Artifical Intelligence Based Control

14 weeks of classes: 26 hours of lectures + 13 hours of classroom practices. Classroom practices illustrate the methods with application examples. The topics of the lectures are the following:

1. Fundamentals of fuzzy-neural systems. Fuzzy implication, defuzzification, Sugeno-type fuzzy systems.

 

2. The block diagram of Fuzzy Logic Controllers (FLC), the functionality of the blocks, Fuzzy PID and PD controllers.  MacVicar-Whelan meta rules. The rule base design of Fuzzy PD controller. 

 

3. Overview of numerical optimization methods. The necessary analytical condition of the optimal solution considering the constraints. The statement of optimization problem, aczive set, LICQ condition, the Lagrange function of the optimization problem. First order (Karush-Kuhn-Tucker) conditions.

 

4. Optimization methods. Gradient-like, conjugate gradient, quasi Newton methods. Computation of gradient in neural networks. Subtractive clustering, computation of gradient in adaptive networks, ANFIS.

 

5. The architecture of Genetic Algorithms. Linear and nonlinear fitness function, selection, binary and real genetic operators, reinsertation strategies. Multipopulation algorithm. Controller design with genetic algorithm.

 

6. Adptive fuzzy control. Nominal and supervisory control. Indirect (model based) and direct adaptive control. Stability analysis.

 

7. Direct adaptive neural control with full state feedback, adaptive control with neural network based nonlinear observer. Case study: flight control.

 

8. Fuzzy approximation based on SVD. The algorithm, methods to satisfy the mathematical conditions, multivariable extension. Control  design with SVD technique.

 

9. Optimization and control design with evolutionary programming and bacterial algorithms. The algorithms, guzzy interpretation, control design.

 

10. Swarm intelligence. Motiovation, common properties, The definition of swarms and intelliogence. Ant Colony Optimization (ACO). The base of global behavior, the mathematical model of ants. The difference between the real and artificial ants. The role of pheromone. The methaheuristic of ACO.

 

11. Particle swarm optimization (PS). Motiovations, benefits and drawbacks, the concept of PSO, the artificial swarm, optimization algorithm, the motion of particles. Implementation issues, discrete implementation, variants.

 

12.    Deep neural networks. The Architecture of deep neural network, the connection between shallow and deep neural networks. Deep learning methods: autocoders, stochastic neural networks, convolution networks.

 

13.   Learning algorithms.  The fundamentals of reinforcement learning,  Wolf algorithm and its variants. The concept of deep reinforcement learning, The control of multiagent systems with learning algorithms.

 

14.   Lookout. New AI methods in control engoneering.