scholarly journals Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

2018 ◽  
Vol 14 (02) ◽  
pp. 103 ◽  
Author(s):  
Huifang Kong ◽  
Yao Fang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Model Parameters ◽  
Pid Algorithm ◽  
Mode Control ◽  
Model Free

<p class="0abstract"><span lang="EN-US">The control of nonlinear system is the hotspot in the control field. The paper proposes an algorithm to solve the tracking and robustness problem for the discrete-time nonlinear system. The completed control algorithm contains three parts. First, the dynamic linearization model of nonlinear system is designed based on Model Free Adaptive Control, whose model parameters are calculated by the input and output data</span><span lang="EN-US"> of system</span><span lang="EN-US">. Second, the model error is estimated using the Quasi-sliding mode control algorithm</span><span lang="EN-US">, hence, the whole model of system is estimated</span><span lang="EN-US">. Finally, the neural network </span><span lang="EN-US">PID </span><span lang="EN-US">controller is designed to get the optimal control law. The convergence and BIBO stability of the control system is proved by the Lyapunov function. The simulation results </span><span lang="EN-US">in</span><span lang="EN-US"> the </span><span lang="EN-US">linear and </span><span lang="EN-US">nonlinear system validate the effectiveness and robustness of the algorithm.</span><span lang="EN-US"> The robustness </span><span lang="EN-US">effort </span><span lang="EN-US">of </span><span lang="EN-US">Quasi-sliding mode control algorithm</span><span lang="EN-US"> in nonlinear system is also verified in the paper.</span></p>

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

scholarly journals Discrete–Time Integral Sliding Mode Control with Disturbances Compensation and Reduced Chattering for Pv Grid–Connected Inverter

2015 ◽  
Vol 66 (2) ◽  
pp. 61-69 ◽  
Author(s):  
Santolo Meo ◽  
Vincenzo Sorrentino
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Integral Sliding Mode Control ◽  
Integral Sliding Mode ◽  
Mode Control ◽  
Time Integral ◽  
Grid Connected Inverter ◽  
Disturbances Compensation

Abstract In the paper a new discrete-time integral sliding mode control (DISMC) with disturbances compensation and reduced chattering for grid-connected inverter is proposed for active and reactive power regulation. Differently by many SMC proposed in literature that have a time-continuous formulation in spite have been implemented with digital processor, the proposed DISMC is fully formulated in discrete-time, taking into account the effects introduced by a microprocessor-based implementation. As will be demonstrated such approach consents to reduce the chattering about the sliding manifold within a boundary layer of O(T2) thickness instead of O(T) (being T the sampling period of the control algorithm). Moreover it introduces a correction of the control vector which eliminates the influence of modeling error and external disturbances improving stability and robustness of the controlled system. Constant converter switching frequency is achieved by using space vector modulation, which eases the design of the ac harmonic filter. In the paper, after a detailed formalization of the proposed control algorithm, several numerical and experimental results on a three-phase grid-connected inverter prototype are shown, proving the effectiveness of the control strategy.

scholarly journals Neural Network Identification and Sliding Mode Control for Hysteresis Nonlinear System with Backlash-Like Model

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ruiguo Liu ◽  
Xuehui Gao
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Control Strategies ◽  
Tracking Error ◽  
Mode Control ◽  
Hysteresis Nonlinearity ◽  
System States

A new neural network sliding mode control (NNSMC) is proposed for backlash-like hysteresis nonlinear system in this paper. Firstly, only one neural network is designed to estimate the unknown system states and hysteresis section instead of multiscale neural network at former researches since that can save computation and simplify the controller design. Secondly, a new NNSMC is proposed for the hysteresis nonlinearity where it does not need tracking error transformation. Finally, the Lyapunov functions are adopted to guarantee the stabilities of the identification and control strategies semiglobally uniformly ultimately bounded (UUB). Two cases simulations are proved the effectiveness of the presented identification approach and the performance of the NNSMC.

scholarly journals Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanchao Yin ◽  
Hongwei Niu ◽  
Xiaobao Liu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Approximation Error ◽  
Model Parameters ◽  
Control Law ◽  
The Novel ◽  
Adaptive Neural Network ◽  
Mode Control ◽  
Attitude Tracking

A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

scholarly journals The Control Algorithm and Experimentation of Coaxial Rotor Aircraft Trajectory Tracking Based on Backstepping Sliding Mode

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 337
Author(s):  
Jiulong Xu ◽  
Yongping Hao ◽  
Junjie Wang ◽  
Lun Li
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Model Parameters ◽  
External Interference ◽  
Mode Control ◽  
Coaxial Rotor ◽  
Non Linear ◽  
Backstepping Sliding Mode Control

In view of the uncertainty of model parameters, the influence of external disturbances and sensor noise on the flight of coaxial rotor aircraft during autonomous flight, a robust backstepping sliding mode control algorithm for the position and attitude feedback control system is studied to solve the trajectory tracking problem of an aircraft in the case of unknown external interference. In this study, a non-linear dynamic model based on a disturbed coaxial rotor aircraft was established for an unknown flight. Then, a non-linear robust backstepping sliding mode controller was designed, which was divided into two sub-controllers: the attitude controller and the position controller of the coaxial rotor aircraft. In the controller, virtual control was introduced to construct the Lyapunov function to ensure the stability of each subsystem. The effectiveness of the proposed controller was verified through numerical simulation. Finally, the effectiveness of the backstepping sliding mode control algorithm was verified by flight experiments.

Sign in / Sign up

Export Citation Format

Share Document