scholarly journals Constrained Adaptive Neural Control of Nonlinear Strict-Feedback Systems with Input Dead-Zone

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jingping Shi ◽  
Zhonghua Wu ◽  
Jingchao Lu
Keyword(s):  
Neural Network ◽  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
State Feedback Control ◽  
System Transformation ◽  
Feedback Systems ◽  
Limit Condition ◽  
Performance Function ◽  
Strict Feedback

This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

scholarly journals Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Xiang ◽  
Guangkui Xu ◽  
Fang Zhu ◽  
Chunzhi Yang
Keyword(s):  
Neural Control ◽  
Control Method ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Feedback Systems ◽  
Nonlinear Functions ◽  
Prescribed Performance ◽  
Complexity Problem ◽  
The Stability ◽  
Strict Feedback

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems. To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced. And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method. Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors. The stability of the whole closed-loop system is guaranteed via Lyapunov theory. Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.

scholarly journals Adaptive Neural Control for Nonaffine Pure-Feedback System Based on Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Chenyang Xu ◽  
Humin Lei ◽  
Jiong Li ◽  
Jikun Ye ◽  
Dongyang Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Neural Control ◽  
Control Method ◽  
Tracking Error ◽  
Adjustment Process ◽  
Equivalent Transformation ◽  
State Variables ◽  
Feedback Systems ◽  
Adaptive Neural Control ◽  
Learning Machine

For nonaffine pure-feedback systems, an adaptive neural control method based on extreme learning machine (ELM) is proposed in this paper. Different from the existing methods, this scheme firstly converts the original system into a nonaffine system containing only one unknown term by equivalent transformation, thus avoiding the cumbersome and complex indirect design process of traditional backstepping methods. Secondly, a high-performance finite-time-convergence-differentiator (FD) is designed, through which the system state variables and their derivatives are accurately estimated to ensure the control effect. Thirdly, based on the implicit function theorem, the ELM neural network is introduced to approximate the uncertain items of the system, which simplifies the repeated adjustment process of the network training parameters. Meanwhile, the minimum learning parameter algorithm (MLP) is adopted to design the adaptive law for the norm of the network weight vector, which significantly reduces calculations. And it is theoretically proved that the closed-loop control system is stable and the tracking error is bounded. Finally, the effectiveness of the designed controller is verified by simulation.

scholarly journals Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Shifen Shao ◽  
Kaisheng Zhang ◽  
Jun Li ◽  
Jirong Wang
Keyword(s):  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
Transient Behavior ◽  
Robotic Manipulators ◽  
System Stability ◽  
Control Effect ◽  
Tracking Errors ◽  
Control Scheme ◽  
Funnel Control

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

scholarly journals Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
José de Jesús Rubio ◽  
Rodrigo Encinas ◽  
Ricardo Balcazar
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Neural Control ◽  
Tracking Error ◽  
Uncertain System ◽  
Reference Trajectory ◽  
System A ◽  
Multiple Input ◽  
Feedback Linearization Control

The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

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