Engineering Applications of MATLAB® 5.3 and SIMULINK® 3

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 modelling.- 1.1. Expression of the F attraction power according to the I current in the coil and the e air gap.- 1.2. Process linearization around a quiescent point e(t)=e0.- 1.3. Process transfer functions.- 2. Electric current amplifier control system.- 3. Analogical and discrete models of the x(t) position control system.- 4. x(t) digital follower control.- 5. Using a fuzzy corrector.- 5.1. Variables fuzzification.- 5.2. Inference rules definition.- 5.3. Output defuzzification.- Application 3: Cart with inverted pendulum.- 1. System modelling with 2 degrees of freedom.- 1.1. Kinetic energy of the system on motion.- 1.2. Potential energy of the system.- 1.3. Lagrange equation according to q(t)=?(t) degree of freedom.- 1.4. Lagrange equation according to q(t)=x(t) degree of freedom.- 1.5. Linear model around the operating point.- 2. Linear process state modelling.- 3. Edition and test of the discrete model.- 4. Fuzzy regulation of the ?(t) angular position.- 4.1. Inputs fuzzification, membership functions definition.- 4.2. Inference rules definition, defuzzification.- 4.3. Achieving the fuzzy controller.- 5. Fuzzy control of the x(t) position and the ?(t) angle.- 5.1. Inputs fuzzification, membership functions.- 5.2. Inference rules definition, defuzzification.- 5.3. Achieving the fuzzy controller.- 6. Graphical animation of the system.- Application 4: Oven control.- 1. Oven modelling.- 2. Integral control with compensation of poles and zeros.- 3. Discrete state representation of the oven.- 4. Control by state return with integration.- 5. Using a Kaiman reconstructor.- 6. LQ quadratic linear control.- 7. Control by neuronal inverse model.- Application 5: Travelling gantry crane with suspended mass.- 1. Modelling the travelling gantry crane with 2 degrees of freedom.- 1.1. Kinetic energy of the system on motion.- 1.2. Potential energy of the system.- 1.3. Lagrange equation for the q(t)=?(t) degree of freedom.- 1.4. Lagrange equation for the q(t)=x(t) degree of freedom.- 1.5. Linear model upon the operating point.- 2. Transfer functions of the system.- 2.1. Step response of the open loop process.- 2.2. Edition and test of the model.- 3. Regulation of the ?(t) angular position.- 4. Regulation of the x(t) position truck and the ?(t) angle.- 5. State space modelling.- 5.1. Discrete state space model.- 5.2. Luenberger's state observer.- 5.3. State space control of the process.- 5.4. Adding an integral correction.- 6. Graphical animation of the travelling gantry crane.- 7. Fuzzy control of the gantry.- 8. RST and LQI controllers.- 8.1 Discrete model of the gantry.- 8.2 RST control law.- 8.2.1. RST monovariable control of the truck position.- 8.2.2. Multivariable RST control of the travelling gantry crane.- 8.3. LQI mono variable control of the cart position.- Application 6: Hands-free telephone.- 1. Programming Adaline using MATLAB® commands.- 2. Using S-function in a SIMULINK® model.- Application 7: Echo cancellation on a transmission line.- 1. Transmission line modelling.- 2. LMS filtering, lms1 S-function.- 3. RLS filtering, rlsl S-function.- Application 8 : Noise elimination in a conduit.- 1. Conduit modelling.- 2. LMS filtering, lms2 S-function.- 3. RLS filtering, rls2 S-function.- 4. Composite noise filtering.- Application 9 : Equalisation of a symmetrical binary channel.- 1. Generation of a random binary sequence.- 2. The dispersion Channel.- 3. Symmetrical channel equaliser.- 4. Use with SIMULINK®.- 4.1. S-function transmission channel.- 4.2. S-function LMS type adaptive equalizer.- 4.3. Simulation results.- Appendix 1 : S-functions under SIMULINK® 3.- 1. S-functions functioning principle under SIMULINK® 3.- 2. Various stages of the simulation.- 3. S-function creation through a M-file call.- 3. S-function creation through a C MEX file call.- Appendix 2 : Masking a set of blocks in SIMULINK® 3.- 1. Damped sinusoidal generator.- 2. Pseudo-random binary sequence generator (PRBS).