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A digital motor controller for student lab use is designed, built, and tested. The controller uses an encoder for position measurement, and an H-bridge to drive the electromechanical plant. A user interface is created to enhance usability of the device. The user interface is able to display key parameters of the control algorithm as well as the state of the system. It is also used to modify the gains and sample rate of the control algorithm. The design of the system is refined, and 10 units are built for the EE472 Digital Controls Lab. The lab manuals for the first 4 experiments are revised to match and support the new system. The possible future for the project is described with some suggestions for improving the system.

In recent years, multi-robot systems have been widely used in many applications such as warehouse inventory tracking and automatic search and rescue operations. Probability roadmap (PRM) is a typical path planning algorithm that can determine an optimal trajectory once the robot start and goal positions are specified. However, when the number of robots in the system increases, it converges slowly and may even fail to find the solution. In this thesis, a fuzzy inference system is proposed and combined with the probability roadmap algorithm for robot path planning. Computer simulation results in five different environments show this approach is very effective to reduce computation cost in most cases for multi-robot systems of various sizes. It is able to reduce the number of collision checks by at least 27% with a trade off of increased average path length of 12% at the most. It is also noticed that the proposed fuzzy system is not advantageous when combined with the sub-dimension expansion algorithm. More research will be conducted in the future to further improve the performance of the proposed fuzzy inference system.

This thesis investigates the broad theme of guaranteeing the stability of nonlinear control systems. In the first section, we describe the application of discrete controller for the stabilization of certain nonlinear stochastic control systems subject to unavailability of state measurements. In the second section, we consider input constrained nonlinear systems and characterize the region from which stabilization to the origin is possible. We then use this information to design a controller which stabilizes everywhere in this set. / Thesis / Master of Applied Science (MASc)

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A new decentralized nonlinear controller for Robot Manipulators is presented in this thesis. Based on concepts of Lyapunov stability theory and some control ideas proposed in [3]-[7], we obtain continuous nonlinear decentralized control laws which guarantee position and velocity tracking to within an arbitrarily small error. Assumptions based on physical constraints of manipulators are made to guarantee the existence of the controller and asymptotic stability of the closed loop system. Simulations show how well this rather simple control scheme works on two of the links of the Puma 560 Manipulator. The main contribution of this thesis is that it extends the results of a class of complex centralized control algorithms to the decentralized robust control of interconnected nonlinear subsystems like robot manipulators. / Graduation date: 1992

Thesis (Ph. D.)--Worcester Polytechnic Institute. / Keywords: Stochastic optimal control; dynamic programming; Hamilton-Jacobi-Bellman equation; Random vibration. Keywords: Stochastic optimal control; dynamic programming; Hamilton-Jacobi-Bellman equation; Random vibration; energy balance method. Includes bibliographical references (p. 86-89).

Adaptive control of a nonlinear time varying (NTV) plant, such as a robotic manipulator, is intended to tolerate the unmodeled disturbances and the uncertain parameters of the dynamic model. Most of the previous research has been focused on NTV plants with bounded and "slowly-varying" plant terms. Almost all adaptive controllers require persistent excitations to guarantee stable tracking in the presence of unmodeled disturbances. The new adaptive controllers developed in this work provide stable and robust performance without persistent excitations and the "slowly-varying" assumption. Moreover, the uncertainties of a NTV plant model are not required to be bounded. This allows one to treat some potentially unbounded dynamics as disturbances. Stability and robustness analysis of adaptive controllers under the relaxed conditions is an essential part of this study. A major problem arising in robotic control is parameter uncertainty. The linear parameterization approach is also implemented in this work to deal with the parameter uncertainty. An innovative algorithm for determining the manipulator "regressor" (a coefficient matrix in parameter-linearized form of robot dynamics) is developed. Based on this algorithm a robust self-tuning controller is designed. The control law is proved to be robust with respect to parameter errors and disturbances. The robustness of the controller relaxes the requirement for the parameter estimator, and leads to a stable system without persistent excitations. / Graduate

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy