State-of-the-art in control engineering

AbstractThe paper deals with new trends in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network control structure and new information and communication technologies. Furthermore, increasing problems with interactions, process non-linearities, operating constraints, time delays, uncertainties, and significant dead-times consequently lead to the necessity to develop more sophisticated control strategies. Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes. Main ideas covered in this paper are motivated namely by the development of new advanced control engineering methods (predictive, hybrid predictive, optimal, adaptive, robust, fuzzy logic, and neural network) and new possibilities of their SW and HW realizations and successful implementation in industry

Evolutionary design of digital trajectory-tracking controllers for robotic manipulators

The design of digital trajectory-tracking controllers for robotic manipulators is a challenging task, since such manipulators are multivariable non-linear plants. In addition, in many applications of robotic manipulators, it is required that very high-accuracy trajectory-tracking performance be achievable even in the presence of unpredictable payload variations. These requirements can all be met to some extent by application of the previously developed fast-sampling digital PID controllers to robotic manipulators. Indeed, for such controllers, it is possible to prove a series of very reassuring robustness results using only the Markov parameters associated with locally linearised representations of robotic manipulators.However, these theoretical optimisation results for digital PID controllers are only valid as sampling periods become vanishingly small. In practice, of course, the sampling periods of digital controllers remain non-zero; but, in such cases, no theoretical optimisation results are available. There is, therefore, a great need for some alternative optimisation procedure that will facilitate the non- asymptotic design of digital PID controllers for robotic manipulators.This design need is addressed in this thesis. In particular, the following evolutionary optimisation techniques are used to design digital trajectorytracking controllers for robotic manipulators:(i) genetic algorithms,(ii) non-adaptive evolution strategies(iii) adaptive evolution strategies.It is shown that, with increasing effectiveness, these techniques are very useful in the design of high-accuracy digital PID controllers. These techniques are illustrated by the presentation of simulation results for a typical three-link robotic manipulator performing a range of demanding trajectory-tracking tasks in the presence of unpredictable payload variations. In addition, these evolutionary optimisation techniques are also used in the design of unconstrained digital PID controllers, in which all elements of the controller matrices are used as the design parameters.In order to validate these evolutionary design techniques in practice, an experimental laboratory investigation is also undertaken. This involves the practical implementation, in the case of a direct-drive two-link robotic manipulator, of digital PID trajectory-tracking controllers designed using evolutionary techniques. The results thus obtained indicate that such optimisation techniques greatly facilitate the tuning of digital PID controllers for robotic manipulators under practical non-asymptotic conditions

Air Force Institute of Technology Research Report 1997

This report summarizes the research activities of the Air Force Institute of Technology\u27s Graduate School of Engineering and the Graduate School of Logistics and Acquisition Management. It describes research interests and faculty expertise; list student theses/dissertations; identifies research sponsors and contributions; and outlines the procedure for contacting either school

Evolutionary algorithms for active vibration control of flexible manipulator

Flexible manipulator systems offer numerous advantages over their rigid counterparts including light weight, faster system response, among others. However, unwanted vibration will occur when flexible manipulator is subjected to disturbances. If the advantages of flexible manipulator are not to be sacrificed, an accurate model and efficient control system must be developed. This thesis presents the development of a Proportional-Integral-Derivative (PID) controller tuning method using evolutionary algorithms (EA) for a single-link flexible manipulator system. Initially, a single link flexible manipulator rig, constrained to move in horizontal direction, was designed and fabricated. The input and output experimental data of the hub angle and endpoint acceleration of the flexible manipulator were acquired. The dynamics of the system was later modeled using a system identification (SI) method utilizing EA with linear auto regressive with exogenous (ARX) model structure. Two novel EAs, Genetic Algorithm with Parameter Exchanger (GAPE) and Particle Swarm Optimization with Explorer (PSOE) have been developed in this study by modifying the original Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. These novel algorithms were introduced for the identification of the flexible manipulator system. Their effectiveness was then evaluated in comparison to the original GA and PSO. Results indicated that the identification of the flexible manipulator system using PSOE is better compared to other methods. Next, PID controllers were tuned using EA for the input tracking and the endpoint vibration suppression of the flexible manipulator structure. For rigid motion control of hub angle, an auto-tuned PID controller was implemented. While for vibration suppression of the endpoint, several PID controllers were tuned using GA, GAPE, PSO and PSOE. The results have shown that the conventional auto-tuned PID was effective enough for the input tracking of the rigid motion. However, for end-point vibration suppression, the result showed the superiority of PID-PSOE in comparison to PID-GA, PID-GAPE and PID-PSO. The performance of the best simulated controller was validated experimentally later. Through experimental validation, it was found that the PID-PSOE was capable to suppress the vibration of the single-link flexible manipulator with highest attenuation of 31.3 dB at the first mode of the vibration. The outcomes of this research revealed the effectiveness of the PID controller tuned using PSOE for the endpoint vibration suppression of the flexible manipulator amongst other evolutionary methods