy anti-saturation tracking control for uncertain nonlinear systems with external disturbance

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller.

Adaptive fixed-time tracking control for uncertain nonlinear systems with unknown control coefficients and prescribed performance

This paper delves into the problem of fixed-time neural network adaptive prescribed performance control for a category of nonstrict-feedback systems with time-varying unknown control coefficients (UCCs). Firstly, two key technical lemmas are proposed. One is to put forward a novel fixed-time stability lemma with a more precise upper-bound estimate of the settling time. The other is to present a new lemma based on a category of type-B Nussbaum functions (TBNFs), which can effectively address the time-varying UCCs in the systems. Secondly, neural networks are employed to approach the uncertain nonlinear terms, and a fixed-time performance function and a nonlinear shifting function are constructed to eliminate the restriction of tracking error in terms of initial condition. Then, to overcome the singularity problem, the switched virtual controllers are designed with the help of the novel fixed-time stability lemma and dynamic surface control technique. It turns out that the tracking error converges to a predefined asymmetric constraint region within a fixed time and the closed-loop system is practically fixed-time stable. Finally, a numerical example and a mass-spring-damper system are provided to verify the effectiveness of the presented design method.

Operator-based adaptive robust control for uncertain nonlinear systems by combining coprime factorization and fuzzy control method1

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper.

Event-triggered finite-time neural control for uncertain nonlinear systems with unknown disturbances and its application in SVC

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an [Formula: see text]-dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach.

Adaptive fuzzy finite‐time prescribed performance control for uncertain nonlinear systems with actuator saturation and unmodeled dynamics

AbstractIn this article, the tracking control problem is addressed for uncertain nonlinear systems with actuator saturation and unmeasurable unmodeled dynamics. Being different from the existing performance function control results, to constraint the output tracking error within a predefined boundary in finite time, an improved performance function, that is, finite‐time performance function, is introduced. The design difficulties of asymmetric input saturation and unmodeled dynamics are solved simultaneously for the first time by applying a smooth non‐affine function and a measurable dynamic signal. Furthermore, an adaptive fuzzy control scheme is established via command filtering, which not only guarantees the semi‐global boundedness of all the controlled system signals but also makes the output tracking error that can be restrained by the described performance bound within a finite‐time interval. At last, an effective example is supplied to validate the availability of the presented theoretical finding.

Self-Adjustable Performance-Based Adaptive Tracking Control of Uncertain Nonlinear Systems

Self-Adjustable Performance-Based Adaptive Tracking Control of Uncertain Nonlinear Systems

Fixed-Time Neuroadaptive Backstepping Tracking Control for Uncertain Nonlinear Systems With Predictor Based Learning

Fixed-Time Neuroadaptive Backstepping Tracking Control for Uncertain Nonlinear Systems With Predictor Based Learning

Event-Triggered Robust Constrained Control of Uncertain Nonlinear Systems With Input Saturation Based on Self-Learning Disturbance Observer

Event-Triggered Robust Constrained Control of Uncertain Nonlinear Systems With Input Saturation Based on Self-Learning Disturbance Observer

Adaptive Control for Uncertain Nonlinear Systems Against DoS Attacks Using Quantized Output Only and Application to a Single-Link Robot

Adaptive Control for Uncertain Nonlinear Systems Against DoS Attacks Using Quantized Output Only and Application to a Single-Link Robot

Command Filter-Based Adaptive Optimal Control of Uncertain Nonlinear Systems With Quantized Input

Command Filter-Based Adaptive Optimal Control of Uncertain Nonlinear Systems With Quantized Input

Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function

Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function

Improved Dynamic Surface Control for Uncertain Nonlinear Systems With Application to Fighter Jet System

Improved Dynamic Surface Control for Uncertain Nonlinear Systems With Application to Fighter Jet System