• Corpus ID: 250264149

Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control

  title={Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control},
  author={Fangyu Wu and Guanhua Wang and Siyuan Zhuang and Kehan Wang and Alexander Keimer and Ionut Alexandru Stoica and Alexandre M. Bayen},
—Model predictive control (MPC) is a powerful con- trol method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magni- tude. Nevertheless, explicit MPC often requires expensive pre-computation and… 



Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction

This work proposes a novel projection-based strategy that is capable of providing a certificate of robust feasibility and input-to-state stability in real-time and shows how this projection operator can be formulated as a parametric quadratic program that is solvable offline.

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

This work proposes a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction and is the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

Stability and feasibility of neural network-based controllers via output range analysis

  • B. KargS. Lucia
  • Computer Science
    2020 59th IEEE Conference on Decision and Control (CDC)
  • 2020
This paper introduces a parametric description of the neural network controller and uses a mixed-integer linear programming formulation to perform output range analysis of neural networks and proposes a novel method to modify a neural network Controller such that it performs optimally in the LQR sense in a region surrounding the equilibrium.

Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks

A novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning that not only learns the control policy, but also a “certificate policy”, that allows us to estimate the sub-optimality of the learned control policy online, during execution-time.

Predictive Control for Linear and Hybrid Systems

Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

High-Frequency Nonlinear Model Predictive Control of a Manipulator

This paper presents the first hardware implementation of closed-loop nonlinear MPC on a 7-DoF torque-controlled robot and leverages a state-of-the art optimal control solver, namely Differential Dynamic Programming (DDP), in order to replan state and control trajectories at real-time rates (1kHz).

The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamic Systems

A method to learn accurate safety certificates for nonlinear, closed-loop dynamical systems by constructing a neural network Lyapunov function and a training algorithm that adapts it to the shape of the largest safe region in the state space.

A receding-horizon regulator for nonlinear systems and a neural approximation