Serial and Cable-Driven Parallel Robots (CDPRs) are two types of robots that are widely used in industrial applications. Usually, the former offers high position accuracy at the cost of high motion inertia and small workspace envelope. The latter has a large workspace, low motion inertia, and high motion accelerations, but low accuracy. In this thesis, redundant Hybrid Cable-Driven Robots (HCDRs) are proposed to harness the strengths and benefits of serial and CDPRs. Although the study has been directed at warehousing applications, the developed techniques are general and can be applied to other applications.

The main goal of this research is to develop integrated control systems to reduce vibrations and improve the position accuracy of HCDRs. For the proposed HCDRs, the research includes system modeling, redundancy resolution, optimization problem formulation, integrated control system development, and simulation and experimental validation.

In this thesis, first, a generalized HCDR is proposed for the step-by-step derivation of a generic model, and it can be easily extended to any HCDRs. Then, based on an in-plane configuration, three types of control architecture are proposed to reduce vibrations and improve the position accuracy of HCDR. Their performance is evaluated using several well-designed case studies. Furthermore, a stiffness optimization algorithm is developed to overcome the limitations of existing approaches.

Decoupled system modeling is studied to reduce the complexity of HCDRs. Control design, simulations, and experiments are developed to validate the models and control strategies. Additionally, state estimation algorithms are proposed to overcome the inaccurate limitation of Inertial Measurement Unit (IMU). Based on these state observers, experiments are conducted in different cases to evaluate the control performance.

An Underactuated Mobile Manipulator (UMM) is proposed to address the tracking and vibration- and balance-control problems. Out-of-plane system modeling, disturbance analysis, and model validation are also investigated. Besides, a simple but effective strategy is developed to solve the equilibrium point and balancing problem. Based on the dynamic model, two control architectures are proposed. Compared to other Model Predictive Control (MPC)-based control strategies, the proposed controllers require less effort to implement in practice. Simulations and experiments are also conducted to evaluate the model and control performance.

Finally, redundancy resolution and disturbance rejection via torque optimization in HCDRs are proposed:​ joint-space Torque Optimization for Actuated Joints (TOAJ) and joint-space Torque Optimization for Actuated and Unactuated Joints (TOAUJ). Compared to TOAJ, TOAUJ can solve the redundancy resolution problem as well as disturbance rejection. The algorithms are evaluated using a Three-Dimensional (3D) coupled HCDR and can also be extended to other HCDRs.