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In the research put forth, a robust adaptive control method for a nonholonomic mobile manipulator robot, with unknown inertia parameters and disturbances, was proposed. First, the description of the robot's dynamics model was developed. Thereafter, a novel adaptive sliding mode control was designed, to which all parameters describing involved uncertainties and disturbances were estimated by the adaptive update technique. The proposed control ensures a relatively good system tracking, with all errors converging to zero. Unlike conventional sliding mode controls, the suggested is able to achieve superb performance, without resulting in any chattering problems, along with an extremely fast system trajectories convergence time to equilibrium. The aforementioned characteristics were attainable upon using an innovative reaching law based on potential functions. Furthermore, the Lyapunov approach was used to design the control law and to conduct a global stability analysis. Finally, experimental results and comparative study collected via a 05-DoF mobile manipulator robot, to track a given trajectory, showing the superior efficiency of the proposed control law. [ABSTRACT FROM AUTHOR]

The number of wheelchair users has increased at an alarming rate throughout the last decade due to the increased number of individuals with upper/lower extremity dysfunctions resulting from a stroke, spinal cord injury, trauma, accident, or workplace injuries. Wheelchair-mounted assistive robots have been playing an important role in assisting the activities of daily living (ADLs) of these individuals. This research focuses on the design optimization of wheelchair-mounted assisted robots for essential ADLs. The genetic algorithm (GA) was used to estimate the link length parameters of 5 degrees of freedom (DoFs), 6DoFs, and 7DoFs wheelchair-mounted robots to cover the seven workspaces commonly used in ADLs. The objective function used in the GA includes singularity avoidance, collision avoidance, and ADL coverage. The results show that the proposed optimized 5DoFs robot can cover 25%, 6 DoFs robot can cover 69%, and 7 DoFs robot can cover 71% of essential ADL workspaces.