Course content

The course is given every second year, next time in Fall 2025.

Guidance, navigation, and control (GNC) systems for ships, aircraft, spacecraft, and uncrewed 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 architectures as mentioned above.
2. Navigation systems. Methods for determination of the vehicle's position, velocity, and attitude with an emphasis on advanced inertial navigation systems (INS). Mathematical models for inertial sensor error characteristics include noise, bias, scale factor, cross-coupling errors, vibration-induced inertial measurement errors, coming, and sculling. Inertial sensor outputs. Sampling strategies. Anti-sculling and anti-coning algorithms. Position and attitude representations. Strapdown equations and accurate numerical implementations. INS aiding sensors and techniques. Advanced filters, observers, and estimators for integrated navigation systems.
3. Control systems. Advanced motion control systems for autonomous vehicles, marine craft, and aircraft. Moving mass control and control moment gyros (CMGs) using the principle of conservation of linear and angular momentum. Integral and adaptive gain super-twisting sliding mode control. Successive-loop closure and LOS path-following guidance systems. Extensions to path tracking using particle methods and the Serret-Frenet frame. Compensation of drift in LOS path-following control systems using integral LOS (ILOS). Uniform semiglobal exponential stable adaptive LOS (ALOS) guidance laws for 3-D path following. Target-tracking models for state estimation. Kalman filter design for estimation of speed over ground (SOG), course over ground (COG), and course rate from position measurements. Design of weathervaning control systems taking advantage of the natural environmental forces, wind, waves, and ocean currents to control the vehicle's heading.

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.