Particle swarm optimization–based adaptive super-twisting sliding mode control design for 2-degree-of-freedom helicopter

Pereira

et al. 2021

IRASE

Ball and Beam system is one of the most popular and important laboratory models for teaching control systems. This paper proposes a new control strategy to the position control for the ball and beam system. Firstly, a nonlinear controller is proposed based on the backstepping approach. Secondly, in order to adapt online the dynamic control law, adaptive laws are developed to estimate the uncertain parameters. The stability of the proposed adaptive backstepping controller is proved based on the Lyapunov theorem. Simulated results are presented to illustrate the performance of the proposed approach.

Section: Adaptive Backstepping Control Designmentioning

confidence: 99%

“…Now, we try to design the backstepping controller to the system in (35)(36)(37)(38) with uncertainty, following a procedure that is similar to backstepping without uncertainty. First, we present the state equations including the uncertain terms…”

Section: Adaptive Backstepping Control Designmentioning

confidence: 99%

Adaptive backstepping control design for ball and beam system

Al-Dujaili

Pereira

et al. 2021

IRASE

“…This old technique could not find the optimal dynamic performance of the controlled system based on the proposed controllers. Therefore, a modern optimization technique is used to tune these design parameters to improve the dynamic performance of the controlled system [ 28 , 29 ]. In the present work, the Particle Swarm Optimization (PSO) has been suggested to adjust the design parameters of the controlled system.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Ibraheem

Azar

et al. 2020

Entropy

Self Cite

This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

“…9 To avoid these restrictions, a natural solution is the application of the SMC with higher-order control structure. The super-twisting sliding mode control (STSMC) is a second-order control structure and characterized by the following advantages 10 –13 :…”

Section: Introductionmentioning

confidence: 99%

Optimal super-twisting sliding mode control design of robot manipulator: Design and comparison study

Al-Dujaili

Falah

International Journal of Advanced Robotic Systems

et al. 2020

Self Cite

This article presents a tracking control design for two-link robot manipulators. To achieve robust tracking control performance, a super-twisting sliding mode control (STSMC) is derived. The stability of the system based on the proposed approach is proved based on the Lyapunov theorem. However, one problem with the designed STSMC is to properly set its parameters during the design. Therefore, it is proposed a social spider optimization (SSO) to tune these design parameters to improve the dynamic performance of the robot manipulator controlled considering STSMC. The performance of the STSMC approach based on SSO is compared to that based on particle swarming optimization (PSO) in terms of dynamic performance and robustness characteristics. The effectiveness of the proposed optimal controllers is verified by simulations within the MATLAB software. It is verified that the performance given by SSO-based STSMC outperforms that resulting from PSO-based STSMC. The experimental results are conducted based on LabVIEW 2019 software to validate the numerical simulation.