Decentralized neural network variable structure controller design for wood drying process
This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating decentralized variable structure control (DVSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description of the dynamics of the wood drying process by means of the time-delay neural network is also presented, in which the back-propagation algorithm was implemented for testing, training and validation. Then this identified model is used for simulation purpose and controller design. For comparison purpose, a conventional proportional-integral-derivative (PID) controller is also employed and system performance is evaluated through simulations. The results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln and the DVSC strategy promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying.
Decentralized direct adaptive neural network control of interconnected systems
The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in This work. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by the adjusted weights and sliding mode gains on-line for each subsystem only using local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero.
Intelligent hierarchical control of a deployable manipulator
This thesis focuses on the development and implementation of an intelligent hierarchical controller for a deployable manipulator that may be used in spaceand ground-based applications. The emphasis is on the use of knowledge-based tuning of the low-level controllers so as to realize accurate operation under complex and incompletelyknown conditions, with a degree of autonomy. The robot that is investigated consists of two links: one free to slew (revolute joint) while the other allowed to deploy (prismatic joint). A three-level hierarchical system is developed, to control the manipulator. In the bottom layer of the hierarchy, a conventional proportional-integral-derivative (PID) controller is used, for regulating the manipulator joints. The middle layer monitors the performance of the joints of the manipulator, preprocesses the response signals, and extracts the performance parameters, based on a step-input response. The top layer makes inferences for appropriate tuning actions on the PID servos. A decision table is developed using linguistic rules of expert knowledge in tuning PID servos, in order to facilitate fast decision making in the top layer. The knowledge that is contained in this rulebase is acquired from available sources, and represented and processed using the fuzzy logic theory. The governing equations of motion for the ground-based manipulator are developed using the Lagrangian procedure. A Turbo C program is written for the groundbased simulations of the system, which incorporates the manipulator model and the hierarchical control system. Extensive ground-based simulations are carried out for trajectory following tasks. The space-based simulations are also carried out, making use of an existing Fortran program for a robot model tracking a trajectory. These simulations incorporate proportional-derivative control, along with the feedback linearization technique (FLT). An extensive study is carried out to determine the sensitivity of the performance of the control system to changes in the knowledge base. The developed control system is implemented on an existing prototype manipulator, in laboratory. The control software is written in Watcom C, and the manipulator is controlled using the QNX operating system, for real-time operation. A number of trajectory-following experiments were carried out. The study shows a good