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Intelligent vs Classical Control Bax Smith EN9940 Today’s Topics Distinguishing Between Intelligent and Classical Control Methods of Classical Control Methods of Intelligent Control Applications for Both Types of Control Discussion Distinguishing b/w Intelligent and Classical Control Classical Control The Mathematicians Approach – Rigidly Modeled System Software does what it is told – Intelligence comes from the Designer Intelligent Control The Lazymans Approach – System not Rigidly Modeled Software does what it wants to – Intelligence comes from the Software Shifting Intelligence Classical Control Designer Software Increasing Intelligence Designer Intelligent Control Software Methods for Classical Control Open-Loop Control System Closed-Loop Control System System Modeling First-Order System: Second-Order System: Classical Control Examples PID Control Optimal Control Discrete-Event Control Hybrid Control PID Control Proportional Control – Integral Control – Pure gain adjustment acting on error signal Adjust accuracy of the system Derivative Control – Adjust damping of the system PID Control t de(t ) m(t ) K p e(t ) K I e( )d K D dt 0 KI GC (s) K p KDs s Optimal Control (LQR) Optimal Control (LQR) Inverted Pendulum Inverted Pendulum Model Methods for Intelligent Control Intelligent Control Examples Fuzzy Logic Control Neural Network Control Genetic Programming Control Support Vector Machines Numerical Learning COMDPs - POMDPs No System Modeling Software learns system model Fuzzy Logic Control Multi-valued Logic – – Rather warm/pretty cold vs hot/cold Fairly dark/very light vs Black/White Apply a more human-like way of thinking in the programming of computers Sets Set A = {set of young people} = [0,20] Is somebody on his 20th birthday young and right on the next day not young? Fuzzy Sets Fuzzy Example – Inverted Pendulum Fuzzy Rules If angle is zero and angular velocity is zero then speed shall be zero If angle is zero and angular velocity is pos. low then speed shall be pos. low … Actual Values Neural Network Control Mimic Structure and Function of the Human Nervous System Biological Neurons Dendrites – – Synapses – Connects neurons Modify signals Connects Dendrites Neuron – – Emits a pulse if input exceeds a threshold Stores info in weight patterns Mathematical Representation of a Neuron Back-Propagation Neural Network Training a Neural Network Analogous to teaching a child to read – – – Present some letters and assign values to them Don’t learn first time, must repeat training Knowledge is stored by the connection weights Minimize the error of the output using LMS algorithm to modify connection weights Genetic Programming Control Output of Genetic Programming is another computer program! Genetic Programming Steps Generate a random group of functions and terminals (programs) – – Execute each program assigning fitness values Create a new population via: – – – Functions: +, -, *, /, etc… Terminals: velocity, acceleration, etc… Mutation Crossover Most fit Which ever program works best is the result Crossover Operation Mutation Operation Applications In general, – Use Classical Control (Intelligent Control can take long to train) If problem too complex – Use Intelligent Control Discussion