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Intelligent systems and control : principles and applications / Laxmidhar Behera

By: Behera, LaxmidharContributor(s): Behera, LaxmidharMaterial type: Text

TextPublisher number: :Donated from Prof Madan GopalPublication details: New Delhi : Oxford University Press, ©2009Description: xiii, 373 pages : illustrations ; 25 cmISBN: 9780198063155Subject(s): Computer science | Special computer methods (e.g. AI, multimedia, VR)[4] 007–009 [Unassigned] | Intelligent control systems | Expert systems (Computer science)DDC classification: 006.3 BEH

Contents:

Machine generated contents note: 1. Non-linear Control: Primer -- 1.1. Norms of Signals, Vectors, and Matrices -- 1.2. Positive Definite Functions -- 1.3. Positive Definite Matrices -- 1.4. Continuous Time State-Space Model -- 1.4.1. LTI State-Space Model -- 1.5. Non-linear State-Space Model -- 1.5.1. Equilibrium Point and Linearization using First-order Taylor Series -- 1.5.2. Linearization Technique for Operating Points Other Than the Origin -- 1.6. Lyapunov Stability Theory -- 1.6.1. Lyapunov Stability of Time Invariant System -- 1.6.2. LaSalle's Invariance Theorem -- 1.6.3. Chetaev's Instability Theorem -- 1.6.4. Lyapunov Stability of Time Varying System -- 1.6.5. Lyapunov's Indirect Method -- 1.6.6. Lyapunov Stability for Linear Systems -- 1.7. Discrete Time Systems -- 1.7.1. Discrete Time LTI State-Space Model -- 1.7.2. Discrete Time Non-linear State-Space Model -- 1.7.3. ARMAX and NARMAX Models -- 1.7.4. Lyapunov Stability for Discrete Time Systems -- 1.8. Modelling of Different Non-linear Systems -- 1.8.1. Inertial Wheel Pendulum -- 1.8.2. Two Link Manipulator -- 1.8.3. An Inverted Pendulum Mounted on a Cart -- 1.8.4. Induction Motor -- 1.9. Non-linear Control Strategies -- 1.9.1. Feedback Linearization -- 1.9.2. Back-stepping Design -- 1.9.3. State Feedback Linearizable Systems -- 2. Neural Networks -- 2.1. Feed-forward Networks -- 2.2. Multi-layered Neural Networks -- 2.2.1. Principle of Gradient Descent -- 2.2.2. Derivation of Back Propagation Algorithm -- 2.2.3. Generalized Delta Rule -- 2.2.4. Convergence of the BP Learning Algorithm -- 2.3. Radial Basis Function Networks -- 2.3.1. Radial Basis Functions -- 2.3.2. Learning in RBFN -- 2.4. Adaptive Learning Rate -- 2.4.1. Lyapunov Function Based Adaptive Learning Rate -- 2.5. Feedback Networks -- 2.5.1. Response of Recurrent Networks -- 2.5.2. Learning Algorithms -- 2.5.3. Back Propagation Through Time -- 2.5.4. Real Time Recurrent Learning -- 2.6. Kohonen Self-organizing Map -- 2.7. System Identification