Course - Advanced Guidance, Navigation and Control - TK8109
TK8109 - Advanced Guidance, Navigation and Control
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Passed/Failed
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Report | 60/100 | |||
| School exam | 40/100 | 2 hours | E |
Course content
The course is given every second year, next time Fall 2021.
Guidance, navigation and control (GNC) systems for ships, aircraft, spacecraft and unmanned vehicles including AUV, UAV and USV systems. The course is given as three modules:
- Guidance systems and path planning. Determination of the desired path of travel from the vehicle's current location to a designated target, as well as desired changes in velocity, rotation and acceleration for following that path. Introduction to motion planning based on computational geometry (CG) and optimal control (OC). Analysis of path properties relevant to robotic applications. Conventional path-following algorithms (Dubins paths, clothoids, Pythagorean Hodographs, Fermat’s spirals, splines) followed by state-of-the-art hybrid solutions, which combine CG and OC. Path planning, guidance and control in a a cascaded-systems perspective. Brief overview of line-of-sight (LOS) guidance laws and their variations. Alternative guidance and control architectures, which combines reinforcement learning with optimal control, as well as deep reinforcement learning. Incorporation of collision avoidance algorithms in the aforementioned architectures.
- Navigation systems. Methods for determination of the vehicle's location and attitude with emphasis on advanced inertial navigation systems (INS). Mathematical models for inertial sensors error characteristics including noise, bias, scale factor and cross-coupling errors. Vibration induced inertial measurement errors. Coning and sculling. Angular velocity and specific force sensor outputs versus incremental sensor outputs. Anti-sculling and anti-coning algorithms. Position and attitude representations. Strapdown equations and accurate numerical implementations. INS aiding techniques and sensors. Error state and extended Kalman-filter formulations for integrated navigation systems. Complementary and nonlinear-observer-based navigation filters. Exogenous Kalman filters (XKF) and Invariant extended Kalman filters for navigation.
- Control systems. Advanced motion control systems for vehicles with focus on watercraft and aircraft. Methods for nonlinear and adaptive autopilot design. Robust motion control systems including conventional sliding mode control and super-twisting adaptive sliding mode control. Hybrid MPC and reinforced learning (RL). Nonlinear attitude control on SO(3).
Learning outcome
KNOWLEDGE: In-depth knowledge of design and analysis of GNC systems. Focus is placed on path planning, guidance laws and state estimators for navigation systems. This includes inertial navigation systems and aiding techniques. GNC architectures for watercraft, aircraft and unmanned vehicles. Knowledge of inertial sensors and global navigation systems. SKILLS: Be able to model, simulate and implement GNC systems for unmanned underwater vehicles and aerial vehicles, ships, aircraft and satellites. Understand how Kalman filters and nonlinear observers are used to estimate position, velocity and attitude of moving objects. GENERAL COMPETENCE: Skills in applying this knowledge and proficiency in new areas and complete advanced tasks and projects. Skills in communicating extensive independent work, and master the technical terms of nonlinear observer theory. Ability to contribute to innovative thinking and innovation processes.
Learning methods and activities
Lectures, study groups and independent study. Mandatory project report (pass/fail).
Recommended previous knowledge
Background in nonlinear systems, dynamic optimization, kinematics, vehicle dynamics and Kalman filtering.
Required previous knowledge
TTK 4150 Nonlinear Systems, TTK4135 Optimization and Control and TTK 4190 Guidance, Navigation and Control of Vehicles or similar background.
Course materials
Lecture notes, conference and journal papers.
No
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: AUTUMN 2021
Language of instruction: English
Location: Trondheim
- Engineering Cybernetics
Department with academic responsibility
Department of Engineering Cybernetics
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD School exam 40/100 E 2021-12-02 09:00 INSPERA
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Room Building Number of candidates SL311 orange sone Sluppenvegen 14 13 - Autumn ORD Report 60/100
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Room Building Number of candidates - Spring ORD School exam 40/100 E INSPERA
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Room Building Number of candidates - Spring ORD Report 60/100
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"