scholarly journals A Robust Adaptive CMAC Neural Network-Based Multisliding Mode Control Method for Unmatched Uncertain Nonlinear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Honghui Wang ◽  
Xiaojun Yu ◽  
Shicheng Liang ◽  
Sheng Dong ◽  
Zeming Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Strategy ◽  
Control Method ◽  
Control Variable ◽  
Uncertain Nonlinear Systems ◽  
Cerebellar Model Articulation Controller ◽  
Mode Control ◽  
Cmac Neural Network ◽  
Robust Adaptive

This paper proposes a new robust adaptive cerebellar model articulation controller (CMAC) neural network-based multisliding mode control strategy for a class of unmatched uncertain nonlinear systems. Specifically, by employing a stepwise recursion-based multisliding mode method, such a proposed strategy is able to obtain the virtual variables and the actual control inputs of each order first, and then it reduces the conservativeness for controller parameter design by adopting the CMAC neural network to learn both system uncertainties and virtual control variable derivatives of each order online. Meanwhile, with the hyperbolic tangent function being chosen to replace the sign function in the variable structured control components, the proposed strategy is able to avoid the chattering effects caused by the discontinuous inputs. The stability analysis shows that the proposed control strategy ensures that both the system tracking errors and the sliding modes of each order could converge exponentially to any saturated layer being set. The control strategy was also applied onto a passive electrohydraulic servo loading system for verifications, and simulation results show that such a proposed control strategy is robust against all system nonlinearities and external disturbances with much higher control accuracy being achieved.

Design of Robust Adaptive Inverse Controller for a Hypersonic Aircraft Based on CMAC Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 1135-1139
Author(s):  
Cong Chao Yao ◽  
Xin Min Wang ◽  
Xiao Chen Zhang
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Control Method ◽  
Uncertain System ◽  
Lyapunov Stability Theory ◽  
Convergence Condition ◽  
Cerebellar Model Articulation Controller ◽  
Robust Tracking Control ◽  
Cmac Neural Network ◽  
Robust Adaptive

A new control structure based on dynamic inversion (DI) and neural network technology for a class of nonlinear uncertain system is proposed. DI is an effective nonlinear tracking and decoupling control method. However, the performance of the current DI may significantly degrade when internal unmodeled dynamics and external disturbances exist. In this paper, a Cerebellar Model Articulation Controller (CMAC) neural network is used to improve overall system performance of robust tracking control. The algorithm convergence condition is shown. Based on Lyapunov stability theory, all signals are proved to be uniform convergence. Finally, the flight control system of the hypersonic vehicle is designed based on the proposed method and the simulation results demonstrate the excellent performance and robustness of the controllers.

scholarly journals Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Li-lian Huang ◽  
Jin Chen
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Control Method ◽  
Networked Control Systems ◽  
Networked Control ◽  
Cerebellar Model Articulation Controller ◽  
Pd Controller ◽  
Control Effect ◽  
Cmac Neural Network ◽  
Reasoning Mechanism

The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively.

Event-triggered Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Systems

2021 ◽  
pp. 2150275
Author(s):  
Hui Hu ◽  
Yang Li ◽  
Wei Yi ◽  
Yuebiao Wang ◽  
Fan Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Uncertain Nonlinear Systems ◽  
Network Resource ◽  
Lyapunov Approach ◽  
Stability Method ◽  
The Stability ◽  
Event Triggered

In the paper, an event triggering adaptive control method based on neural network (NN) is proposed for a class of uncertain nonlinear systems with external disturbances. In order to reduce the network resource utilization, a novel event-triggered condition by the Lyapunov approach is proposed. In addition, the NN controller and adaptive parameters determined by the Lyapunov stability method are updated only at triggered instants to reduce the amount of calculation. Only one NN is used as the controller in the entire system. The stability analysis results of the closed-loop system are obtained by the Lyapunov approach, which shows that all the signals in the systems with bounded disturbance are semi-globally bounded. Zeno behavior is avoided. Finally, the analytical design is confirmed by the simulation results on a two-link robotic manipulator.

Melt temperature learning control of injection molding process based on CMAC neural network

2011 ◽  
Vol 31 (1) ◽  
Author(s):  
Yonggang Peng ◽  
Wei Wei
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Control Method ◽  
System Modeling ◽  
Learning Control ◽  
Injection Molding Process ◽  
Cerebellar Model Articulation Controller ◽  
Molding Process ◽  
Melt Temperature ◽  
Cmac Neural Network

Abstract Accurately monitoring and controlling melt temperature in the injection molding process can be a challenge. A barrel temperature model was achieved with the use of a system modeling method. Because of time variance, uncertainty and non-linearity of injection molding barrel temperature, a learning control method based on the use of a cerebellar model articulation controller (CMAC) neural network was proposed. Simulations and experimental results have demonstrated that this method elicits high response speed and excellent control accuracy of barrel melt temperature.

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