MPC for Robot Manipulators With Integral Sliding Modes Generation

Abstract

Abstract-This paper deals with the design of a robust hierarchical multi-loop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic MIMO system and the combination of a Model Predictive Control (MPC) module with an Integral Sliding Mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodelled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfilling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the model predictive control optimization problem relying on a set of linearized decoupled SISO systems which are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data.Index Terms-Model predictive control, integral sliding mode, robot manipulators, uncertain systems.

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“…By properly integrating the parametric estimation error information into the designed identification scheme, high comprehensive performance has been obtained. In [8], a new model predictive control (MPC) was proposed using integral sliding mode (ISM) dynamics. Thanks to the effective combination of MPC and ISM schemes, satisfactory comprehensive control performance has been obtained with the proposed control scheme.…”

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