A new time-varying feedback RISE control for second-order nonlinear MIMO systems: theory and experiments

Saied, Hussein; Chemori, Ahmed; Bouri, Mohamed; Rafei, Maher El; Francis, Clovis; Pierrot-Deseilligny, Marc

doi:10.1080/00207179.2019.1704063

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…This control law has been implemented satisfactorily in PKMs, as was demonstrated in [16]. Some modifications have been made to the original RISE control to improve its qualities, e.g., in [17], a RISE control with nonlinear gains was proposed to regulate the position of a DPR. Moreover, RISE control is suitable to be combined with model-based terms to enhance the overall system performance, as was demonstrated in [18], where a RISE controller with computed feedforward was proposed to regulate the trajectory tracking of a PKM designed for machining operations.…”

Section: State Of the Artmentioning

confidence: 97%

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Escorcia-Hernández

Aguilar-Sierra

Aguilar-Mejía

et al. 2020

J Intell Robot Syst

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In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

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Section: State Of the Artmentioning

confidence: 97%

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Escorcia-Hernández

Aguilar-Sierra

Aguilar-Mejía

et al. 2020

J Intell Robot Syst

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“…Nonlinear PID (NPID) control is an improvement of the classical linear PID controller, used in robotics for its robust and reactive behaviour with a favorable damping. 30,31,32,33 With this controller, the system response can be improved in terms of three performance indices, including (i) the stability, (ii) the precision and (iii) the rapidity. This can be achieved thanks to the three control actions of the controller.…”

Section: Nonlinear Pid Regulation Controllermentioning

confidence: 99%

A New Hybrid Kinematic/Dynamic Whole-Body Control for Humanoid Robots with Real-Time Experiments

Galdeano¹,

Chemori²,

Krut³

et al. 2021

Int. J. Human. Robot.

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In this paper, a new hybrid kinematic/dynamic control scheme for humanoid robots is proposed. Its basic idea lies in the tracking of several values in both operational and joint spaces. These values include (i) the relative pose of the robot’s feet, (ii) the position of the center of mass, (iii) the body’s orientation, and (iv) the admissible range of variation of the joints. A zero-moment point (ZMP)-based dynamic feedback is included in the proposed scheme to improve the stability of dynamic motions. The proposed stabilizer is based on a spherical projection of a nonlinear PID regulation control law. Through the proposed study, it is shown that these objectives allow us to produce smooth dynamically stable whole-body motions. The effectiveness and robustness of the proposed control scheme are demonstrated through four real-time experimental scenarios conducted on the HOAP-3 humanoid robot.

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“…These gains are adjusted according to a criterion based on the values of the joint tracking errors. It is worth mentioning that, the main difference between this study and the contribution of [20] lies in nature of the control schemes. In [20] the control solution uses nonlinear time-varying gains, whose variation is based on predefined fixed nonlinear functions.…”

mentioning

confidence: 92%

“…The proposed adaptive feedforward term is formulated in a regressor matrix form to estimate the unknown dynamic parameters of a three-DOF delta PKM online. Saied et al designed and validated a RISE control with nonlinear feedback gains for PKMs [20], demonstrating that the tracking error could be significantly reduced with the incorporation of time-varying gains in the control loop. In addition, in [21] RISE feedback control was complemented with neural networks where a B-spline neural network was employed to estimate the dynamics of a PKM online to incorporate it as a feedforward compensation term in the control law.…”

mentioning

confidence: 99%

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

Escorcia-Hernández

Chemori

Aguilar-Sierra

2023

IEEE Trans. Contr. Syst. Technol.

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The development of high-precision tasks, such as machining, needs a positioning device for the cutting tool with the smallest possible error. Multiple design factors need to be considered to ensure a mechatronic device successfully performs such tasks. One of these factors may be attributed to the control scheme, which is responsible for controlling the position of the machine. In view of the importance of designing a good control scheme for a robotic system, in this paper, we propose a new extension of the robust integral sign of the error (RISE) for the positioning device a parallel kinematic machine (PKM). This extension consists in including a nominal feedforward term based on the inverse dynamic model of the robot and replacing the RISE fixed feedback gains with adaptive ones. The RISE part of the proposed controller ensures semi-global asymptotic stability. Moreover, it can accommodate sufficiently smooth bounded disturbances. The feedforward part cancels the nonlinearities of the system, improving the tracking performance of the controller. The adaptive feedback gains produce corrective actions when an increase in the tracking errors is due to the contact forces that occur during the machining process. A Lyapunov-based stability analysis is conducted to prove the semi-global asymptotic stability of the proposed control solution. To show its effectiveness realtime experiments are performed for two case studies; the first one is on a free motion trajectory, and the second on milling experiments under three different forward speeds on SPIDER4, a redundantly actuated PKM.

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A new time-varying feedback RISE control for second-order nonlinear MIMO systems: theory and experiments

Cited by 18 publications

References 25 publications

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

A New Hybrid Kinematic/Dynamic Whole-Body Control for Humanoid Robots with Real-Time Experiments

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

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