scholarly journals Adaptive Differentiator-Based Predefined-Time Control for Nonlinear Systems Subject to Pure-Feedback Form and Unknown Disturbance

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Man Yang ◽  
Qiang Zhang ◽  
Ke Xu ◽  
Ming Chen
Keyword(s):  
Nonlinear Systems ◽  
Control Method ◽  
Tracking Error ◽  
Nonlinear Function ◽  
Original System ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Rbf Neural Networks ◽  
Feedback Form ◽  
Output Tracking Control

In this article, by utilizing the predefined-time stability theory, the predefined-time output tracking control problem for perturbed uncertain nonlinear systems with pure-feedback structure is addressed. The nonaffine structure of the original system is simplified as an affine form via the property of the mean value theorem. Furthermore, the design difficulty from the uncertain nonlinear function is overcome by the excellent approximation performance of RBF neural networks (NNs). An adaptive predefined-time controller is designed by introducing the finite-time differentiator which is used to decrease the computational complexity problem appeared in the traditional backstepping control. It is proved that the proposed control method guarantees all signals in the closed-loop system remain bound and the tracking error converges to zero within the predefined time. Based on the controller designed in this paper, the expected results can be obtained in predefined time, which can be illustrated by the simulation results.

scholarly journals Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chaojiao Sun ◽  
Bo Jing ◽  
Zongcheng Liu
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Tracking Error ◽  
Nonlinear Function ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Adaptive Neural Control ◽  
Control Gain ◽  
Control Scheme ◽  
Nonaffine Nonlinear Systems

An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

scholarly journals Real Time Stabilization of Lipschitz Nonlinear Systems with Nonlinear Output

2020 ◽  
Vol 53 (4) ◽  
pp. 493-498
Author(s):  
Assem Thabet ◽  
Ghazi Bel Haj Frej ◽  
Noussaiba Gasmi ◽  
Brahim Metoui
Keyword(s):  
Nonlinear Systems ◽  
Real Time ◽  
Nonlinear Function ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Linear Matrix ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Lipschitz Nonlinear Systems ◽  
Parameter Varying

This brief discusses a simple stabilization strategy for a class of Lipschitz nonlinear systems based on the transformation of nonlinear function to Linear Parameter Varying system. Due to the introduction of the Differential Mean Value Theorem (DMVT), the dynamic and output nonlinear functions are transformed into Linear Parameter Varying (LPV) functions. This allows to increase the number of decision variables in the constraint to be resolved and, then, get less conservative and more general Linear Matrix Inequality (LMI) conditions. The established sufficient stability conditions are in the form of LMI with the introduction of a cost control to ensure closed-loop stability. Finally, Real Time Implementation (RTI) using a DSP device (ARDUINO UNO R3) to a typical robot is given to illustrate the performances of the proposed method with a comparison to some existing results.

scholarly journals Adaptive Fuzzy Fast Finite-Time Tracking Control for Nonlinear Systems in Pure-Feedback Form with Unknown Disturbance

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ke Xu ◽  
Huanqing Wang ◽  
Xiaoping Liu ◽  
Ming Chen
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Control ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Small Neighborhood ◽  
Mean Value Theorem ◽  
Feedback Form ◽  
Adaptive Fuzzy ◽  
Unknown Disturbance

In this paper, based on the fast finite-time stability theorem, an adaptive fuzzy control problem is considered for a class of nonlinear systems in pure-feedback form with unknown disturbance. In the controller design process, the mean value theorem is applied to address the nonaffine structure of the pure-feedback plant, the universal approximation capability of the fuzzy logic system (FLS) is utilized to compensate the unknown uncertainties, and the adaptive backstepping technique is used to design the controller model. Combined with the selection of the appropriate Lyapunov function at each step, a fuzzy-based adaptive tracking control scheme is proposed, which ensures that all signals in the closed-loop system are bounded and tracking error converges to a small neighborhood of the origin in fast finite-time. Finally, simulation results illustrate the validity of the proposed approach.

scholarly journals Adaptive Fuzzy Control for Stochastic Pure-Feedback Nonlinear Systems with Unknown Hysteresis and External Disturbance

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xikui Liu ◽  
Yingying Ge ◽  
Yan Li
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Control ◽  
External Disturbance ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Nonlinear Function ◽  
Small Neighborhood ◽  
Mean Value Theorem ◽  
Adaptive Fuzzy ◽  
Control Scheme

This paper solves the tracking control problem of a class of stochastic pure-feedback nonlinear systems with external disturbances and unknown hysteresis. By using the mean-value theorem, the problem of pure-feedback nonlinear function is solved. The direction-unknown hysteresis problem is solved with the aid of the Nussbaum function. The external disturbance problems can be solved by defining new Lyapunov functions. Using the backstepping technique, a new adaptive fuzzy control scheme is proposed. The results show that the proposed control scheme ensures that all signals of the closed-loop system are semiglobally uniformly bounded and the tracking error converges to the small neighborhood of origin in the sense of mean quartic value. Simulation results illustrate the effectiveness of the proposed control scheme.

