A Model-Free Controller for Guaranteed Prescribed Performance Tracking of Both Robot Joint Positions and Velocities

Karayiannidis, Yiannis; Papageorgiou, Dimitrios; Doulgeri, Zoe

doi:10.1109/lra.2016.2516245

Cited by 64 publications

(44 citation statements)

References 11 publications

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“…Moreover, up to now, the FC has been used in some specific fields only. [51][52][53][54][55] In this paper, a novel UIO-based funnel nonsingular terminal SMC (FNTSMC) method is proposed for servomechanisms with unknown dynamics (eg, nonlinear friction, uncertainties, and external disturbances). An auxiliary filtered variable is defined and used to construct a UIO to estimate the lumped unknown dynamics.…”

Section: Introductionmentioning

confidence: 99%

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Wang

Ren

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, an unknown input observer is proposed for servomechanisms with unknown dynamics (eg, nonlinear friction, parameter uncertainties, and external disturbance). An unknown input observer is developed by introducing auxiliary filtered variables in terms of first‐order low‐pass filters, where only one constant (filter parameter) needs to be tuned. A modified funnel variable is developed by using the tracking error to replace the scaling factor to improve the transient and steady‐state performance of the control systems. Then, a sliding mode surface with the new transformed error is defined and used to design a funnel nonsingular terminal sliding mode controller to achieve finite‐time convergence. In addition, the proposed funnel nonsingular terminal sliding mode controller method avoids the singularity problem in the conventional sliding mode control via modifying the terminal sliding manifold. Finally, experimental results are given to illustrate the effectiveness of the suggested control scheme.

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Section: Introductionmentioning

confidence: 99%

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Wang

Ren

et al. 2018

Intl J Robust & Nonlinear

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“…Fig. 3 shows the output performance, control signal and transformed error respectively for the proposed control algorithm using the transformed error in (14) instead of that in (21). In all figures, x 0 denotes the output response of the leader node, u 0 denotes the control signal of the leader node and x i and u i are the output performance and control signal of the associated agent, respectively, for all i = 1, .…”

Section: Agentsmentioning

confidence: 99%

“…Fig. 4 and 5 present the output performance, control signal and transformed error respectively using the proposed control algorithm with the smooth error function, erf (), in the transformed error (21). The advantage of introducing such smooth function is reflected in the good tracking performance and with no oscillation in the behavior of transformed Fig.…”

Section: Agentsmentioning

confidence: 99%

“…Error and transformed error of neuro-adaptive control with PPF of Example 1 using the transformed error in equation(21).…”

mentioning

confidence: 99%

“…Neuro-adaptive control with PPF output performance of Example 2 using the transformed error in equation(21). Error and transformed error of output 1 of example 2 using the transformed error in equation(21). Error and transformed error of output 2 of Example 2 using the transformed error in equation(21).…”

mentioning

confidence: 99%

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Neuro-Adaptive Distributed Control With Prescribed Performance for the Synchronization of Unknown Nonlinear Networked Systems

El-Férik

Hashim

Lewis

2018

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes a neuro-adaptive distributive cooperative tracking control with prescribed performance function (PPF) for highly nonlinear multiagent systems. PPF allows error tracking from a predefined large set to be trapped into a predefined small set. The key idea is to transform the constrained system into unconstrained one through transformation of the output error. Agents' dynamics are assumed to be completely unknown, and the controller is developed for strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness of the transformed error and the adaptive neural network weights. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous networked system with time varying uncertain parameters and external disturbances.

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Prescribed Performance Concurrent Control

2023

Connected Vehicular Systems

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A Model-Free Controller for Guaranteed Prescribed Performance Tracking of Both Robot Joint Positions and Velocities

Cited by 64 publications

References 11 publications

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Neuro-Adaptive Distributed Control With Prescribed Performance for the Synchronization of Unknown Nonlinear Networked Systems

Prescribed Performance Concurrent Control

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