The Simulation of Semi-Active Suspension System Based on RBF Neural Network Sliding Mode Control

2012 ◽  
Vol 229-231 ◽  
pp. 1763-1767
Author(s):  
Hua Zhu
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Control Object ◽  
Active Suspension ◽  
Random Excitation ◽  
Suspension System ◽  
Performance Parameters ◽  
System A ◽  
Suspension Performance

Focusing on the nonlinear and uncertain characteristics of suspension system,a 2-DOF vehicle is regarded as the control object, sliding mode theory was used to design a sliding mode controller for the 2 DOFs vehicle semi-active suspension system,then RBF neural network was employed to optimize the sliding mode controller.The control effects of three key performance parameters of suspension, the acceleration of car body, the dynamic travel of suspension and the dynamic deflection of tire are studied under random excitation conditions.The results indicate that in comparison with the passive suspension，sliding mode semi-active control based on RBF neural network can improve suspension performance effectively.

Fuzzy Sliding Mode Control for a Three-Links Spatial Robot Based on RBF Neural Network

2011 ◽  
Vol 141 ◽  
pp. 303-307 ◽  
Author(s):  
Sheng Bin Hu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Sliding Mode Controller ◽  
System A ◽  
Radial Basis ◽  
Fuzzy Sliding Mode

To achieve the tracing control of a three-links spatial robot, a adaptive fuzzy sliding mode controller based on radial basis function neural network is proposed in this paper. The exponential sliding mode controller is divided into two parts: equivalent part and exponential corrective part. To realize the control without the model information of the system, a radial basis function neural network is designed to estimate the equivalent part. To diminish the chattering, a fuzzy controller is designed to adjust the corrective part according to sliding surface. The simulation studies have been carried out to show the tracking performance of a three-links spatial robot. Simulation results show the validity of the control scheme.

scholarly journals Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Feng Zhao ◽  
Mingming Dong ◽  
Yechen Qin ◽  
Liang Gu ◽  
Jifu Guan
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Active Suspension ◽  
Vertical Displacement ◽  
Suspension System ◽  
Adaptive Neural Network ◽  
Mode Control ◽  
Model Control

The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN) is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

A sliding Mode Control of Active Suspension Using H2/GH2 Approach to Surface Design

2014 ◽  
Vol 945-949 ◽  
pp. 1305-1308
Author(s):  
Shu Bo Liu ◽  
Xian Xi Luo ◽  
Zhi Hui Wan
Keyword(s):  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Surface Design ◽  
Quarter Car Model ◽  
Mode Control ◽  
System A ◽  
Passive Suspension System

This paper considers a new H2/GH2sliding mode control in controlling active suspension system subject to mismatched disturbance, without requiring a prior knowledge of disturbance. A quarter-car model is used in the study and the performance of the controller is compared with the existing passive suspension system. A simulation study is presented to prove the effectiveness and robustness of the proposed control method.

A Novel Fuzzy Controller to Improve Desired Suspension Performance

2007 ◽  
Author(s):  
Mohammad Biglarbegian ◽  
William Melek ◽  
Farid Golnaraghi
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Fuzzy Controller ◽  
Active Suspension ◽  
Suspension System ◽  
Suspension Systems ◽  
System A ◽  
Active Suspension Systems ◽  
Vehicle Suspension System ◽  
Suspension Performance

Semi-active suspension systems allow for adjusting the vehicle shock damping and hence improved suspension performance can be achieved over passive methods. This paper presents the design of a novel fuzzy control structure to concurrently improve ride comfort and road handling of vehicles with semi-active suspension system. A full car model with seven degrees of freedom is adopted that includes the vertical, roll, and pitch motions as well as the vertical motions of each wheel. Four decentralized fuzzy controllers are developed and applied to each individual damper in the vehicle suspension system. Mamdani’s method is applied to infer the damping coefficient output from the fuzzy controller. To evaluate the performance of the proposed controller, numerical analyses were carried out on a real road bump. Moreover, results were compared with well-known and widely used controllers such as Skyhook. It is shown that the proposed fuzzy controller is capable of achieving enhanced ride comfort and road handling over other widely used control methods.

Sliding Mode Control of Active Suspension System

2012 ◽  
Author(s):  
Yahaya Md. Sam ◽  
Johari Halim Shah Osman ◽  
Mohd. Ruddin Abd. Ghani
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Sliding Surface ◽  
Automotive Control ◽  
Quarter Car Model ◽  
Mode Control ◽  
System A ◽  
Control Scheme

Kertas kerja ini mempersembahkan suatu pendekatan baru dalam mengawal sistem gantungan aktif. Pendekatan yang dicadangkan ialah menggunakan skim kawalan mod gelangsar perkadaran-pengkerbedaan. Menggunakan permukaan gelangsar jenis ini, kestabilan asimptotik sistem adalah lebih pasti berbanding dengan jenis permukaan gelangsar konvensional. Skim kawalan yang dicadangkan diaplikasikan penggunaannya dalam mereka bentuk sistem gantungan aktif automotif model kereta suku. Keupayaan sistem kawalan yang direka bentuk akan dibandingkan dengan sistem gantungan pasif yang sedia ada. Kajian dalam bentuk penyelakuan dijalankan untuk membuktikan keberkesanan reka bentuk sistem kawalan berkenaan. Kata kunci: Kawalan automotif; kawalan mod gelangsar; kawalan tegap; ketakpastian tak terpadan The purpose of this paper is to present a new approach in controlling an active suspension system. This approach utilized the proportional integral sliding mode control scheme. Using this type of sliding surface, the asymptotic stability of the system during sliding mode is assured compared to the conventional sliding surface. The proposed control scheme is applied in designing an automotive active suspension system for a quarter-car model and its performance is compared with the existing passive suspension system. A simulation study is performed to prove the effectiveness of this control design. Key words: Automotive control; sliding mode control; robust control; mismatched uncertainties

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 Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

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