Engineering Applications of MATLAB® 5.3 and SIMULINK® 3: Translated from the French by Mohand Mokhtari, Michel Marie, Cécile Davy and Martine Neveu - Softcover

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1 : Analog and digital control.- 1. The principle.- 2. Presentation of main types of corrector.- 2.1. Proportionnal corrector.- 2.2. Integral control.- 2.3. Derivative corrector.- 2.4. Derivative return corrector.- 2.5. Phase lead corrector.- 2.6. Phase lag corrector.- 2.7. PID controller.- 2.8. Predictive action corrector.- 2.9. PIR corrector, pure delay system.- 3. Analog correctors discretisation.- 4. Corrected systems stability.- 4.1. General conditions of stability.- 4.2. Nyquist criterion.- 4.3. Discrete systems stability.- 5. Examples.- 5.1. Using some MATLAB® functions.- 5.2. Using a PIR corrector.- 6. LQ, LQI, quadratic linear control.- 6.1. LQI control of a monovariable process.- 6.1.1. Model without integrator.- 6.1.2. Model with integrator.- 6.2. LQI control of a multivariable process.- 6.2.1. LQ multivariable control.- 6.2.1. LQI multivariable control.- 6.3. Application example.- 6.3.1. LQI control of a monovariable aerothermal system.- 6.3.2. LQI control of a multivariable system.- 7. RST control.- 7.1 Monovariable system.- 7.2 Multivariable system.- 7.3 Application example.- 7.3.1. RST monovariable control of the temperature.- 7.3.2. RST multivariable control of the aerothermal process.- 2: State representation of continuous and discrete systems.- 1. State representation of continuous systems.- 1.1. Heuristic approach.- 1.2. State representation generalization.- 2. State representation of discrete systems.- 3.1. Heuristic approach.- 3.2. Application.- 3. Controllability and observability.- 3.1. Controllability.- 3.2. Observability.- 4. State reconstruction of a discrete dynamic system.- 4.1. Closed-loop estimation of a deterministic process.- 5. State return control.- 6. Examples.- 6.1. State return control system of a process including an integration.- 6.2. State return control system of a process not including an integration.- 6.3. Control system by poles placing of a discrete system.- 7. Kalman filter.- 8. Discrete stochastic Kalman predictor.- 3: Fuzzy logic control.- 1. The fundamental principle.- 2. Stages of implementation of a fuzzy regulator.- 2.1. Fuzzification stage.- 2.2. Inference stage.- 2.3. Defuzzification stage.- 3. Graphical interface of the "Fuzzy Logic TOOLBOX".- 4. Creation of a fuzzy system using the toolbox commands.- 4.1. Input and output variables fuzzification.- 4.2. Fuzzy Rules Editor.- 4.3. Defuzzification.- 4.4. Using the regulator in a control law.- 5. Fuzzy regulator use in SIMULINK®.- 6. Sugeno's method.- 6.1. Realisation of the fuzzy regulator using the graphic interface.- 6.1. Realisation of the fuzzy regulator using the TOOLBOX commands.- 4: Neural networks.- 1. Introduction.- 2. Linear adaptive neural networks.- 2.1. Architecture.- 2.2. Training law.- 2.3. Some applications fields.- 2.3.1. Process identification.- 2.3.2. Signal prediction.- 2.3.3. Interference cancellation.- 3. Neural networks with hidden layers, back-propagation error.- 3.1. Principle.- 3.2. Transfer functions.- 3.3. Back-propagation algorithm.- 4. Inverse neural model control.- 4.1. First architecture.- 4.2. Second architecture.- 4.2.1. Addition of an integration.- 4.2.2. Adaptive control.- 5. Signal prediction.- 5: Adaptive filtering.- 1. The adaptive filtering principle.- 2. Gradient algorithm, LMS criterion.- 2.1. ? scalar adaptation choice.- 2.2. Adaptation speed, filter time constant.- 3. The recursive least squares algorithm, exact least squares criterion.- 4. Examples of LMS adaptive filters.- 4.1. Adaptive predictor for an autoregressive process.- 4.1. Interference cancellation.- 4.1. Extraction of a signal drowned in noise.- 5. RLS adaptive filter example.- 5.1. Extraction of a signal drowned in noise.- Application 1: Power amplifier.- 1. Description of amplifier.- 2. Characterization of amplifier.- 3. Amplifier with transistors stage feedback.- 4. Amplifier with phase lag corrector.- 5. Amplifier with feedback of phase lead type corrector.- Application 2: Electromagnetic levitation.- 1. Process

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