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| en:safeav:curriculum:ctrl-b [2025/11/04 14:43] – raivo.sell | en:safeav:curriculum:ctrl-b [2025/11/05 09:07] (current) – airi |
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| ^ **ECTS credits** | 1 ECTS | | ^ **ECTS credits** | 1 ECTS | |
| ^ **Study forms** | Hybrid or fully online | | ^ **Study forms** | Hybrid or fully online | |
| ^ **SModule aims**S | The aim of the module is to introduce control and planning methods for autonomous systems. The course develops students’ ability to design and analyse feedback control, motion planning and decision-making algorithms that generate safe and reliable vehicle behaviour in dynamic environments, using both classical and modern AI-based approaches. | | ^ **Module aims** | The aim of the module is to introduce control and planning methods for autonomous systems. The course develops students’ ability to design and analyse feedback control, motion planning and decision-making algorithms that generate safe and reliable vehicle behaviour in dynamic environments, using both classical and modern AI-based approaches. | |
| ^ **SPre-requirements**S | Basic knowledge of linear algebra, differential equations and control theory, as well as programming skills in Python or C++. Familiarity with system dynamics, robotics or numerical tools (e.g. MATLAB/Simulink or ROS) is recommended but not mandatory. | | ^ **Pre-requirements** | Basic knowledge of linear algebra, differential equations and control theory, as well as programming skills. Familiarity with system dynamics, robotics or numerical tools (e.g. MATLAB/Simulink) is recommended but not mandatory. | |
| ^ **Learning outcomes** | **Knowledge**\\ • Explain classical control principles and their application to vehicle dynamics.\\ • Describe AI-based control methods, including reinforcement learning and neural network controllers.\\ • Understand motion planning and behavioral algorithms\\ • Discuss safety verification, validation, and certification issues for autonomous control systems.\\ **Skills**\\ • Design, simulate, and tune classical controllers for trajectory tracking and stabilization.\\ • Implement basic reinforcement learning or hybrid control strategies in simulation environments.\\ • Develop motion planning pipelines integrating perception, planning, and control layers.\\ **Understanding**\\ • Recognize trade-offs between transparency, performance, and adaptability in control architectures.\\ • Evaluate robustness, explainability, and ethical implications in AI-driven control.\\ • Appreciate interdisciplinary approaches to achieve safe and reliable autonomous operation. | | ^ **Learning outcomes** | **Knowledge**\\ • Explain classical control principles and their application to vehicle dynamics.\\ • Describe AI-based control methods, including reinforcement learning and neural network controllers.\\ • Understand motion planning and behavioral algorithms\\ • Discuss safety verification, validation, and certification issues for autonomous control systems.\\ **Skills**\\ • Design, simulate, and tune classical controllers for trajectory tracking and stabilization.\\ • Implement basic reinforcement learning or hybrid control strategies in simulation environments.\\ • Develop motion planning pipelines integrating perception, planning, and control layers.\\ **Understanding**\\ • Recognize trade-offs between transparency, performance, and adaptability in control architectures.\\ • Evaluate robustness, explainability, and ethical implications in AI-driven control.\\ • Appreciate interdisciplinary approaches to achieve safe and reliable autonomous operation. | |
| ^ **Topics** | 1. Classical Control Strategies:\\ – Feedback control fundamentals, PID design and tuning, LQR, Sliding Mode Control.\\ – Model Predictive Control (MPC) and real-time optimization.\\ 2. AI-Based Control Strategies:\\ – Reinforcement learning for control, supervised imitation learning.\\ – Neural network controllers and hybrid architectures.\\ 3. Integration and Safety:\\ – Verification, validation, and certification of control systems.\\ – Robustness, interpretability, and failure handling.\\ 4. Motion Planning and Behavioral Algorithms:\\ – FSMs, Behavior Trees, and rule-based systems.\\ – Planning methods: A*, D*, RRT, RRT*, and MPC-based trajectory generation.\\ – Predictive and optimization-based planning for dynamic environments.\\ 5. Future Trends:\\ – Explainable AI control, safe RL, and human-like behavioral models. | | ^ **Topics** | 1. Classical Control Strategies:\\ – Feedback control fundamentals, PID design and tuning, LQR, Sliding Mode Control.\\ – Model Predictive Control and real-time optimization.\\ 2. AI-Based Control Strategies:\\ – Reinforcement learning for control, supervised imitation learning.\\ – Neural network controllers and hybrid architectures.\\ 3. Integration and Safety:\\ – Verification, validation, and certification of control systems.\\ – Robustness, interpretability, and failure handling.\\ 4. Motion Planning and Behavioral Algorithms:\\ – FSMs, Behavior Trees, and rule-based systems.\\ – Planning methods: A*, D*, RRT, RRT*, and MPC-based trajectory generation.\\ – Predictive and optimization-based planning for dynamic environments.\\ 5. Future Trends:\\ – Explainable AI control, safe RL, and human-like behavioral models. | |
| ^ **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation | | ^ **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation | |
| ^ **Learning methods** | **Lecture** — Introduce theoretical and mathematical foundations of classical and AI-based control strategies.\\ **Lab works** — Implement and compare controllers (PID, LQR, RL) and motion planners (A*, RRT) using simulation tools such as low-fidelity planning simulators, or MATLAB/Simulink.\\ **Individual assignments** — Design a control or planning pipeline and evaluate safety/performance trade-offs.\\ **Self-learning** — Independent exploration of open-source control frameworks and reading of selected research literature. | | ^ **Learning methods** | **Lecture** — Introduce theoretical and mathematical foundations of classical and AI-based control strategies.\\ **Lab works** — Implement and compare controllers (PID, LQR, RL) and motion planners (A*, RRT) using simulation tools such as low-fidelity planning simulators, or MATLAB/Simulink.\\ **Individual assignments** — Design a control or planning pipeline and evaluate safety/performance trade-offs.\\ **Self-learning** — Independent exploration of open-source control frameworks and reading of selected research literature. | |
| ^ **Recommended tools and environments** | FSM, Behavior Trees, A*, RRT, MPC | | ^ **Recommended tools and environments** | FSM, Behavior Trees, A*, RRT, MPC | |
| ^ **Verification and Validation focus** | | | ^ **Verification and Validation focus** | | |
| ^ **Relevant standards and regulatory frameworks** | | | ^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 21448 (SOTIF), SAE J3016 | |
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