The following assumed knowledge is expected for postgraduate students undertaking this course: MMAN3200 and MATH2089 and MTRN2500.
The course is aimed at learning basic and advanced techniques necessary for sensing and control of autonomous mechatronic systems. Contents covered in this course include stochastic processes, state estimation, Sensor data fusion, nonlinear control, optimal control, stochastic control, behaviour-based control, machine learning techniques (genetic algorithms, neural networks, fuzzy logic and reinforcement learning). Half of the course is lecture-based. In the other half, students will program and control autonomous indoor robots.
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