Adaptive multi-model sliding mode control of robotic manipulators using soft computing

Sadati, N.; Ghadami, Rasoul

doi:10.1016/j.neucom.2007.06.019

Cited by 66 publications

(36 citation statements)

References 13 publications

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“…Real robot arm model 36 is used for practical condition that described in section 5 and a separate FOPID controller is used for each individual link of the robot arm. Indeed, both links of robot arm are controlled in parallel with the aim of tracking the input desired trajectory.…”

Section: Proposed Methodsmentioning

confidence: 99%

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

Fani¹,

Shahraki²

2016

Biosci., Biotech. Res. Asia

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Fractional order (FO) controllers are highly considered with regard to higher performance and robustness of these controllers in FO systems. According to advantages of PID controllers such as suitable performance, low price and simplicity of design, they are widely used in industry. A FOPID controller is used for two-link robot control in this paper. Considering vast use of evolutionary algorithms and numerical optimization, coefficients of the FO controller are optimized using evolutionary algorithms in this paper. An individual FOPID controller is applied in order to control each link. Three evolutionary optimization algorithms including particle swarm optimization (PSO), genetic algorithm and estimation of distribution algorithm, are compared from optimal coefficients determination point of view. Experimental results indicate that FOPID controller is more applicable according to use of actual model for robot and suitable performance of the PSO algorithm. Key words: Evolutionary algorithms, fractional order controller, PID controller, two-link robotAlong with introduction of fractional order (FO) controllers and their higher performance and robustness on FO systems by Podlubny in 1994, FO systems and their control process are significantly considered 1 . Although PID controllers are introduced long time ago, they are widely used in industry because of their advantages such as low price, design simplicity and suitable performance 2, 3 . While three parameters of design including proportional (K p ), integral (K i ), and derivative (K d ) are available in PID controllers, two more parameters exist in FOPID controllers for adjustment. These parameters are integral fractional order and derivative fractional order 4 . In comparison with PID controllers, FOPID controllers have more flexible design that result in more precise adjustment of closed-loop system 5 . FOPID controllers are defined by FO differential equations. It is possible to tune frequency response of the control system by expanding integral and derivative terms of the PID controller to fractional order case. This characteristic result in a more robust design of control system, but it is not easily possible 6 .According to non-linearity, uncertainty, and confusion behaviors of robot arms, they are highly recommended for experimenting designs of control systems. Despite non-linear behavior of robot arm, it is demonstrable that a linear proportional derivative controller can stabilize the system using Lyapanov 7 . But, classic PD controller itself cannot control robot to reach suitable condition. Several papers and wide researches in optimizing performance of the robot manipulator show the importance of this issue. Different adaptive and robust procedures are proposed for robot control that all of them are so complicated in analysis and design [8][9][10][11] . Approaches that have been proposed for robot control are categorized in non-

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Section: Proposed Methodsmentioning

confidence: 99%

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

Fani¹,

Shahraki²

2016

Biosci., Biotech. Res. Asia

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“…Reference [25] describes the process of improvement in detail. However, the constant switching gains may increase the excitation of the system's high frequency unmodeled dynamics [26][27][28], which will cause undesirable phenomena to occur. Furthermore, before designing the control law, the boundaries of the disturbance term needs to be estimated.…”

Section: Contact Force Distribution Algorithmmentioning

confidence: 99%

Dynamic Stability Analysis of a Trotting Quadruped Robot Based on Switching Control

Lin

Wang

Rao

et al. 2015

Int. j. adv. robot. syst.

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This paper presents a dynamic stability analysis method for a trotting quadruped robot on unknown rough terrains, which is based on the Lyapunov theory of a switching system. Firstly, the dynamical model of a trotting quadruped robot is built as a nonlinear switching system. In the stance phase, the dynamical model of a body and two stance legs is approximated as a compound model including a seven-link mechanism and a linear inverted pendulum. Furthermore, as a result of the switching process, the trotting quadruped robot becomes a non-autonomous system. Secondly, a contact force distribution/control strategy is proposed, based on adaptive sliding mode to guarantee the position and orientation of the seven-link mechanism asymptotically stable in the stance phase. With the proposed strategy, a common Lyapunov function is designed in order to validate the uniform asymptotic stability of the body's height and orientation variations. Then, the landing positions of the swing legs are calculated, based on the linear inverted pendulum model in order to make the horizontal position error of the robot converge to a bounded region. Finally, quadruped trotting experiments are performed in order to validate the effectiveness of the proposed methods.

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“…Fuzzy approach as an intelligent control approach has more flexibility and causes controllers' coefficients to be adjusted based on desires of designer in a way that controller which has low estimation error has more influence on total control signal. In prior weighting method, like what is in [3], excessive gains for corresponding controllers result in having high gain for total control signal. This issue can eventuate in probability of instability.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Switching for Multiple Model Adaptive Control in Manipulator Robot

Kharabian¹,

Bolandi²,

Smaeilzadeh³

et al. 2017

JAMRIS

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Adaptive multi-model sliding mode control of robotic manipulators using soft computing

Cited by 66 publications

References 13 publications

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

Dynamic Stability Analysis of a Trotting Quadruped Robot Based on Switching Control

Fuzzy Switching for Multiple Model Adaptive Control in Manipulator Robot

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