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Research Papers

Multi-Objective Design of Optimal Sliding Mode Control for Trajectory Tracking of SCARA Robot Based on Genetic Algorithm

[+] Author and Article Information
Wafa Boukadida

National Engineering School of Monastir,
University of Monastir,
Monastir 5000, Tunisia
e-mail: wafaboukadida91@gmail.com

Anouar Benamor

National Engineering School of Monastir,
University of Monastir,
Monastir 5000, Tunisia
e-mail: benamor_anouar@yahoo.com

Hassani Messaoud

National Engineering School of Monastir,
University of Monastir,
Monastir 5000, Tunisia
e-mail: Hassani.Messaoud@enim.rnu.tn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received April 23, 2018; final manuscript received October 22, 2018; published online November 22, 2018. Assoc. Editor: Heikki Handroos.

J. Dyn. Sys., Meas., Control 141(3), 031015 (Nov 22, 2018) (11 pages) Paper No: DS-18-1203; doi: 10.1115/1.4041852 History: Received April 23, 2018; Revised October 22, 2018

This paper focuses on robust optimal sliding mode control (SMC) law for uncertain discrete robotic systems, which are known by their highly nonlinearities, unmodeled dynamics, and uncertainties. The main results of this paper are divided into three phases. In the first phase, in order to design an optimal control law, based on the linear quadratic regulator (LQR), the robotic system is described as a linear time-varying (LTV) model. In the second phase, as the performances of the SMC greatly depend on the choice of the sliding surface, a novel method based on the resolution of a Sylvester equation is proposed. The compensation of both disturbances and uncertainties is ensured by the integral sliding mode control. Finally, to solve the problem accompanying the LQR synthesis, genetic algorithm (GA) is used as an offline tool to search the two weighting matrices. The main contribution of this paper is to consider a multi-objective optimization problem, which aims to minimize not only the chattering phenomenon but also other control performances. A novel dynamically aggregated objective function is proposed in such a way that the designer is provided, once the optimization is achieved, by a set of nondominated solutions and then he selects the most preferable alternative. To show the performance of the new controller, a selective compliance assembly robot arm robot (SCARA) is considered. The results show that the manipulator tracing performance is considerably improved with the proposed control scheme.

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References

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Figures

Grahic Jump Location
Fig. 1

The procedure of GA

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Fig. 2

Schematic diagram of the SCARA robot

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Fig. 4

Position tracking q1 in simulation 1

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Fig. 5

Position tracking q2 in simulation 1

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Fig. 6

The tracking error e1

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Fig. 7

The tracking error e2

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Fig. 8

The control input u1

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Fig. 9

The control input u2

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Fig. 10

Desired and actual tracking trajectories in simulation 2

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Fig. 11

Position tracking q2 in simulation 2

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Fig. 12

Position tracking q1 in simulation 2

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Fig. 13

The tracking error e1

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Fig. 14

The tracking error e2

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Fig. 15

The control input u1

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Fig. 16

The control input u2

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