Position Domain PD Control for Contour Tracking

2010 ◽  
Author(s):  
P. R. Ouyang ◽  
Truong Dam
Keyword(s):  
Motion Tracking ◽  
Control Method ◽  
Tracking Error ◽  
Original System ◽  
Contour Tracking ◽  
Pd Control ◽  
Tracking Errors ◽  
Motion System ◽  
Common Control ◽  
The Time Domain

For multi-axis motion control applications, contour tracking is one of the most common control problems encountered by industrial manipulators and robots. In this paper, a position domain PD control method is proposed for the purpose of improving the contour tracking performance. To develop the new control method, the multi-axis motion system is viewed as a master-slave motion system where the master motion is sampled equidistantly and used as an independent variable, while the slave motions are described as functions of the master motion according to the contour tracking requirements. The dynamic model of the multi-axis motion system is developed in the position domain based on the master motion by transforming the original system dynamic equations from the time domain to the position domain. In this control methodology, the master motion will yield zero tracking error for the position as it is used as reference, and only the slave motion tracking errors will affect the final contour tracking errors. The proposed position domain PD controller is successfully examined in a Cartesian robotic system for linear motion tracking and circular contour tracking.

Output tracking control of high-order nonlinear systems with dynamic uncertainties

2019 ◽  
Vol 42 (8) ◽  
pp. 1511-1520
Author(s):  
Zong-Yao Sun ◽  
Yu-Jie Gu ◽  
Qinghua Meng ◽  
Wei Sun ◽  
Zhen-Guo Liu
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
High Order ◽  
Approximation Technique ◽  
Zero Dynamic ◽  
Adding A Power Integrator ◽  
Output Tracking ◽  
Output Tracking Control ◽  
Dynamic Uncertainties

This paper investigates the output tracking control problem for a class of nonlinear systems with zero dynamic. On the basis of adding a power integrator method and approximation technique, an appropriate controller is proposed to guarantee that the tracking error turns to a preassigned neighborhood of the origin. The systems under investigation allow unmeasurable dynamic uncertainties, unknown nonlinear functions and unknown high-order terms. As an application, two examples are provided to illustrate the effectiveness of a control strategy.

scholarly journals Robust Preview Control for a Class of Uncertain Discrete-Time Lipschitz Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiao Yu ◽  
Fucheng Liao ◽  
Jiamei Deng
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Tracking Error ◽  
Reference Signal ◽  
Original System ◽  
Linear Matrix ◽  
Preview Control ◽  
Lipschitz Nonlinear Systems ◽  
Error System ◽  
Discrete Integrator

This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller.

scholarly journals Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2783
Author(s):  
Yanbin Liu ◽  
Jue Wang ◽  
Luis Gomes ◽  
Weichao Sun
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Networked Control Systems ◽  
Tracking Error ◽  
Adaptive Robust Control ◽  
Feedback Form ◽  
Control Approach ◽  
Input Quantization ◽  
The Stability ◽  
Strict Feedback

Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.

scholarly journals Adaptive Fixed-Time Tracking Consensus Control for Multiagent Nonlinear Pure-Feedback Systems with Performance Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Pinwei Li ◽  
Jiyang Dai ◽  
Jin Ying
Keyword(s):  
Fixed Time ◽  
Mean Value ◽  
High Order ◽  
Mean Value Theorem ◽  
Feedback Systems ◽  
Feedback Form ◽  
Consensus Control ◽  
Performance Constraints ◽  
Whole Process ◽  
Constraint Variable

This paper investigates adaptive fixed-time tracking consensus control problems for multiagent nonlinear pure-feedback systems with performance constraints. Compared with existing results of first/second/high-order multiple agent systems, the studied systems have more complex nonlinear dynamics with each agent being modeled as a high-order pure-feedback form. The mean value theorem is introduced to address the problem of nonaffine structure in nonlinear pure-feedback systems. Meanwhile, radial basis function neural networks (RBFNNs) are employed to approximate unknown functions. Furthermore, a constraint variable is used to guarantee that all local tracking errors are within the prescribed boundaries. It is shown that, by utilizing the proposed consensus control protocol, each tracking consensus error can converge into a neighborhood around zero within designed fixed time, the tracking consensus performance can be ensured during the whole process, and all signals in the investigated systems are bounded. Finally, two simulations are performed and the results demonstrate the effectiveness of the proposed control strategy.

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