Longitudinal control of an intelligent vehicle using particle swarm optimization based sliding mode control

Thanok, Somphong; Parnichkun, Manukid

doi:10.1080/01691864.2015.1011298

Cited by 12 publications

(3 citation statements)

References 22 publications

(27 reference statements)

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“…whereÛ 1smc is equivalent control 5 and equals to − . h w 1 , b 1 is the inverse of the inertia moment, and f 1 represents system dynamics that, according to (15), is defined as follows:…”

Section: Optimal Space Determinationmentioning

confidence: 99%

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

2019

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In this paper, a new technique to reduce the search space in evolutionary algorithms for operative identification of sliding mode control parameters is proposed, especially in the case of a rigid satellite. The reduced domain provides a solution closer to the actual optimum at a lower computational cost. The optimized sliding mode controller provides superior performance that eliminates the need for trial‐and‐error exploration. In this approach, a linear quadratic regulator is initially designed for the equivalent linear system. In order to reduce the convergence time and computational cost, the regulator history is then used as a reference to provide optimal space for the sliding mode control parameters. Consequently, boundaries of the optimal space are fed to the genetic algorithm to optimize the control parameters. On the other hand, the particle swarm optimization uses the midpoint of the optimal space as an initial guess. The validation process is performed by tuning the sliding mode control parameters for a rigid satellite, taking into account uncertainties associated with the inertia matrix. The simulation results suggest the convergence to the near‐optimal solution, and the stability and robustness of the controller are enhanced by this technique.

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Section: Optimal Space Determinationmentioning

confidence: 99%

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

2019

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“…Soon et al, 2016;C. C. Soon, Ghazali, Jaafar, & Hussien, 2017;Thanok & Parnichkun, 2015). These combinations can be an alternative method to classical optimization methods and reduce the time again (Chopard & Tomassini, 2018).…”

Section: Introductionmentioning

confidence: 99%

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Hazareh,

Ghadiri,

Ranjbar

et al. 2023

Preprint

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Leukemia is a type of blood cancer that affects the bone marrow and lymphatic system. Chemotherapy as a drug treatment method is one of the popular ways of treating this disease to destroy fast-growing cells. In this paper, the finite-time adaptive integral fast terminal sliding mode control (AIFTSMC) as a robust strategic method for treating leukemia cancer based on the chemotherapy process has been introduced. Two different treatment modes called uniform and non-uniform have been investigated in detail. Our goal in this trial is to reduce the number of cancer cells during treatment while minimizing damage to healthy cells. Moreover, the controller's coefficients in the sliding surface have been optimized using the water cycle algorithm, a novel type of metaheuristic algorithm. The simulation results show that AIFTSMC effectively targets cancer cells while minimizing damage to healthy cells. The results promise a novel and practical way to treat leukemia in clinical applications.

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“…Simulation results show the robustness of the control system during the whole motion. In Thanok and Parnichkun, 31 a longitudinal controller for a passenger automotive vehicle is discussed, while particle swarm optimization (PSO) is implemented in order to track the sliding surface and to tune the SMC gains. The presented method proposes a linear control algorithm to control throttle valve angle and brake force based on a simplified first-order model of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and optimized trajectory tracking control of an autonomous tracked vehicle via backstepping and sliding mode control

Sabiha

Kamel

Said

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a complete kinematic and dynamic modeling and trajectory tracking control of an autonomous tracked vehicle. First, based on the vehicle’s kinematics, the reference linear and angular velocities are evaluated. The kinematic controller is proposed as an integrated backstepping controller. Then, based on vehicle dynamics and slipping characteristics, an integral sliding mode control is utilized to get the desired vehicle drive torques and converge its trajectory to the desired one. Whereas the controller gains are optimally calculated. Furthermore, based on the Lyapunov stability, the proof of stability for proposed controllers is presented. Finally, simulation results are conducted to validate the effectiveness of the proposed control algorithm compared with a hybrid backstepping-modified proportional–integral–derivative dynamic controller and a nonlinear feedback acceleration controller.

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Longitudinal control of an intelligent vehicle using particle swarm optimization based sliding mode control

Cited by 12 publications

References 22 publications

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

Optimized Finite-Time Integral Fast Terminal Sliding Mode Control for Leukemia Cancer Treatment

Dynamic modeling and optimized trajectory tracking control of an autonomous tracked vehicle via backstepping and sliding mode control

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