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Considering input constraints and parameter uncertainty of the nonholonomic wheeled mobile robots, a new control law has been designed in this paper to solve the tracking and stabilization control problems simultaneously. The asymptotic convergence of the stabilization or tracking errors is achieved by a smooth controller with time-varying feedback parameters subject to set conditions. A geometric-based parameter design strategy is employed to overcome the difficulties resulting from input constraints. With the parameter design strategy, the designed parameters satisfy the set conditions and the control inputs stay in the constrained domain. Experimental studies have been conducted to verify the effectiveness of the proposed control law. [ABSTRACT FROM AUTHOR]

Collision-free navigation of autonomous vehicles by means of convex quadratic programming (CQP) based model predictive control (MPC) is considered in this paper. A new collision-free navigation function is designed for real-time collision avoidance of an autonomous vehicle in both static and dynamic environments. Furthermore, vehicle shape is taken into consideration during trajectory generation as a convex polygonal region defined by linear constraints rather than a single point. Then, the MPC optimization problem with the vehicle shape is solved as a CQP-based MPC scheme in the sense of path planning. Compared to the previous MPC, which can only be reduced to a nonlinear programming problem, the control sequences of CQP-based MPC can be obtained quickly with improved real-time system performance. Simulations in diverse scenarios, including a real vehicle dataset, show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]