scholarly journals Neural network adaptive command filtered control of robotic manipulators with input saturation

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989477
Author(s):  
Lin Wang ◽  
Chunzhi Yang
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Tracking Error ◽  
Input Saturation ◽  
Robotic Manipulators ◽  
Small Region ◽  
Network Control ◽  
External Disturbances ◽  
Time Control ◽  
The Neural Network

This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.

scholarly journals Adaptive Finite-time Dynamic Surface Neural Network Control of an Uncertain Robot with Output Constraint and Input Saturation

2021 ◽  
Author(s):  
Zhao Zhang ◽  
Lingxi Peng ◽  
Zhijia Zhao
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Control Method ◽  
Tracking Error ◽  
Input Saturation ◽  
Network Control ◽  
Neural Network Control ◽  
Dynamic Surface ◽  
Time Dynamic ◽  
Output Constraints

Abstract In this study, a finite-time dynamic surface neural network control is developed for an uncertain n-link robot subject to input saturation and output constraints. First, a barrier Lyapunov function and a hyperbolic tangent function are applied to solve the system constraints using a dynamic surface control. Subsequently, a radial basis function neural network is utilized to handle system uncertainties. Then, a finite-time filter is employed in the design to achieve the fast convergence and a Nussbaum function is employed to optimize the design process. Finally, the simulation results show that the dynamic tracking error is proved to converging to zero, and the proposed control method is effective and never violates the constraints.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

scholarly journals Robust Prescribed Performance Control of Uncertain Vehicle Lateral System under State Constraints

2021 ◽  
Vol 2083 (3) ◽  
pp. 032029
Author(s):  
Jing Yu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Lyapunov Function ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Lateral Control ◽  
Barrier Lyapunov Function ◽  
The Neural Network

Abstract In the study of the zero-error tracking control problem for vehicle lateral control systems under full-state constraints and nonparametric uncertainties, the zero-error tracking control problem is presented in this paper. A neural adaptive tracking control scheme is proposed by combining the error transformation of the vehicle lateral control system with the barrier Lyapunov function, which realizes that the tracking error of the vehicle lateral control converges to a prescribed compact set at a controllable or specified convergence rate in a specified finite time. The scheme has the following significant characteristics: 1) Based on the Nussbaum gain, the preset new energy finite-time control algorithm, the tracking error of the vehicle lateral control system with non-parametric uncertainty and external disturbance decreases to zero with t → ∞. In addition, it also has the control ability to cope with the presence or even unknown moment of inertia of the system. 2) Barrier Lyapunov function (BLF) ensures the bounded input of the neural network during the whole system envelope, and ensures the stable learning and approximation of the neural network. Furthermore, the bounded stability of the closed-loop system is proved by Lyapunov analysis. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

Finite-time tracking control for nonstrict-feedback nonlinear systems with unknown time delays and input saturation

2021 ◽  
pp. 014233122110583
Author(s):  
Xiaojing Qi ◽  
Wenhui Liu
Keyword(s):  
Finite Time ◽  
State Constraints ◽  
Tracking Error ◽  
Input Saturation ◽  
Small Neighborhood ◽  
Delay Systems ◽  
Fuzzy Logic Systems ◽  
Time Control ◽  
Full State ◽  
Full State Constraints

In this article, the problem of adaptive finite-time control is studied for a category of nonstrict-feedback nonlinear time-delay systems with input saturation and full state constraints. The fuzzy logic systems are applied to model the unknown nonlinear terms in the systems. Then, a novel tan-type barrier Lyapunov function is adopted to overcome the problem of full state constraints. By utilizing the finite-time control theory and the backstepping technique, a finite-time fuzzy adaptive controller is designed. The controller can guarantee that the tracking error is adjusted around zero with a small neighborhood in a finite time and all the signals in the closed-loop system are bounded. Finally, two simulation examples are included to verify the validity and feasibility of the control scheme.

scholarly journals Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiaoli Jiang ◽  
Mingyue Liu ◽  
Siqi Liu ◽  
Jing Xu ◽  
Lina Liu
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Input Saturation ◽  
Small Neighborhood ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Feedback Form ◽  
Design Algorithm ◽  
Adaptive Neural Network Control

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

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