Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

Loucif, Fatiha; Kechida, Sihem

doi:10.1515/comp-2020-0144

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the discontinuous nature of the SMC law can lead to a chattering effect, characterized by high-frequency oscillations of the controlled variable when the system state reaches the sliding surface. This chattering effect can have detrimental consequences on actuator control and excite unwanted unmodeled dynamics 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Azeez,

Abdelhaleem,

Elnaggar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In the control of a nonlinear 2-DOF system using a sliding mode controller (SMC) with a PID surface, various optimization techniques have been explored. These techniques include the Antlion Optimization Algorithm (ALO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), and Whale Optimizer Algorithm (WOA) 14 .…”

Section: Introductionmentioning

confidence: 99%

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Azeez,

Abdelhaleem,

Elnaggar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

Control Options—MIMO

Raghavendra

2023

Springer Tracts in Mechanical Engineering

View full text Add to dashboard Cite

Enhancing robustness and noise rejection in flexible joint manipulators: an optimized sliding mode controller with enhanced gray wolf optimization for trajectory tracking

Azeez,

Elnaggar,

Abdelhaleem

et al. 2023

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

The objective of this study is to enhance the performance of a nonlinear three-rigid-link manipulator (RLM) with a focus on trajectory tracking, robustness against disturbances and noises, and adaptability to joint flexibility. To achieve this, we have employed an optimized sliding mode controller with a proportional integral derivative (PID) sliding manifold. The tuning process involves selecting the critical gains of the controller that minimizes the integral time absolute error (ITAE), serving as the objective function (OBJF) to optimize the performance of the robot manipulator. To identify the optimal gains of the controller, we have utilized a new optimization algorithm known as memory enhanced linear population size reduction gray wolf optimization (MELGWO). The efficacy of this algorithm is compared to other existing optimization methods in the literature. Moreover, this research has delved into the impact of joint flexibility on the robot system’s performance. Encouragingly, the results demonstrate that the optimized SMC–PID with MELGWO adaptation can effectively address joint flexibility while maintaining acceptable performance levels.

show abstract

Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

Cited by 5 publications

References 29 publications

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Control Options—MIMO

Enhancing robustness and noise rejection in flexible joint manipulators: an optimized sliding mode controller with enhanced gray wolf optimization for trajectory tracking

Contact Info

Product

Resources

About