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10 Optimal Control Lecture 1 by Prof Rahdakant Padhi, IISc Bangalore
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There is a real need for progress today in all areas, including control technology. The process of continuously reviewing and improving decision making enables the system to better respond to parameter changes and disturbances. It improves the overall performance of the system. Adaptive Control The controller can modify its behavior in response to changes in process dynamics and disturbances. Its main goal is to compensate for parameter changes (which may occur due to nonlinear actuators or operating conditions). It is also consered a special kind of nonlinear feedback control, in which multiple stages of any process can be dived into different categories that can change at any different rate. In addition to adjustable parameters, an adaptive controller is a controller and a mechanism for setting indivual parameters. It can be thought of as two loops, one loop is the normal feedback to the process and controller. Another loop is the parameter tuning loop, for clarity you can refer to the diagram below: Model Reference Adaptive Control: This is an adaptive servo system where the desired performance is expressed in the form of a reference model that proves the desired Respond to a reference signal. You can refer to the diagram below: MARC basically consists of a factory with unknown parameters, also a reference model to compactly specify the desired output of the control system, a feedback control law with adjustable parameters. The common feedback loop is also called the inner loop, and the parameter adjustment loop is called the outer (outer) loop. A reference model is used to demonstrate the eal response of an adaptive control system to external commands. Controllers are usually parameterized by a set of tunable parameters. Adaptive control design usually requires linear parameterization of the controller to obtain an adaptive mechanism. MARC design typically involves the following three steps: First, a control law with variable parameters is selected. Then select an adjustment law to adjust the parameters. Analyze the convergence properties of the resulting control loop. Neuro-Fuzzy Adaptive Control: As the name suggests, this type of control combines two paradigms, fuzzy logic and neural networks. They are often consered part of soft computing. The term neuro-fuzzy is related to a hybr system acting on two different subproblems: the first subproblem uses a neural network, and the second subproblem uses a fuzzy logic system. There are several different implementations of neuro-fuzzy systems. One such simplified block diagram of a neuro-fuzzy controller is shown below: In the above technique, the learning mechanism of the neural network determines fuzzy rules from a training set. In general, the term neuro-fuzzy refers to a system in which fuzzy sets and rules are iteratively fitted to a vector of data using neural network tuning techniques. If you are more interested in this area, you can consult some reference books or websites to obtain more knowledge in this area. report this ad
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Adaptive and Optimal Control Systems Guide
In this post you can view various different types of adaptive and optimal controller present.Also brief description has been proved for each controller .
(PDF) Adaptive optimal control algorithm for continuous-time …
PDF | In this paper we develop a new online adaptive control scheme, for partially unknown nonlinear systems, which converges to the optimal state.
Nonlinear and Optimal Control Systems: | Guide books – ACM …
Nonlinear and Optimal Control Systems offers a self-contained introduction to analysis techniques used in the design of nonlinear and optimal feedback …
Self-Learning Optimal Control of Nonlinear Systems
This book presents a of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain …
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