Using Neural Networks -- 2.8. SOM Based Identification -- 3. Fuzzy Logic -- 3.1. Classical Sets -- 3.1.1. Operations on Classical Sets -- 3.2. Fuzzy Sets -- 3.2.1. Concept of a Fuzzy Number -- 3.2.2. Operations on Fuzzy Sets -- 3.2.3. Other Fuzzy Operations -- 3.2.4. Properties of Fuzzy Sets -- 3.2.5. Some Typical Membership Functions -- 3.2.6. Fuzzy Membership versus Probability -- 3.2.7. Extension Principle of Fuzzy Sets -- 3.2.8. Crisp Relation -- 3.2.9. Fuzzy Relations -- 3.2.10. Projection of Fuzzy Relations -- 3.2.11. Cylindrical Extension of Fuzzy Relations -- 3.2.12. Relation Inference -- 3.3. Fuzzy Rule Base and Approximate Reasoning -- 3.3.1. Fuzzy Linguistic Variables -- 3.3.2. Linguistic Modifier -- 3.3.3. Rule-base Systems -- 3.3.4. Fuzzy Rule Base -- 3.3.5. Fuzzy Implication Relations -- 3.3.6. Fuzzy Compositional Rules -- 3.3.7. Inference Mechanism Compared -- 3.3.8. Approximate Reasoning -- 3.4. Fuzzy Logic Control -- 3.4.1. Mamdani Model -- 3.4.2. Takagi-Sugeno Fuzzy Model -- 3.5. System Identification Using T-S Fuzzy Models -- 3.5.1. The T-S Model from Input-Output Data -- 3.5.2. The T-S Fuzzy Model Using Linearization -- 4. Indirect Adaptive Control Using Neural Networks -- 4.1. Continous Time Affine Systems -- 4.1.1. Model Identification -- 4.1.2. Controller Design -- 4.2. Discrete Time Affine Systems -- 4.2.1. Model Identification -- 4.2.2. Controller Design -- 4.3. Discrete Time Non-affine System -- 4.3.1. Model Identification -- 4.3.2. Controller Design: Traditional NN Approach -- 4.3.3. Controller Design: Network Inversion -- Appendix -- 5. Direct Adaptive Control Using Neural Networks -- 5.1. Direct Adaptive Control -- 5.2. Single Input Single Output Affine Systems -- 5.2.1.f(x) is Unknown But g(x) is Known -- 5.2.2.f(x) and g(x) Both are Unknown -- 5.3. Multi-input Multi-output Systems -- 5.4. Single Input Single Output Discrete Time Affine Systems -- 5.4.1.f(x) is Unknown But g(x) is Known -- 5.4.2.f(x) and g(x) Both Are Unknown -- 5.5. Back-stepping Control -- 5.5.1. System Description -- 5.5.2. Traditional Back-stepping Design -- 5.5.3. Robust Back-stepping Controller Design Using RBFN -- 5.5.4. Back-stepping Control for a Robot Manipulator -- 6. Approximate Dynamic Programming -- 6.1. Linear Quadratic Regulator -- 6.2. The HJB Formulation -- 6.3. HJB for Affine Systems -- 6.4. HDP and DHP -- 6.5. Single Network Adaptive Critic -- 6.6. Continuous Time Adaptive Critic -- 6.7. Adaptive Critic Using the T-S Fuzzy Model -- 6.7.1. Continuous Time Adaptive Critic -- 6.7.2. Discrete Time Adaptive Critic -- 7. Fuzzy Logic Control -- 7.1. Construction