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This paper addresses the robust explicit model predictive control scheme for linear systems with input and output constraint in the presence of disturbances and noise. Conditions for disturbance rejection are established by incorporating a full state/disturbance observer. The separation principle is applied to design an optimal observer in the unconstrained problem. Then, an efficient algorithm is developed to explicitly design observer gains by minimizing a quadratic performance criterion. It is shown that the solution includes a set of regions with piecewise affine functions of the state and reference vectors and a set of regions with optimal observers. In the proposed method, two sets of partitions associated with the control law and the observer gains are obtained. Therefore, the online computation includes finding the active regions of both observer and control law partitions in which the current state is located. The proposed technique is particularly attractive for a wide range of practical problems where the exact model of the actual system is not available.

In this paper, new hybrid robots are suggested which divided the task into a position and orientation tasks. The position mechanism controls the position whereas the orientation one manipulates the orientation of the end effector. These robots consist of a translational parallel manipulator and a rotational serial or parallel mechanism. The 3UPU or Tricept parallel manipulator and a three-axis gimbaled system or parallel shoulder manipulator are chosen for translational and rotational movements, respectively. The main goal of this paper is analyzing the development and combination of serial and parallel manipulators in order to increase their features. According to this purpose, serial and parallel mechanisms with three DOF are combined in a way to encompass six DOF space. It is shown hybrid mechanisms with less coupling between their subsystems are capable of increasing robot characteristics.