Constrained robust model predictive control for time-delay descriptor systems with linear fractional uncertainty

This paper addresses the robust model predictive control (MPC) for a class of time delay descriptor systems with linear fractional uncertainty and input constrains. The systems are transferred to the piecewise continuous descriptor systems and a piecewise constant control sequence is calculated by minimizing the worst-case quadratic objective function. At each sampling internal, by means of Lyapunov theory and optimization theory, the optimal problem with infinite horizon objective function is reduced to a convex optimization problem involving linear matrix inequalities. The sufficient conditions for the existence of the state feedback control are derived and expressed as linear matrix inequalities. Further, an iterative model predictive control algorithm is proposed for the on-line synthesis of state feedback controllers with the conditions guaranteeing that the closed-loop descriptor systems are regular, impulse-free and robust stable. Finally, a numerical example is presented to show the efficiency of the proposed approach

MPC for Robot Manipulators with Integral Sliding Modes Generation

This paper deals with the design of a robust hierarchical multiloop 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 multi-input-multi-output (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 unmodeled 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 fulfiling 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 MPC optimization problem relying on a set of linearized decoupled single-input-single-output (SISO) systems that 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