Sliding mode control parameter tuning using ant colony optimization for a 2-DOF hydraulic servo system

Mpanza, Lindokuhle J.; Pedro, Jimoh O.

doi:10.1109/i2cacis.2016.7885322

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…To specify the controller parameters appropriately, the feedback control methods, including the SMC method, can be integrated with various choices of swarm optimization algorithms [37]- [51]. Dragonfly algorithm (DA) is one of the optimization techniques which has been employed for solving various optimization problems and has been extensively utilized in sliding mode controller parameters [39], [42]- [56].…”

Section: Introductionmentioning

confidence: 99%

Time-varying sliding mode controller for heat exchanger with dragonfly algorithm

Boonyaprapasorn

Kuntanapreeda

Ngiamsunthorn

et al. 2023

IJECE

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<span lang="EN-US">This article proposes the design of a sliding mode controller with a time-varying sliding surface for the plate heat exchanger. A time-varying sliding mode controller (TVSMC) combines the benefit of the control system’s robustness and convergence rate. Using Lyapunov stability theory, the stability of the designed controller is proved. In addition, the controller parameters of the designed controller are specified optimally via the dragonfly algorithm (DA). The input constraint’s effect is considered in the controller design process by applying the concept of the auxiliary system. The bounded disturbances are applied to investigate the robustness of the proposed techniques. Moreover, the quasi-sliding mode controller (QSMC) is developed as a benchmark to evaluate the convergence behavior of the proposed TVSMC technique. The simulation results demonstrate the proposed TVSMC with the optimal parameters provided by the DA algorithm (TVSMC+DA) can regulate the temperature to the desired level under bounded disturbances. When compared to the QSMC method, the TVSMC+DA performs significantly faster convergence speed and greater reduction in chattering occurrence. The results clearly indicate that the proposed controller can enhance convergence properties while being robust to disturbances.</span>

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Section: Introductionmentioning

confidence: 99%

Time-varying sliding mode controller for heat exchanger with dragonfly algorithm

Boonyaprapasorn

Kuntanapreeda

Ngiamsunthorn

et al. 2023

IJECE

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“…Researchers all over the world began to investigate and develop several types of optimization approaches to use in the controller design process to control the EHSA system. Ant colony optimization [30], Genetic Method [31], Artificial Bee Colony optimization [32], Firefly algorithm [33], and Particle Swarm Optimization (PSO) [34][35][36] are a few examples. Distinct kinds of algorithms were employed to select controller parameters and control the EHSA system.…”

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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Model Predictive Control of Half-Car Active Suspension Systems Using Particle Swarm Optimisation

2020

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Sliding mode control parameter tuning using ant colony optimization for a 2-DOF hydraulic servo system

Cited by 4 publications

References 23 publications

Time-varying sliding mode controller for heat exchanger with dragonfly algorithm

Time-varying sliding mode controller for heat exchanger with dragonfly algorithm

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

Model Predictive Control of Half-Car Active Suspension Systems Using Particle Swarm Optimisation

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