In this paper, a novel design for gravity compensation of manipulators has been introduced and its effect on the static and dynamic performance has been studied. The gravity compensation mechanism (GCM) of this study deploys a symmetrical configuration for the pulleys and cable to reduce the tension in the cable without requiring additional space for longer springs and their displacements. The reduction of cable tension significantly increases the durability of the proposed GCM, which tackles one of the most critical issues of the cable-pulley based gravity compensating systems. The configuration of the proposed GCM also distributes equal internal forces and moments over the joints of the manipulator. As a key part of the design, the dynamic performance of the manipulator is evaluated before and after it is equipped with the GCM. The dynamic manipulability ellipse is used to measure the variations in dynamic performance. Implementation on a real multi-degree-of-freedom (DOF) manipulator showed the effectiveness of the proposed GCM in gravity compensation and also its simplicity of extension and larger achievable workspace comparing to the previous works.
The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.
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