Courses

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MO89905: Learning-Based Control for Mobility Systems

• This course develops learning-based control through exact and approximate dynamic programming (DP). Approximate DP provides a common framework for interpreting reinforcement learning and connecting learned values, policies, and models to LQR, MPC, and rollout. A central emphasis is online improvement: using lookahead and optimization to improve a learned controller during execution. Modern RL algorithms are then interpreted within this framework rather than treated as isolated recipes.

• Course Materials: K. Choi, Learning-Based Control for Mobility Systems, evolving working draft, 2026.

The online version provides the current working draft and may evolve during the course.

For more information, Read Current Working Draft and View Fall 2026 Syllabus (PDF)

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M089902: Optimal Control on Electric Motors

• This course introduces control methods for electric motors, with a particular focus on synchronous motors (e.g., PMSM and SynRM), which are widely used in modern mobility systems. Core topics include motion control (speed and position control), torque generation, and current control of electric drives. The course first presents conventional cascaded drive control structures and their limitations, and then introduces model-based and optimal control approaches, with an emphasis on model predictive control (MPC).

Selected Invited Talks

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xEV Energy Management: From Vehicle Control to Network Intelligence

KIEVC 2026 · Samcheok, South Korea · October 15, 2026 Slides (PDF)

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