Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

Yu, Wangshui; Zhu, Kai; Yu, Yating

doi:10.1109/access.2024.3368762

IEEE Access

2024

DOI: 10.1109/access.2024.3368762

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Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

Wangshui Yu,

Kai Zhu,

Yating Yu

Abstract: The traditional active suspension system controlled by fuzzy PID fails to consider external road information adaptively, lending to low control precision. To solve this problem, this novel variable universe fuzzy PID control strategy, which combines road recognition and chaotic particle swarm optimization (CPSO), is proposed. Firstly, a dynamic model of four degree of freedom vehicle suspension is established based on the half-vehicle model. Secondly, the Back Propagation (BP) neural network is optimized by Te… Show more

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Cited by 4 publications

References 24 publications

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Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Tan,

Tu,

Vinh

et al. 2024

IEEE Access

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Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Tan,

Tu,

Vinh

et al. 2024

IEEE Access

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Variable universe fuzzy inference decided neural network feed-forward compensation for pid control of motor position

Li,

Zhu,

Huang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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To improve the compensation accuracy of neural network decided by traditional fuzzy inference engine (FIE), a variable universe fuzzy inference (VUFI) decided neural network feed-forward compensation method for PID control of motor position is proposed. The method contains four control blocks: fuzzy inference (FI) block, variable universe (VU) block, neural network (NN) control block, and basic control block. The FI block adaptively decides the feed-forward control quantity of NN based on the error and the error change rate of control system to keep the dynamic performance of NN at the beginning of the control or the transient jump signal. The VU block constructs the contraction expansion (CE) factors from the absolute change quantity of NN weights and adjusts the universes of membership functions based on the learning situation of NN in real time so that high-precision inference can be achieved. The NN control block consists of a neural network identifier (NNI) and a neural network controller (NNC) that has the same structure with the NNI, and the NNI dynamically transfers network parameters to the NNC and outputs feed-forward compensation control quantity after learning the dynamic inverse model of DC servo motor online. The basic control block adopts a PID controller, which provides the learning samples for NN online. Experimental results proved that the proposed method can improve the inference accuracy of FIE without sacrificing steady-state accuracy, significantly reduce overshoot, and have better stability and dynamic performance.

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PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization

Roeva,

Slavov,

Kralev

2024

Processes

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This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

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Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

Cited by 4 publications

References 24 publications

Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Variable universe fuzzy inference decided neural network feed-forward compensation for pid control of motor position

PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization

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