Active Suspension Control Based on Particle Swarm Optimization

2020 ◽  
Vol 13 (1) ◽  
pp. 60-78
Author(s):  
Shaobin Lv ◽  
Guoqiang Chen ◽  
Jun Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Ride Comfort ◽  
Control Strategies ◽  
Particle Swarm ◽  
Active Suspension ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Suspension Control

Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state to enhance the performance of active suspension that has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research directions for active suspension patents. Objective: The numerical analysis method is proposed to study the performance characteristics of the active suspension controlled by different controllers. Methods: The active suspension control model and control strategy based on particle swarm optimization are established, and two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed. Moreover, two active suspension systems are optimized by particle swarm optimization. Results: The results of the analysis show that the performance of the active suspension is significantly improved compared with the passive suspension when the vehicle runs on the same road. The ride comfort of the active suspension controlled by the fuzzy PID controller has the best adaptive performance when the vehicle runs on different grade roads or white noise roads. The active suspension controlled by the fuzzy PID controller has the best ride comfort. Conclusion: A good control strategy can effectively improve the performance of the active suspension. To improve the performance of the active suspension, it can be controlled by utilizing different control strategies. The results lay a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.

A Hybrid PSO-LEVY Flight Algorithm Based Fuzzy PID Controller for Automatic Generation Control of Multi Area Power Systems

2017 ◽  
Vol 6 (2) ◽  
pp. 42-63 ◽  
Author(s):  
Ajit Kumar Barisal ◽  
Tapas Kumar Panigrahi ◽  
Somanath Mishra
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Generation Control

This article presents a hybrid PSO with Levy flight algorithm (LFPSO) for optimization of the PID controllers and employed in automatic generation control (AGC) of nonlinear power system. The superiority of the proposed LFPSO approach has been demonstrated with comparing to recently published Lozi map-based chaotic optimization algorithm (LCOA) and Particle swarm optimization to solve load-frequency control (LFC) problem. It is found that the proposed LFPSO method has robust dynamic behavior in terms of settling times, overshoots and undershoots by varying the system parameters and loading conditions from their nominal values as well as size and locations of disturbance. Secondly, a three-area thermal power system is considered with nonlinear as Generation Rate Constraints (GRC) and outperforms to the results of Bacteria Foraging algorithm based integral controller as well as hybrid Differential Evolution and Particle Swarm Optimization based fuzzy PID controller for the similar power system. Finally, the proficiency of the proposed controller is also verified by random load patterns.

scholarly journals Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abroon Jamal Qazi ◽  
Clarence W. de Silva ◽  
Afzal Khan ◽  
Muhammad Tahir Khan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Active Suspension ◽  
Suspension System ◽  
Swarm Optimization ◽  
Quarter Model ◽  
Semiactive Suspension

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

Design of Fuzzy PID Controller Based on Adaptive Particle Swarm Optimization with Cloud Theory

2013 ◽  
Vol 706-708 ◽  
pp. 720-723
Author(s):  
Ming Feng Ying ◽  
Li Xin Zai ◽  
Hai Xiang Wang
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Excitation Control ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Cloud Theory

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

Design of Fuzzy-PID Controller for Ride Comfort Enhancement of a Half-Vehicle with Active Suspension System

2013 ◽  
Vol 313-314 ◽  
pp. 382-386
Author(s):  
Wen Kui Lan ◽  
Er Dong Ni
Keyword(s):  
Pid Controller ◽  
Passive Control ◽  
Ride Comfort ◽  
Control Method ◽  
Active Suspension ◽  
Suspension System ◽  
The Body ◽  
Fuzzy Pid ◽  
Vehicle Model ◽  
Fuzzy Pid Controller

A fuzzy-PID controller is developed and applied to the active suspension system for the ride comfort enhancement of a half-vehicle model. A four degree-of-freedom vehicle model with active suspension system is proposed, which focused on the passenger’s ride comfort performance, and a fuzzy-PID controller is developed by incorporating the fuzzy logic control mechanism into the modifications of the PID structure. The performance of the proposed controller has been verified by comparing it with passive control method in MATLAB/Simulink. The simulation results indicate that the developed fuzzy-PID controller enhances the ride comfort performance of the vehicle active suspension system by reducing the body acceleration and pitch angle significantly.

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