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Novel solutions on model-based and model-free robotic-assisted ankle rehabilitation

Arceo Juan Carlos, Alvarez Jorge, Armenta Carlos, Lauber Jimmy, Cremoux Sylvian, Simoneau-Buessinger Emilie, Bernal Miguel

Archives of Control Sciences

2021

Vol. 31, No. 1

5--27

In this report, ankle rehabilitation routines currently approved by physicians are implemented via novel control algorithms on a recently appeared robotic device known as the motoBOTTE. The physician specifications for gait cycles are translated into robotic trajectories whose tracking is performed twofold depending on the availability of a model: (1) if obtained via the Euler-Lagrange approach along with identification of unknown plant parameters, a new computed-torque control law is proposed; it takes into account the parallel-robot characteristics; (2) if not available, a variation of the active disturbance rejection control technique whose parameters need to be tuned, is employed. A detailed discussion on the advantages and disadvantages of the model-based and model-free results, from the continuous-time simulation to the discrete-time implementation, is included.

Tracking control of an underactuated rigid body with a coupling input force

Korczak S.

Archives of Control Sciences

2014

Vol. 24, no. 3

321--332

This paper presents a set of basic problems concerning the control of an underactuated dynamic system. Exemplary system of a planar rigid body with a coupling input force is described. Lie brackets method is used to show accessibility of the system. A tracking problem is solved with computed torque algorithm. The coupling force makes the convergence to zero of all state variables errors impossible. After numerical simulation, stability of the system is mentioned.

Iterative learning control for robot manipulators

Bouakrif F., Boukhetala D., Boudjema F.

Archives of Control Sciences

2007

Vol. 17, no. 1

57-69

In this paper, we present a time-domain iterative learning control scheme for the trajectory tracking problem of rigid robot manipulators that perform repeated tasks. The proposed control scheme comprises a computed torque control designed exploiting the approximated linear model of a manipulator and a learning law to compensate effects of nonlinear terms, that are ignored in obtaining the linear model, and the external disturbance. We show that the iterative learning controller is capable of effectively canceling the disturbances caused by nonlinear terms and other disturbance. The asymptotic stability of the closed-loop system is guaranteed, and the conditions of this stability are given. Simulation results on PUMA 560 robot show clearly efficiency of the proposed scheme.