Total Energy Shaping with Neural Interconnection and Damping Assignment -- Passivity Based Control
This work addresses control design for mechanical systems, but it is incremental as it applies existing neural network techniques to a known control framework without broad new applications.
The paper tackled the problem of designing passivity-based control for mechanical systems by transforming the interconnection and damping assignment method into a supervised learning problem using neural networks, and validated it via numerical simulations for systems with one and two degrees-of-freedom.
In this work we exploit the universal approximation property of Neural Networks (NNs) to design interconnection and damping assignment (IDA) passivity-based control (PBC) schemes for fully-actuated mechanical systems in the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC method into a supervised learning problem that solves the partial differential matching equations, and fulfills equilibrium assignment and Lyapunov stability conditions. A main consequence of this, is that the output of the learning algorithm has a clear control-theoretic interpretation in terms of passivity and Lyapunov stability. The proposed control design methodology is validated for mechanical systems of one and two degrees-of-freedom via numerical simulations.