Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 107-112). Also available via the Internet.

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

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This thesis discusses the use of genetic algorithms as a global search technique to solve three optimization problems: a sixth-order polynomial problem, a single-degree-of-freedom spring-mass-damper (SDOF SMD) system problem, and a loading bridge regulator problem. Genetic algorithms are iterative global search techniques based on the principles of natural selection and population genetics. The theory, design and implementation of the algorithm is discussed in detail. The Simple Genetic Algorithm (SGA) is presented to solve a sixth-order polynomial optimization problem. Results from two traditional numerical techniques will be compared with the SGA results as well as the analytical calculus solution. In addition, the effect of different parametric sizes of the genetic operators are investigated. In the second problem, genetic algorithms are used to design a two-state feedback optimal gain set for a SDOF SMD model with a given initial condition. An improved selection scheme called the stochastic remainder selection without replacement is introduced. An improved GA-based (IGA) feedback controller is designed to control the system. Lastly, a regulator control problem is presented using advanced genetic algorithms (AGA). Two-point crossover and inversion operators are employed. A loading bridge is chosen as the control model. An advanced GA-based full-state feedback controller is designed to control the loading bridge with the given reference input voltage. The conclusions show that SGA is more robust than traditional numerical techniques to solve multi-modal functions. Among the three GA approaches considered, AGA is the most robust one for the design of adaptive feedback controllers. / Master of Science

Three discrete-time adaptive controllers are developed and applied to Four-bar linkage velocity control to reduce the input link velocity fluctuations without compromising the control system velocity transient response. The successful control techniques use the known mechanism kinematics and the mechanism input link position to control the nonlinear mechanism dynamics. The study shows that the adaptive controls are feasible to implement using current microprocessor technology, and the velocity control performance is improved when compared to an industry-standard analog servomotor control. However, more development is required to realize the full potential of the adaptive control technique. A nonlinear Four-bar dynamic model is developed using Kinematic Influence Coefficients. This model is used to develop the adaptive controls and to computer simulate the control scheme performances. The simulated model velocity response is compared qualitatively to experimental data and shown to be similar to an experimental device. / Master of Science

This thesis is concerned with the applicability of model reference adaptive control to the control of robot manipulators under a wide range of configuration and payload changes, and a comparison of the performance of this technique with that of the non-adaptive schemes. The dynamic equations of robot manipulators are highly nonlinear and are difficult to determine precisely. For these reasons there is an interest in applying adaptive control techniques to robot manipulators. In this work, the detailed performance of three adaptive controllers are studied and compared with that of a non-adaptive controller, namely, the computed torque control scheme. Computer simulation results show that the use of adaptive control improves the performance of the manipulator despite changes in the payload or in the manipulator configuration. Making use of these results, a rule-based controller is developed by dividing a given manipulation task into portions where a particular adaptive control scheme, based on a specific linearized subsystem model, performs best. This strategy of selecting the proper controller during each portion of the overall task yields a performance having the least deviation from the desired trajectory during the entire length of the task.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. The internal model of this predictive controller is based on step response measurements at an average operating point. As the process moves away from this point, however, control becomes sub-optimal due to process non-linearity. If DMC is made adaptive, it can be expected to perform well even in the presence of uncertainties, non-linearities and time-vary ing process parameters. This project examines modelling and control issues for a complex multivariable industrial operator training plant, and develops and applies a method for adapting the controller on-line to account for non-linearity. A two-input/two-output sub-system of the Training Plant was considered. A special technique had to be developed to deal with the integrating nature of this system - that is, its production of ramp outputs for step inputs. The project included the commissioning of the process equipment and the addition of instrumentation and interfacing to a SCADA system which has been developed in the School of Chemical Engineering. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.