of an FLC -- 7.2. Fuzzy PD Controller -- 7.2.1. The Rule Base -- 7.2.2. Membership Function -- 7.2.3. Fuzzy Parameter Optimization -- 7.2.4. Rule Generation Using Optimization Technique -- 7.3. Fuzzy PI Controller -- 7.3.1. The Rule Base for the Fuzzy PI Controller -- 7.3.2. Membership Function -- 7.3.3. Parameter Optimization and Rule Generation Using UMDA -- 7.4. Fuzzy PI Controller for a Series DC Motor -- 7.4.1. Parameter Optimization and Rule Generation -- 7.5. FLC Using Lyapunov Synthesis -- 7.5.1. Rotational-Translational Proof Mass Actuator -- 7.6. Horizontal Planar Two Link Robot Manipulator -- 7.6.1. Arm Posture -- 7.6.2. Elbow Control -- 7.6.3. Controller Design -- Appendix -- 8. Takagi -- Sugeno Fuzzy Model Based Control -- 8.1.T-S Fuzzy Model -- 8.2. Linear Matrix Inequality Technique -- 8.2.1.Common Lyapunov Matrix Criterion for Stability of the T-S Model -- 8.2.2. Parallel Distributed Fuzzy Compensator -- 8.3. Fixed Gain State Feedback Controller Design Technique -- 8.3.1. Fixed Gain State Feedback Controller -- 8.4. Variable Gain Controller Design Using Single Linear Nominal Plant -- 8.4.1. The Control Problem -- 8.4.2. Variable Gain Controller I -- 8.5. Variable Gain Controller Design Using Each Linear Subsystem as Nominal Plant -- 8.5.1. The Control Problem -- 8.5.2. Variable Gain Controller II -- 8.6. Controller Design Using Discrete T-S Fuzzy System -- 8.6.1. Linear State Feedback Controller for Discrete T-S Fuzzy System -- Appendix -- 9. Intelligent Control of a Pendulum on a Cart -- 9.1.T-S Fuzzy Model Representation -- 9.2. Control Using the T-S Fuzzy Model -- 9.3.Network Inversion Based Control -- 9.3.1. Continuous-time Iterative Update -- 9.3.2. Discrete-time Update -- 9.4.T-S Fuzzy Controller -- 9.4.1. Continuous Time Weight Update Law -- 9.4.2. Discrete Time Weight Update Law -- 9.5. Cart-Pole System: Simulation and Experiment -- 9.5.1.T-S Fuzzy Model of the Cart-Pole -- 9.5.2. Control Systems Design -- 9.5.3. Experiment on a Cart-Pole System -- 10. Visual Motor Control of a Redundant Manipulator -- 10.1. System Model -- 10.1.1. Experimental Set-up -- 10.1.2. The Manipulator Model -- 10.1.3. The Camera Model -- 10.2. Visual Motor Control Using Neural Networks -- 10.2.1. Visual Motor Control with KSOM -- 10.2.2. Simulation and Experimental Results -- 10.2.3. Training -- 10.2.4. Testing -- 10.2.5. Real-time Experiment -- 10.3. Visual Motor Control Using a Fuzzy Network -- 10.3.1. Fuzzy C-Mean Clustering -- 10.3.2. Multi-step Incremental Learning -- 10.3.3. Simulation and Experimental Results -- 10.3.4. VMC Using Incremental Learning.

