Control Of Uncertain Nonlinear Systems Research Articles

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

A Simple Controller Design for a Class of Uncertain Nonlinear Control Systems Guaranteed to Achieve Arbitrary Exponential Convergence Rates

This paper first proposes the concepts of high-efficiency stabilizable systems and high-efficiency controller. Then, using the time-domain analysis methodology, it is proved that a class of uncertain nonlinear systems can indeed become high-efficiency stabilizable systems under certain conditions; at the same time, we also provide its corresponding high-efficiency controller. Finally, some numerical simulation results are offered to illustrate and demonstrate the correctness of the main theorem and the design process of such a high-efficiency controller

Adaptive Fuzzy Asymptotic Tracking Control of Uncertain Nonlinear Systems With Full State Constraints

Adaptive Fuzzy Asymptotic Tracking Control of Uncertain Nonlinear Systems With Full State Constraints

Improved Command-Filtering-Based Fixed-Time Fuzzy Adaptive Control for Uncertain Nonlinear Systems With Full State Constraints

Improved Command-Filtering-Based Fixed-Time Fuzzy Adaptive Control for Uncertain Nonlinear Systems With Full State Constraints

Logic-Based Fixed-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions.

The fixed-time control problem is investigated for a class of uncertain nonlinear systems subjected to multiple unknown control directions. The control coefficients of nonlinear systems under consideration are time varying and their signs are not required to be identical. To tackle this challenge, a switching mechanism along with a novel dynamic boundary function is proposed. Utilizing the devised dynamic boundary function, adaptive parameters are introduced into the controller to effectively handle system uncertainties. It is proved that the system output converges to a small neighborhood of the origin in fixed time and the boundedness of all system signals is maintained. Finally, two simulation examples are used to show the validity of the presented switching control strategy.

Adaptive prescribed performance tracking control for uncertain nonlinear systems with unknown backlash-like hysteresis

In this paper, an adaptive prescribed performance tracking control(PPTC) problem of uncertain nonlinear systems with unknown hysteresis input is investigated. For the purpose of implementing the PPTC, a performance function and an error conversion function are introduced. Moreover, a backlash-like hysteresis model is adopted to describe the hysteresis nonlinearity, which makes the controller design feasible. Additionally, by converting the backlash-like hysteresis model into a linear model with bounded error, the difficulties caused by hysteresis behavior on controller design are settled. Thus, integrating multi-dimensional Taylor network(MTN) approximation technique into adaptive backstepping method, an adaptive control scheme for uncertain nonlinear systems is proposed. Apart from ensuring that all signals of the closed-loop system keep bounded, the proposed control scheme not only makes the tracking error converge to an arbitrarily small neighborhood around the origin, but also guarantees the tracking error trajectory within the limits set by PPTC. Ultimately, two simulations are applied to verify the validity of the proposed control scheme.

Event‐triggered adaptive estimated inverse neural network control of uncertain nonlinear systems with unknown hysteresis effects

AbstractThis article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller.

Efficient learning control of uncertain nonlinear systems with input constraints: a disturbance observer-based neural network approach

Efficient learning control of uncertain nonlinear systems with input constraints: a disturbance observer-based neural network approach

A fully actuated system approach: Desired compensation adaptive robust control for uncertain nonlinear systems

A desired compensation adaptive robust control (DCARC) framework is presented for two types of fully actuated systems (FASs) subject to both parametric and nonlinear uncertainties based on the Lyapunov theory. The designed controller consists of three parts: (i) a fundamental linear state error feedback term; (ii) an adaptive feedforward compensation term; (iii) a robust compensation term. Different from the existing FAS approaches, the adaptive compensation in the proposed controller relies on the noise-free desired trajectory and online parameter estimate only. A better overall tracking performance with an accelerated transient response is expected, arising from significantly reducing interaction between the robustness part and the adaptation part. Through theoretical analysis, the proposed controller ensures that the estimation error of the unknown parameter vector and the tracking error finally converge to a bounded ellipsoid. The comparative simulation of a permanent magnet (PM) stepper motor demonstrates the effectiveness of the presented approach.

Supervisory adaptive safety control for uncertain nonlinear systems

This paper investigates the supervisory adaptive safety control problem for uncertain nonlinear systems with unmeasurable states. In order to estimate unknown parameters and unmeasurable states, a supervisory observer framework consisting of a multi-observer bank and a supervisor unit, is proposed based on a dividing rectangles (DIRECT) algorithm. Under a desired accuracy, the parameter estimates converge to near the true parameters within finite-time while the estimation error of the system state converges to a small neighborhood near the origin. Also, an observer robust adaptive control barrier function is constructed to ensure safety for uncertain nonlinear systems by guaranteeing controlled invariance of a time-varying auxiliary set. By adjusting some parameters in the DIRECT algorithm, the time-varying auxiliary set approaches the original safe set, which reduces the conservatism generated by the compression of the safe set. Finally, two examples, including an adaptive cruise control system, are given to demonstrate the effectiveness of the proposed method.

Actor–Critic-Based Predefined-Time Fuzzy Adaptive Optimal Control for Uncertain Nonlinear Systems With Input Saturation

Actor–Critic-Based Predefined-Time Fuzzy Adaptive Optimal Control for Uncertain Nonlinear Systems With Input Saturation

Hierarchical Self-Organizing Fuzzy Control for Uncertain Nonlinear Systems

Hierarchical Self-Organizing Fuzzy Control for Uncertain Nonlinear Systems

Approximate Optimal Adaptive Prescribed Performance Control for Uncertain Nonlinear Systems With Feature Information

Approximate Optimal Adaptive Prescribed Performance Control for Uncertain Nonlinear Systems With Feature Information

A Simple Linear Controller Design to Ensure Global Exponential Stability for a Class of Uncertain Fifth-Order Nonlinear Systems

This paper intends to explore the controller design problem for a class of uncertain fifth-order nonlinear control systems. Combining the theory of differential and integral inequalities, a linear controller will be proposed to promote a class of nonlinear control systems with multiple uncertainties to achieve the goal of global exponential stability. In addition, the guaranteed exponential convergence rate of such uncertain nonlinear systems will also be precisely calculated. Finally, some numerical simulation results will also be presented to verify and illustrate the correctness of this main theorem and the design process of the controller

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.