Optimal Energy Shaping via Neural Approximators
This work addresses a gap in control theory for engineers by providing a more systematic approach to performance tuning in passive control frameworks, though it appears incremental as it builds on classical methods.
The paper tackles the lack of systematic performance tuning in passivity-based control by framing energy-shaping control as an optimal control problem, using neural networks and gradient optimization to derive solutions, and validates it on state-regulation tasks with unspecified numerical results.
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally been claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach to adjust performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks.