A conservativeness-reduced design of a gain scheduled output feedback H/sub /spl infin// controller for an n-joint rigid robotic manipulator, which integrates the varying-parameter rate without their feedback, is proposed. The robotic system is reduced to be a linear parameter varying (LPV) form, which depends affinely on the varying-parameter. The design of a controller, which satisfies the closed-loop H/sub /spl infin// performance, is reduced to be a solution of the parameterized linear matrix inequalities (LMIs) of parameter matrices. With a use of the concept of "multi-convexity", the solution of the infinite LMIs in the varying-parameter and its rate space is reduced to be a solution of the finite LMIs for the vertex set. The proposed controller eliminates the feedback of the varying-parameter rate and prescribes its upper boundary so that the conservativeness of the controller design is reduced. The experiment results verify the effectiveness of the proposed design.

A new approach to the design of a fuzzy continuous gain scheduled H/sub /spl infin// controller based on Taylor series fitting for n-joint rigid robotic manipulators, which guarantees satisfactory dynamic characteristics in the whole movement range, is presented. This scheme combines the gain scheduled H/sub /spl infin// theory with LMI approach to design a continuous gain scheduled H/sub /spl infin// controller, which is applicable to systems with fast state variations so that the deficiency of the conventional gain scheduled controllers is overcome, with the use of Taylor series fitting. Fuzzy control is then incorporated so chat the designed controller possesses the characteristics of a fast response for large errors and a well-damped response for small errors. The system thus always has a good dynamic performance along with the variations of the system states. Simulations and experiments demonstrate the effectiveness of the designed controller.

Concerns the time optimal motion planning problem under kinematic and dynamic constraints for a 2-DOF wheeled mobile robot (WMR). The dynamic model of a WMR is derived using a Newton-Euler method and its constraints are analyzed. Kinematic constraints are imposed by its nonholonomy and structural limits while dynamic constraints are due to motor saturation. The motion planning problem is formulated as two stage planning. First, path planning under kinematic constraints is transformed into a pure geometric problem. The shortest path composed of circular arcs and straight lines is obtained. Then, combined with dynamic characteristics of the WMR, a time optimal velocity profile is generated under dynamic constraints. Since constraints of a WMR are fully exploited, the proposed method is simple and effective for motion planning. Simulation results illustrate the capability of the planning scheme.

Concerns trajectory tracking control of mobile robots. In order to overcome the local stability resulting from design methods adapted from linearization, a global asymptotic stable controller, which both achieves global stability margin and avoids winding phenomenon, is designed using a backstepping method. This method breaks down nonlinear systems into low dimensional systems and simplifies the controller design using virtual control inputs and partial Lyapunov functions. The stability of the system is easily proven via the Lyapunov function. Abundant simulation results validate the theoretical analysis.

From the practical engineering point of view, path tracking control for mobile robots has been investigated. It comes from planning trajectory with the planned geometric path and enables direct tracking of the geometric path. The kinematic model of mobile robots and the desired path are described in polar coordinates. The influence on control performance resulting from the factitious choice of desired reference points is eliminated by considering the polar angle as a parameter. The controller is designed based on a backstepping method, which is systematic and flexible. The convergence of the system is throughout the design procedure. Simulation results are given to verify the proposed control laws.