Summary: Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate students of electrical and computer science engineering as also postgraduate students undertaking courses on intelligent control, intelligent systems, adaptive control, and nonlinear control.

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Mathematics Departmental Library	Mathematics Departmental Library	006.3 BEH (Browse shelf(Opens below))	Available		M370

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Machine generated contents note: 1. Non-linear Control: Primer --
1.1. Norms of Signals, Vectors, and Matrices --
1.2. Positive Definite Functions --
1.3. Positive Definite Matrices --
1.4. Continuous Time State-Space Model --
1.4.1. LTI State-Space Model --
1.5. Non-linear State-Space Model --
1.5.1. Equilibrium Point and Linearization using First-order Taylor Series --
1.5.2. Linearization Technique for Operating Points Other Than the Origin --
1.6. Lyapunov Stability Theory --
1.6.1. Lyapunov Stability of Time Invariant System --
1.6.2. LaSalle's Invariance Theorem --
1.6.3. Chetaev's Instability Theorem --
1.6.4. Lyapunov Stability of Time Varying System --
1.6.5. Lyapunov's Indirect Method --
1.6.6. Lyapunov Stability for Linear Systems --
1.7. Discrete Time Systems --
1.7.1. Discrete Time LTI State-Space Model --
1.7.2. Discrete Time Non-linear State-Space Model --
1.7.3. ARMAX and NARMAX Models --
1.7.4. Lyapunov Stability for Discrete Time Systems --
1.8. Modelling of Different Non-linear Systems --
1.8.1. Inertial Wheel Pendulum --
1.8.2. Two Link Manipulator --
1.8.3. An Inverted Pendulum Mounted on a Cart --
1.8.4. Induction Motor --
1.9. Non-linear Control Strategies --
1.9.1. Feedback Linearization --
1.9.2. Back-stepping Design --
1.9.3. State Feedback Linearizable Systems --
2. Neural Networks --
2.1. Feed-forward Networks --
2.2. Multi-layered Neural Networks --
2.2.1. Principle of Gradient Descent --
2.2.2. Derivation of Back Propagation Algorithm --
2.2.3. Generalized Delta Rule --
2.2.4. Convergence of the BP Learning Algorithm --
2.3. Radial Basis Function Networks --
2.3.1. Radial Basis Functions --
2.3.2. Learning in RBFN --
2.4. Adaptive Learning Rate --
2.4.1. Lyapunov Function Based Adaptive Learning Rate --
2.5. Feedback Networks --
2.5.1. Response of Recurrent Networks --
2.5.2. Learning Algorithms --
2.5.3. Back Propagation Through Time --
2.5.4. Real Time Recurrent Learning --
2.6. Kohonen Self-organizing Map --
2.7. System Identification Using Neural Networks --
2.8. SOM Based Identification --
3. Fuzzy Logic --
3.1. Classical Sets --
3.1.1. Operations on Classical Sets --
3.2. Fuzzy Sets --
3.2.1. Concept of a Fuzzy Number --
3.2.2. Operations on Fuzzy Sets --
3.2.3. Other Fuzzy Operations --
3.2.4. Properties of Fuzzy Sets --
3.2.5. Some Typical Membership Functions --
3.2.6. Fuzzy Membership versus Probability --
3.2.7. Extension Principle of Fuzzy Sets --
3.2.8. Crisp Relation --
3.2.9. Fuzzy Relations --
3.2.10. Projection of Fuzzy Relations --
3.2.11. Cylindrical Extension of Fuzzy Relations --
3.2.12. Relation Inference --
3.3. Fuzzy Rule Base and Approximate Reasoning --
3.3.1. Fuzzy Linguistic Variables --
3.3.2. Linguistic Modifier --
3.3.3. Rule-base Systems --
3.3.4. Fuzzy Rule Base --
3.3.5. Fuzzy Implication Relations --
3.3.6. Fuzzy Compositional Rules --
3.3.7. Inference Mechanism Compared --
3.3.8. Approximate Reasoning --
3.4. Fuzzy Logic Control --
3.4.1. Mamdani Model --
3.4.2. Takagi-Sugeno Fuzzy Model --
3.5. System Identification Using T-S Fuzzy Models --
3.5.1. The T-S Model from Input-Output Data --
3.5.2. The T-S Fuzzy Model Using Linearization --
4. Indirect Adaptive Control Using Neural Networks --
4.1. Continous Time Affine Systems --
4.1.1. Model Identification --
4.1.2. Controller Design --
4.2. Discrete Time Affine Systems --
4.2.1. Model Identification --
4.2.2. Controller Design --
4.3. Discrete Time Non-affine System --
4.3.1. Model Identification --
4.3.2. Controller Design: Traditional NN Approach --
