Subject » BMEVIIIMB06
Artifical Intelligence Based Control
Mesterséges intelligencia alapú irányítások
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Mesterséges intelligencia alapú irányítások
Artifical Intelligence Based Control
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| Subject code | BMEVIIIMB06 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Harmati István
position: egyetemi docens
contact:
harmati.istvan@vik.bme.hu
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| Responsible department |
Irányítástechnika és Informatika Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | https://edu.vik.bme.hu/ | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
Detailed topics of the lectures:
1. Basics of fuzzy systems. The concept of the fuzzy system, the theoretical foundations of fuzzy inference. The structure, rule base and algorithm of regulations operating on the fuzzy principle. MacVicar-Whelan meta-rules. (2 weeks)
2. Construction of genetic algorithms. Genetic operators: selection, recombination, mutation, back substitution, migration. Controller design with genetic algorithm. (1 week)
3. Linear and non-linear parameter estimation. Batch and recursive parameter estimation procedures for linear and non-linear system models. (1 week)
4. Clustering procedures. Theoretical foundations of grid partitioning, subtractive clustering, fuzzy c-mean clustering, structure of algorithms (1 week).
5. Construction of feedforward shallow neural networks, learning by error backpropagation. Basics of deep learning methods. Autoencoders, stochastic neural networks, convolutional networks in control tasks. Feedback (RNN, LSTM) networks for solving dynamic tasks (2 weeks)
6. Identification with adaptive Neuro-fuzzy systems, structure of the method, tuning rules, ANFIS. (1 week)
7. Adaptive fuzzy control. Nominal and supervisory control design, indirect (based on a model) and direct (not using a model) adaptive control, stability testing and parameter tuning rules. (2 weeks)
8. Basics of reinforcement learning. Prediction and control of known and unknown/large Markov decision processes: Dynamic programming, Monte Carlo, Temporal Difference based learning, Sarsa, Q-learning. Basics of deep reinforcement learning: DQN, REINFORCE, Actor-Critic networks in prediction and control. (2 weeks)
9. Swarm intelligence methods. Construction of ant colony algorithms and their applications for solving discrete optimization problems. The theoretical background of particle swarm optimization and the steps of the algorithm. Optimization based on swarm intelligence methods, system identification and control design. (1 week)
The detailed topics of the exercises:
1. Fuzzy controller design in Matlab-Simulink environment using Fuzzy Toolbox. (1 week)
2. Determination of PID controller parameters using genetic algorithm in Matlab-Simulink environment (1 week)
3. System identification with linear and non-linear parameter estimation in the Matlab environment (1 week)
4. Implementation of clustering and ANFIS methods in Matlab environment. (1 week)
5. Prediction of failure of railway safety equipment using Matlab Deep Learning Toolbox. (1 week)
6. Control of a nonlinear system with reinforcement learning in the Matlab environment using the Reinforcement Learning Toolbox. (2 weeks)
The aim of the course is for students to gain knowledge about the latest methods of controlling and identifying complex systems using artificial intelligence methods, which are also used in practice. Students will learn about the concept and theoretical background of the following most common artificial intelligence methods:
• Fuzzy systems
• Genetic algorithms
• Neural networks
• Neuro-fuzzy systems
• Swarm intelligence methods
• Reinforcement learning
The subject shows how the above-mentioned methods can be used primarily (but not exclusively) to solve control engineering, system modeling and optimization problems using modern computer science-supporting programming platforms (mainly MATLAB).
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
The course material is presented in lectures and exercises. Lectures and exercises alternate at the pace of the material. In the exercises, the theoretical material presented in the lectures is deepened in the form of calculation examples and case studies.
Tanulástámogató anyagok
Online források
B. Lantos: Fuzzy systems and genetic algorithms, 2002, Műegyetemi kiadó; M. Dorigo: Ant Colony Optimization, MIT University Press Ltd., 2004; R. S. Sutton, A. G. Barto: Reinforcement Learning: An introduction, MIT Press, 2018; P. Kim: MATLAB Deep Learning, Apress, 2017
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Mathematics (Linear algebra, analysis, gradient-based numerical optimization), Control technology
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Mathematics (Linear algebra, analysis, gradient-based numerical optimization), Control technology
General rules
Requirements:
In study period: The knowledge of the course material is measured once during the stuy period with a written midterm in closed form. The condition for obtaining the signature is to pass the midterm at least at a sufficient level.
In exam period: Obtaining the signature is a condition for admission to the exam. The exam consists of a written performance evaluation and the the result achieved in the midterm. There is no way to improve the result of midterm during the exam period.
The grade obtained for the subject is determined 20% from the result (score) achieved in the midterm and 80% from the exam.
Additional possibilities:
There is one retake in the study period or on the retake week.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.