Multiobjective Optimization of an Observer-Based Controller: Theory and Experiments on an Underwater Grinding Robot

Hamelin, Philippe; Bigras, Pascal; Beaudry, Julien; Richard, Pierre-Luc; Blain, Michel

doi:10.1109/tcst.2013.2296355

Cited by 9 publications

(13 citation statements)

References 19 publications

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“…The general principle of DO has been presented in refs. [25][26][27]. Another approach to compensate disturbances is active disturbance rejection control.…”

Section: Need For Dsomentioning

confidence: 99%

A Two-Stage State Feedback Controller Supported by Disturbance-State Observer for Vibration Control of a Flexible-Joint Robot

et al. 2019

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SUMMARYJoint flexibility introduces additional degrees of freedom and vibration modes, thus limiting the performance of the manipulator. To improve control bandwidth, this paper proposes an enhanced two-stage state feedback (SFB) controller, which combines two parts. The first is a SFB loop, which considers the motor position as a virtual control input for the link side dynamics. The second is a disturbance-state observer, which compensates disturbances and reconstructs indirect measurements. Experimental results show the effectiveness of the proposed controller in terms of position tracking, link vibration, and rejection of the kinematic error from the joint’s harmonic drive reducer.

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“…The general principle of DO has been presented in refs. [25][26][27]. Another approach to compensate disturbances is active disturbance rejection control.…”

Section: Need For Dsomentioning

confidence: 99%

A Two-Stage State Feedback Controller Supported by Disturbance-State Observer for Vibration Control of a Flexible-Joint Robot

et al. 2019

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“…and depicted as in Figure 3. (4) and depicted as in Figure 3. Thus, the following property is satisfied:…”

Section: Dead Zone Non-linearity Descriptionmentioning

confidence: 99%

“…Where u is the ideal control signal, u c and u d are unknown vectors and represent the input and the output of the dead zone, respectively [10]. (4) and depicted as in Figure 3. Thus, the following property is satisfied:…”

Section: An Adaptive Robust Controller Using Smcs and Rbfnnsmentioning

confidence: 99%

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A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

et al. 2019

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The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.

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“…A new methodology based on a multi-objective genetic algorithm was proposed [12]. An algorithm designed for high-accuracy local dynamic identification was presented [10].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy PID control method of deburring industrial robots

Tao

Zheng

Lin

et al. 2015

IFS

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Abstract. This paper proposes a fuzzy PID control method for deburring industrial robots. The adaptive fuzzy PID controller relates to the trajectory and joint angular parameters of the end-effector on a robot. The PID controller parameters update online at each sampling time to guarantee trajectory accuracy of the end-effector. The simulation of the fuzzy PID control is provided based on the 6-DOF deburring industrial robot. Experimental results demonstrate the efficiency of the fuzzy PID control method.

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Multiobjective Optimization of an Observer-Based Controller: Theory and Experiments on an Underwater Grinding Robot

Cited by 9 publications

References 19 publications

A Two-Stage State Feedback Controller Supported by Disturbance-State Observer for Vibration Control of a Flexible-Joint Robot

A Two-Stage State Feedback Controller Supported by Disturbance-State Observer for Vibration Control of a Flexible-Joint Robot

A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

Fuzzy PID control method of deburring industrial robots

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