Lyapunov-based control of nonlinear mechatronic systems

This Ph.D. dissertation describes the design and implementation of various Lyapunov-based nonlinear control strategies for mechatronic systems. Both an adaptive tracking controller of on-line pathplanners and a fault identification strategy is presented for robot manipulators. A passive coordination control strategy is presented for nonlinear teleoperated systems. In closing an adaptive battery state of charge estimation strategy is presented which can be utilized in the hybrid electric vehicle application.; First, motivated by task objectives that are more effectively described by online, state-dependent trajectories, two adaptive tracking controllers are developed to accommodate on-line path planning objectives. An example adaptive controller is first modified to achieve velocity field tracking in the presence of parametric uncertainty in the robot dynamics. The development aims to relax the typical assumption that the integral of the velocity field is bounded by incorporating a norm squared gradient term in the control design so that the boundedness of all signals can be proven. An extension is then provided that targets the trajectory planning problem where the task objective can be described as the desire to move to a goal configuration while avoiding known obstacles. Specifically, an adaptive navigation function based controller is designed to provide a path from an initial condition inside the free configuration space of the robot manipulator to the goal configuration. Experimental results for each controller are provided to illustrate proof of validation of the approaches.; Secondly, an Lyapunov-based nonlinear observer is presented for the application of fault identification for robotic systems. Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. A nonlinear observer is used to identify a class of actuator faults once the fault has been detected by some other method. Advantages of the proposed fault identification method are that it is based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), it does not require acceleration measurements, and it is independent from the controller.; In the following section, two controllers are developed for a nonlinear teleoperator system that target coordination of the master and slave manipulators and passivity of the overall system.; In the final section, an adaptive least-squares estimation strategy is presented to determine the state of charge (SOC) for an electro-chemical battery. (Abstract shortened by UMI.)...