Improved computed torque control for industrial robots

Uebel, M.; Minis, Ioannis; Cleary, K.

doi:10.1109/robot.1992.220238

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…CTC requires the perfect model of the robotic manipulator to linearize [10] or decouple the system dynamics so that each joint can be controlled individually using well-recognized control strategies. It seems that CTC control is sometimes applied, unless that in the scheme proposed by [14], without a feedback to compensate model errors; but a perfect model including all friction effects is never possible in practice, therefore all CTC methods have a closed loop correction, not only the one proposed in [14] and [15].…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Based Computed Torque Control of 2-link Robotic Arm

Anwaar¹,

Yin²,

Ijaz³

et al. 2018

Adv. Sci. Technol. Res. J.

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The paper proposes the application of fractional order controller in position tracking control of 2-link nonlinear robotic arm. The nonlinear system dynamics is linearized using inverse dynamics of the model and fractional order PID controller is designed to deal with remaining tracking errors. The optimal values of controller parameters are calculated using Nelder-Mead optimization technique based on desired design criteria. The objective function is designed using weighted sum approach on each performance specification based on transient domain parameters. It can be seen from simulation results that fractional order controller together with computed torque controller improved tracking performance of proposed system as compared to PID controller used in the outer loop. Moreover, the robustness of proposed scheme is checked by applying the disturbance signal at control input channels of 2-link nonlinear robotic arm links.

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Section: Introductionmentioning

confidence: 99%

Fractional Order Based Computed Torque Control of 2-link Robotic Arm

Anwaar¹,

Yin²,

Ijaz³

et al. 2018

Adv. Sci. Technol. Res. J.

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“…The tracking control of robot manipulators is one of the most challenging problems for control engineers. Accurate robot dynamic model is required to realize high accuracy-tracking control with model-based controllers [3][4][5]. The calculations of nonlinear robot models are complex to compute correctly, and even impossible if precise models of the robot manipulators are unknown.…”

Section: Introductionmentioning

confidence: 99%

Control of Robot Manipulators Using Time-Delay Estimation and Fuzzy Logic Systems

Bae¹,

Jin²,

Suh³

et al. 2017

Journal of Electrical Engineering and Technology

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-A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMAtype robot manipulator.

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“…An accurate dynamic model of a robot manipulator is useful in many ways: for the design of motion control systems, analysis of mechanical design, simulation of manipulator motion, etc. Many control alorithms, such as computed torque control [1], predictive control [2] and sliding mode control [3] normally require an accurate model of the manipulator dynamics, commonly in the form:…”

Section: Introductionmentioning

confidence: 99%

Development of a dynamics model for the Baxter robot

Smith

Yang

et al. 2016

2016 IEEE International Conference on Mechatronics and Automation

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The dynamic model of a robot is important to find the relation between the joint actuator torques and the resulting motion. There are two common methods to do this: The Lagrange formulation, which gives a closed form of the dynamic equations, and the Newton-Euler method, which uses a recursive form. Presented in this paper is a formulation of the Lagrange-Euler (L-E) equations representing the dynamics of the Baxter manipulator. These equations are then verified against torque trajectories recorded from the Baxter robot. Experimental studies show that torques generated using the L-E method closely match recorded actuator torques. All of Baxter's kinematic and dynamic parameters are presented here for easy future reference, and the full symbolic dynamics are made available online for closed loop analysis by the community.

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Improved computed torque control for industrial robots

Cited by 20 publications

References 17 publications

Fractional Order Based Computed Torque Control of 2-link Robotic Arm

Fractional Order Based Computed Torque Control of 2-link Robotic Arm

Control of Robot Manipulators Using Time-Delay Estimation and Fuzzy Logic Systems

Development of a dynamics model for the Baxter robot

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