A systematic and unified presentation of the fundamentals of adaptive control theory in both continuous time and discrete time
Today, adaptive control theory has grown to be a rigorous and mature discipline. As the advantages of adaptive systems for developing advanced applications grow apparent, adaptive control is becoming more popular in many fields of engineering and science. Using a simple, balanced, and harmonious style, this book provides a convenient introduction to the subject and improves one's understanding of adaptive control theory....
Lecture "Fundamentals of control systems - Chapter 9: Design of discrete control systems" presentation of content: Introduction, discrete lead - lag compensator and PID controller, design discrete systems in the Z domain,.... Invite you to reference.
Control problems arise in the plant and must be solved in the plant. Until plant engineers and control designers are able to communicate with each other, their mutual problems await solution. I do not mean to imply that abstract mathematics is not capable of solving control problems, but it is striking how often the same solution can be reached by using good common sense. High-order equations and high-speed computers can be manipulated to the point where common sense is dulled
inaccuracies lead to the deviation of operational space trajectory provided by the kinematic
One method to deal with this issue can be found in an adaptive control. Xu and Gu
proposed an adaptive control scheme for space robots in both joint space and operational
space [Xu et al., 1992, Gu & Xu, 1993]. However, the adaptive control proposed in [Xu et al.,
1992] requires perfect attitude control and the adaptive control in [Gu & Xu, 1993] is
developed based on an under-actuated system on the assumption that the acceleration of the
base-satellite is measurable.
Preface are not communicated to the people who must apply them. Control problems arise in the plant and must be solved in the plant. Until plant engineers and control designers are able to communicate with each other, their mutual problems await solution. I do not mean to imply that abstract mathematics is not capable of solving control problems, but it is striking how often the same solution can be reached by using good common sense. High-order equations and high-speed computers can be manipulated to the point where common sense is dulled....
This document provides design guidance for enterprises that want to provide Internet and limited
corporate access for their guests and partners. Several solutions for guest and partner access challenges
are proposed and analyzed in this document, at both the architectural and functional levels.
First placed on the market in 1939, the design of PID controllers remains a challenging area that requires new approaches to solving PID tuning problems while capturing the effects of noise and process variations. The augmented complexity of modern applications concerning areas like automotive applications, microsystems technology, pneumatic mechanisms, dc motors, industry processes, require controllers that incorporate into their design important characteristics of the systems.
Fuzzy control is a practical alternative for a variety of challenging control applications
since it provides a convenient method for constructing nonlinear controllers
via the use of heuristic information. Such heuristic information may come from
an operator who has acted as a “human-in-the-loop” controller for a process. In
the fuzzy control design methodology, we ask this operator to write down a set of
rules on how to control the process, then we incorporate these into a fuzzy controller
that emulates the decision-making process of the human.
This thesis addresses two neural network based control systems. The ﬁrst is a neural network based predictive controller. System identiﬁcation and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two diﬀerent plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant.
Robot Manipulator Control offers a complete survey of control systems for serial-link robot arms and acknowledges how robotic device performance hinges upon a well-developed control system. Containing over 750 essential equations, this thoroughly up-to-date Second Edition, the book explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers. It also addresses procedures and issues in computed-torque, robust, adaptive, neural network, and force control.
A systematic control design methodology is introduced for multi-input/multi-output stable
open loop plants with multiple saturations. This new methodology is a substantial improvement
over previous heuristic single-input/single-output approaches.
The idea is to introduce a supervisor loop so that when the references and/or disturbances are
sufficiently small, the control system operates linearly as designed.