Adaptive control techniques for friction compensation

Feemster, M.; Vedagarbha, P.; Dawson, D.M.; Haste, D.

doi:10.1109/acc.1998.707076

Cited by 25 publications

(21 citation statements)

References 18 publications

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“…Friction, which is ubiquitous in the joints of the master and slave robots, is an example of NLP terms [12]. Indeed, at the i th joint of a robot, the friction force can be modeled as [13] designed an adaptive controller to compensate for uncertainties in the parameters appearing nonlinearly in the friction model. However, the result in [13] can only be applied to setpoint regulation.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, at the i th joint of a robot, the friction force can be modeled as [13] designed an adaptive controller to compensate for uncertainties in the parameters appearing nonlinearly in the friction model. However, the result in [13] can only be applied to setpoint regulation. For trajectory tracking, Hung et al [14] developed an adaptive controller to compensate for NLP uncertainties in robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive control for linearly and nonlinearly parameterized dynamic uncertainties in bilateral teleoperation systems

Liu

Tavakoli

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-Existing work concerning adaptive control of uncertain teleoperation systems can only deal with linearly parameterized (LP) dynamic uncertainties. Typical teleoperation system dynamics, however, posses terms with nonlinearly parameterized (NLP) structures. Stribeck friction is an example of NLP terms in robot dynamics. If not compensated for in the control scheme, uncertainties in the NLP dynamic terms may lead to significant tracking errors. In this paper, for a bilateral teleoperation system, adaptive controllers are designed for the master and slave robots with both LP and NLP dynamic uncertainties. Next, these controllers are incorporated into the 4-channel bilateral teleoperation framework. Then, transparency of the overall teleoperation is studied via a Lyapunov function analysis. A simulation study demonstrates the effectiveness of the proposed adaptive scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive control for linearly and nonlinearly parameterized dynamic uncertainties in bilateral teleoperation systems

Liu

Tavakoli

2011

2011 IEEE International Conference on Robotics and Automation

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“…Consider that the nonlinear teleoperation system (4)-(5) has both LP and NLP dynamic uncertainties and is controlled by the adaptive control laws (19)-(20) using the LP dynamic adaptation laws (21) and the NLP dynamic adaptation laws (22). Also, assume the following conditions holds:…”

Section: Theoremmentioning

confidence: 99%

“…However, the result in [22] can only be applied to setpoint regulation. For trajectory tracking, Hung et al [23] developed an adaptive controller to compensate for NLP uncertainties in robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Control of Teleoperation Systems With Linearly and Nonlinearly Parameterized Dynamic Uncertainties

Liu

Tavakoli

2012

Journal of Dynamic Systems, Measurement, and Control

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Existing work concerning adaptive control of uncertain teleoperation systems only deals with linearly parameterized (LP) dynamic uncertainties. Typical teleoperation system dynamics, however, also posses terms with nonlinearly parameterized (NLP) structures. An example of such terms is friction, which is ubiquitous in the joints of the master and slave robots of practical teleoperation systems. Uncertainties in the NLP dynamic terms may lead to significant position and force tracking errors if not compensated for in the control scheme. In this paper, adaptive controllers are designed for the master and slave robots with both LP and NLP dynamic uncertainties. Next, these controllers are incorporated into the 4-channel bilateral teleoperation control framework to achieve transparency. Then, transparency of the overall teleoperation is studied via a Lyapunov function analysis. Simulation studies demonstrate the effectiveness of the proposed adaptive scheme when exact knowledge of the LP and NLP dynamics is unavailable.

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Actuator stiction compensation via model predictive control for nonlinear processes

Durand

Christofides

2016

AIChE Journal

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The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC-based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed-loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov-based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set-points that the valve output can reach and causes the operating constraints to be met.

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Adaptive control techniques for friction compensation

Cited by 25 publications

References 18 publications

Adaptive control for linearly and nonlinearly parameterized dynamic uncertainties in bilateral teleoperation systems

Adaptive control for linearly and nonlinearly parameterized dynamic uncertainties in bilateral teleoperation systems

Adaptive Control of Teleoperation Systems With Linearly and Nonlinearly Parameterized Dynamic Uncertainties

Actuator stiction compensation via model predictive control for nonlinear processes

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