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The Value of Planning for Infinite-Horizon Model Predictive Control
Model Predictive Control (MPC) is a classic tool for optimal control of ...
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Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predi...
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Robust Learning-based Predictive Control for Constrained Nonlinear Systems
The integration of machine learning methods and Model Predictive Control...
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Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
This work presents an explicit-implicit procedure that combines an offli...
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A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filter
Model predictive control (MPC) has become one of the well-established mo...
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MPC for Humanoid Gait Generation: Stability and Feasibility
We present a novel MPC framework for humanoid gait generation which inco...
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Learning from the Hindsight Plan -- Episodic MPC Improvement
Model predictive control (MPC) is a popular control method that has prov...
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Neural Lyapunov Model Predictive Control
This paper presents Neural Lyapunov MPC, an algorithm to alternately train a Lyapunov neural network and a stabilising constrained Model Predictive Controller (MPC), given a neural network model of the system dynamics. This extends recent works on Lyapunov networks to be able to train solely from expert demonstrations of one-step transitions. The learned Lyapunov network is used as the value function for the MPC in order to guarantee stability and extend the stable region. Formal results are presented on the existence of a set of MPC parameters, such as discount factors, that guarantees stability with a horizon as short as one. Robustness margins are also discussed and existing performance bounds on value function MPC are extended to the case of imperfect models. The approach is tested on unstable non-linear continuous control tasks with hard constraints. Results demonstrate that, when a neural network trained on short sequences is used for predictions, a one-step horizon Neural Lyapunov MPC can successfully reproduce the expert behaviour and significantly outperform longer horizon MPCs.
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