Examination arrangement

Course content

The course is given every second year, next time Fall 2023.

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:

1. 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, and splines) are followed by state-of-the-art hybrid solutions, which combine CG and OC. Path planning, guidance and control in a cascaded-systems perspective. A brief overview of line-of-sight (LOS) guidance laws and their variations. Alternative guidance and control architectures combine reinforcement learning with optimal control and deep reinforcement learning. Incorporation of collision avoidance algorithms in the aforementioned architectures.
2. Navigation systems. Methods for determination of the vehicle's location and attitude with an 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.
3. Control systems. Advanced motion control systems for vehicles with a focus on marine craft and aircraft. Successive-loop closure and LOS path-following control. Serret-Frenet coordinates for path following. Target-tracking models for state estimation. Kalman filter design for estimation of SOG, COG, and course rate from GNSS position. Methods for nonlinear autopilot design. Weathervaning control; a passivity approach with the practical implementation of a PID weathervaning controller. Moving mass control for AUVs. Control moment gyroscope (CMG) control is applied to marine craft and satellites. Conventional sliding mode control, super-twisting sliding mode control with extensions to 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 estimating moving objects' position, velocity, and attitude. GENERAL COMPETENCE: Skills in applying this knowledge and proficiency in new areas and completing advanced tasks and projects. Skills in communicating extensive independent work and mastering 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).

Compulsory assignments

• Report

Further on evaluation

A multiple-choice school exam is the basis for the final grade in the subject. In addition, an oral presentation of the project report is mandatory to pass the course. The project report and the result for the written exam are given as 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.