A cable-driven parallel robot (CDPR) possesses significant advantages over common rigid-link mechanisms including: large workspace, low inertia, small size actuators, and high speed motion. However, CDPRs have considerable restrictions consist of: holding tension in all cables, cables axial flexibility, and mechanism low rigidity resulting in mechanism undesired vibrations. In majority of previous studies related to redundant CDPRs control, the cables' flexibility is assumed negligible and redundancy problem is solved regardless of mechanism stiffness. Vibration control of a new flexible redundant kinematically-constrained CDPR with warehousing applications is studied in this research. In proposed methods, for minimization of undesired vibrations, the CDPR redundancy problem is solved based on maximizing its stiffness. Proposed controllers are designed based on linear methods of pole placement and also linear quadratic regulation (LQR) technique. Transforming the designed controllers to adaptive ones has shown performance improvements in case of moving platform mass and inertia uncertainty. Simulations results show robustness and high performance of designed controllers beside practicality of the inputs.

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This work deals with the design and comparison of two adaptive position control schemes with a classical PID controller for fully constrained and redundant planar robots. First, a novel method based on inclusion of virtual cables facilitates the linear separation of the uncertain parameters from the input-output signals. Then, two Lyapunov based adaptive controllers based on the sliding mode and PD schemes are designed to compensate for the structure matrix uncertainties, which result from errors in the anchor point locations. Finally, the adaptive controllers are evaluated and compared with a classical PID controller through simulations for a desired 2D singularity-free pose of the mobile platform. The simulation results have shown that the adaptive PD control scheme has the best performance for both fully constrained and redundant cases.