4.3.3. Controller Design: Network Inversion --
Appendix --
5. Direct Adaptive Control Using Neural Networks --
5.1. Direct Adaptive Control --
5.2. Single Input Single Output Affine Systems --
5.2.1.f(x) is Unknown But g(x) is Known --
5.2.2.f(x) and g(x) Both are Unknown --
5.3. Multi-input Multi-output Systems --
5.4. Single Input Single Output Discrete Time Affine Systems --
5.4.1.f(x) is Unknown But g(x) is Known --
5.4.2.f(x) and g(x) Both Are Unknown --
5.5. Back-stepping Control --
5.5.1. System Description --
5.5.2. Traditional Back-stepping Design --
5.5.3. Robust Back-stepping Controller Design Using RBFN --
5.5.4. Back-stepping Control for a Robot Manipulator --
6. Approximate Dynamic Programming --
6.1. Linear Quadratic Regulator --
6.2. The HJB Formulation --
6.3. HJB for Affine Systems --
6.4. HDP and DHP --
6.5. Single Network Adaptive Critic --
6.6. Continuous Time Adaptive Critic --
6.7. Adaptive Critic Using the T-S Fuzzy Model --
6.7.1. Continuous Time Adaptive Critic --
6.7.2. Discrete Time Adaptive Critic --
7. Fuzzy Logic Control --
7.1. Construction of an FLC --
7.2. Fuzzy PD Controller --
7.2.1. The Rule Base --
7.2.2. Membership Function --
7.2.3. Fuzzy Parameter Optimization --
7.2.4. Rule Generation Using Optimization Technique --
7.3. Fuzzy PI Controller --
7.3.1. The Rule Base for the Fuzzy PI Controller --
7.3.2. Membership Function --
7.3.3. Parameter Optimization and Rule Generation Using UMDA --
7.4. Fuzzy PI Controller for a Series DC Motor --
7.4.1. Parameter Optimization and Rule Generation --
7.5. FLC Using Lyapunov Synthesis --
7.5.1. Rotational-Translational Proof Mass Actuator --
7.6. Horizontal Planar Two Link Robot Manipulator --
7.6.1. Arm Posture --
7.6.2. Elbow Control --
7.6.3. Controller Design --
Appendix --
8. Takagi --
Sugeno Fuzzy Model Based Control --
8.1.T-S Fuzzy Model --
8.2. Linear Matrix Inequality Technique --
8.2.1.Common Lyapunov Matrix Criterion for Stability of the T-S Model --
8.2.2. Parallel Distributed Fuzzy Compensator --
8.3. Fixed Gain State Feedback Controller Design Technique --
8.3.1. Fixed Gain State Feedback Controller --
8.4. Variable Gain Controller Design Using Single Linear Nominal Plant --
8.4.1. The Control Problem --
8.4.2. Variable Gain Controller I --
8.5. Variable Gain Controller Design Using Each Linear Subsystem as Nominal Plant --
8.5.1. The Control Problem --
8.5.2. Variable Gain Controller II --
8.6. Controller Design Using Discrete T-S Fuzzy System --
8.6.1. Linear State Feedback Controller for Discrete T-S Fuzzy System --
Appendix --
9. Intelligent Control of a Pendulum on a Cart --
9.1.T-S Fuzzy Model Representation --
9.2. Control Using the T-S Fuzzy Model --
9.3.Network Inversion Based Control --
9.3.1. Continuous-time Iterative Update --
9.3.2. Discrete-time Update --
9.4.T-S Fuzzy Controller --
9.4.1. Continuous Time Weight Update Law --
9.4.2. Discrete Time Weight Update Law --
9.5. Cart-Pole System: Simulation and Experiment --
9.5.1.T-S Fuzzy Model of the Cart-Pole --
9.5.2. Control Systems Design --
9.5.3. Experiment on a Cart-Pole System --
10. Visual Motor Control of a Redundant Manipulator --
10.1. System Model --
10.1.1. Experimental Set-up --
10.1.2. The Manipulator Model --
10.1.3. The Camera Model --
10.2. Visual Motor Control Using Neural Networks --
10.2.1. Visual Motor Control with KSOM --
10.2.2. Simulation and Experimental Results --
10.2.3. Training --
10.2.4. Testing --
10.2.5. Real-time Experiment --
10.3. Visual Motor Control Using a Fuzzy Network --
10.3.1. Fuzzy C-Mean Clustering --
10.3.2. Multi-step Incremental Learning --
10.3.3. Simulation and Experimental Results --
10.3.4. VMC Using Incremental Learning.

Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate students of electrical and computer science engineering as also postgraduate students undertaking courses on intelligent control, intelligent systems, adaptive control, and nonlinear control.

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