Fuzzy auto-tuning PID control of multiple joint robot driven by ultrasonic motors

Sun, Zhigang; Xing, Rentao; Zhao, Chunsheng; Huang, Weiqing

doi:10.1016/j.ultras.2007.04.001

Cited by 39 publications

(16 citation statements)

References 10 publications

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“…are satisfied, then the robotic system described by the dynamical model (1) and the direct kinematic model (2) is asymptotically stable under the constrained force model: (12) and the control law:…”

Section: Theoremmentioning

confidence: 99%

Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

Mehdi¹,

Boubaker²

2011

International Journal of Advanced Robotic Systems

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This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov-based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO) algorithm. For designing the PSO method, different index performances are considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.

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Section: Theoremmentioning

confidence: 99%

Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

Mehdi¹,

Boubaker²

2011

International Journal of Advanced Robotic Systems

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“…However, regardless the types of pure fuzzy controller, every controller is essentially a non-linear PD control that actually does not play an integral role in controlling. Sometimes the control process is not smooth, and the steady-state error is also hard to be reduced to a desired level [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Heating Control of Heated Twin Radiosonde Humidity Sensor Based on DMC

Sun

Zhang

et al. 2016

Polish Maritime Research

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“…In [13], a new fuzzy PID was designed to control some known nonlinear systems. The fuzzy controller tuned with particle swarm optimization (PSO) also was researched [14] and a new fuzzy auto-tuning position speed PID control strategy was proposed [15] , which can not only ensure a higher position accuracy but also keep the robot operating smoothly. In order to realize automatic regulating of PID parameters a fuzzy inference method was developed [16] and the applications of the controller in some systems ware studied with Matlab.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy PID Control of Space Manipulator for Both Ground Alignment and Space Applications

Liu

Liang

Gao

2014

Int. J. Autom. Comput.

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Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential (PID) control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control, the control performance of the fuzzy PID is more effective for manipulator trajectory control.

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Fuzzy auto-tuning PID control of multiple joint robot driven by ultrasonic motors

Cited by 39 publications

References 10 publications

Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

Heating Control of Heated Twin Radiosonde Humidity Sensor Based on DMC

Fuzzy PID Control of Space Manipulator for Both Ground Alignment and Space Applications

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