Intelligent Control Systems

Internal Model Control. Cascade, feedforward, ratio and override control. Multivariable control. Batch processes control. Plantwide control. Process safety. Safety instrumented system. Control engineering projects. Nonlinear systems, Principes of nonlinear systems identification and control; Adaptive control systems; Artificial neural networks. Structures of artificial neural networks and training algorithms; Artificial neural networks based identification of nonlinear systems; Artificial neural networks based control of nonlinear systems; Self-learning neural networks; Artificial neural networks based image recognition and pattern classification; Fuzzy control; Dynamic feedback linearization based control of nonlinear systems; Genetic algorithms and their applications for identification and control of nonlinear systems.

NB! This course will take place in autumn semester 2024/2025 which starts on 2nd of September and ends 26th of January (you can find that information under Start date section). The real course start and end dates will be announced at the beginning of September at the latest.

• Knows about main methods of modeling and control of complex systems, has an overview of practical applications of these methods; • Can analyze and compare different control techniques, estimate limits of their applicability in practice and combine different methods for finding the best solution of a particular problem; • Can design, simulate and analyze behavior of nonlinear systems in MATLAB/Simulink environment; • Knows and can use different artificial neural networks, fuzzy logic, genetic algorithms and fractional order models based control algorithms; • Can design simple safety instrumented systems.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

Knowledge of Modelling and Identification of Dynamic Systems. 10 first applicants will be accepted

• • Huang, S., Tan, K. K., Tang, K. Z. Neural network control : theory and applications, Baldock : Research Studies, 2004;
• • Kevin M. Passino, et al. Fuzzy Control, 1997.;
• • S.N. Sivanandam, S.N. Deepa, Introduction to Genetic Algorithms, Springer-Verlag, 2010
• • Jean Levine, Analysis and Control of Nonlinear Systems, Springer-Verlag, 2009

lectures, practices