Designing a LQR controller for an electro-hydraulic-actuated-clutch model

Pourebrahim, Majid; Ghafari, Ali Selk; Pourebrahim, Mahshid

doi:10.1109/ccsse.2016.7784358

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Other controllers being studied and proposed to control the EHSA system include the Linear Quadratic Regulator (LQR) controller [11][12][13], the Optimal controller [14], the PI controller [15], the modified PI-D controller [16][17], the backstepping controller [18,19], the predictive controller [20][21][22], and the Sliding Mode Controller (SMC) [23][24][25][26]. Nonetheless, the SMC controller has been demonstrated to be the most effective controller for controlling nonlinear systems such as the EHSA system [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

Fadel¹

2023

JESA

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The Electro-Hydraulic Servo Actuators (EHSA) use the technology of the integration between hydraulic and electrical systems. These Actuators are widely used in many modern control systems including aircraft and missile flight control systems which can produce a rapid response, high power-to-weight ratio, and large stiffness. However, the nonlinearity of the EHSA systems has an impact on their accuracy and motion control. In this paper, a hybrid control algorithm sliding mode-PID (SMCPID) controller is designed based on the Particle Swarm Optimization (PSO) technique to maintain or enhance the performance of the utilized controller and reduce the chattering phenomena. First, a detailed non-linear mathematical model of the EHSA is developed and a computer simulation program is built using MATLAB/SIMULINK package. Then, three types of control strategies are designed and studied: PID, SMC, and hybrid SMCPID controllers. The optimization of the parameters of the PID controller and the variables of the SMC and hybrid SMCPID controllers is presented by using the PSO technique. The SMC control strategy is developed from the derived dynamic equation, whose stability is demonstrated by the Lyapunov theorem. Finally, in order to ensure the effectiveness of the optimized controllers, a comparative simulation study between the three types of controllers is presented where the root mean square error (RMS) values, and overshoot percentage (O.S %) are calculated and act as the performance indexes for comparison purposes. The simulation results show that the proposed hybrid SMCPID controller tuned by the PSO technique outperforms the PID and SMC controllers in terms of overall performance.

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Section: Introductionmentioning

confidence: 99%

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

Fadel¹

2023

JESA

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“…Several types of control approaches had been proposed by researchers to control the nonlinear EHA system, such as linear quadratic regulator [6][7][8], PID controller [9][10][11], model predictive controller [12][13][14], backstepping controller [15][16][17], sliding mode controller [18][19][20][21], and other hybrid controllers [22][23][24]. However, the PID controller is still the main choice for industrial applications due to its fast design process, simple structure, and robust control performance [25].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Weightage Values with Two Objective Functions in iPSO for Electro-Hydraulic Actuator System

Chai

Ghazali

Horng

et al. 2021

ARFMTS

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In this paper, the Proportional-Integral-Derivative (PID) controller with improved Particle Swarm Optimization (iPSO) algorithm is proposed for the positioning control of nonlinear Electro-Hydraulic Actuator (EHA) system. PID controller is chosen to control the EHA system due to its popularity in industrial applications. The PID controller parameters will be tuned by using the iPSO algorithm to get the lowest overshoot percentage and steady-state error. The conventional PSO algorithm has only one objective function to get the optimum parameters. However, this is not enough to increase the control performance of the EHA system. Therefore, an improved Particle Swarm Optimization (iPSO) that includes the mean error and overshoot percentage as the two objective functions is proposed in this paper. The most popular method in PSO that included two objective functions is Linear Weight Summation (LWS). In this method, the two objective functions are combined with certain weightage into one equation to give the best control performance. This paper focuses on determining the suitable weightage between these two objective functions so that the EHA system can produce the best control performance with less overshoot and less error. Overshoot percentage and steady-state error are used to indicate the best control performance. The results showed that EHA system can perform better by using suitable weightage between the mean error and overshoot percentage.

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“…Recently, many researchers have been focused on new approaches, such as fault-tolerant control and fault-tolerant control compensation based on fault estimation using observer technology [4,[10][11][12]. Research on linear quadratic regulator (LQR) controller were also implemented for the EHA system [13][14][15].…”

mentioning

confidence: 99%

LQR Controller Design for Mini Motion Package Electro-Hydraulic Actuator Control

Tan¹,

Tran²,

Phu³

et al. 2022

Proceedings of the Sixth International Conference on Research in Intelligent and Computing

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Electro-hydraulic actuators have been widely applied in the industry because they have several major advantages. In this paper, we focused on controlling the mini motion package electro-hydraulics actuator. First, a mathematical model of the electro-hydraulic actuator (EHA) was implemented to apply the control process to the proposed system. Second, we applied the linear quadratic regulator (LQR) controller to a linear model that is converted from the nonlinear EHA system. Finally, the numerical simulation results were performed in which the results obtained from the LQR controller were compared with the PID controller to show the superiority of the proposed solution.

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Designing a LQR controller for an electro-hydraulic-actuated-clutch model

Cited by 9 publications

References 4 publications

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

The Effects of Weightage Values with Two Objective Functions in iPSO for Electro-Hydraulic Actuator System

LQR Controller Design for Mini Motion Package Electro-Hydraulic Actuator Control

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