In this paper, two methods to reduce the complexity of multi-parametric programming model predictive control are proposed. We show that the standard multi-parametric programming problem can be modified by approximating the quadratic programming constraints. For a certain control sequence, only constraints on the first element is considered, while constraints on future elements are ignored or approximated to a simple saturation function. Both the number of critical regions and the computation time are proven to be reduced. Geometric interpretations is given and complexity analysis is conducted. The result is tested on an illustrating example to show its effectiveness.978-1-4799-7862-5/15/$31.00

A novel robust terminal sliding mode control is studied for robotic manipulators with actuator dynamics. It is seen that the developed scheme can ensure a fi nite-time error convergence and the stability of the closed-loop system in the presence of uncertain parameters in both the manipulator and actuator dynamics. The detailed stability analysis is presented to lay a foundation for the readers' understanding of generic theoretical aspects and safe operation for real systems. An illustrative example is bench-tested to validate the effectiveness of the proposed algorithm.

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