• Corpus ID: 231986362

Recurrent Model Predictive Control

  title={Recurrent Model Predictive Control},
  author={Zhengyu Liu and Jingliang Duan and Wenxuan Wang and Shengbo Eben Li and Yuming Yin and Ziyu Lin and Qi Sun and Bo Cheng},
This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), in order to solve large-scale nonlinear finite-horizon optimal control problems. As an enhancement of traditional Model Predictive Control (MPC) algorithms, it can adaptively select appropriate model prediction horizon according to current computing resources, so as to improve the policy performance. Our algorithm employs a recurrent function to approximate the optimal policy, which maps the… 

Figures and Tables from this paper



Fast Model Predictive Control Using Online Optimization

A collection of methods for improving the speed of MPC, using online optimization, which can compute the control action on the order of 100 times faster than a method that uses a generic optimizer.

Polytopic Approximation of Explicit Model Predictive Controllers

  • C. JonesM. Morari
  • Mathematics, Computer Science
    IEEE Transactions on Automatic Control
  • 2010
This paper compute approximate explicit control laws that trade-off complexity against approximation error for MPC controllers that give rise to convex parametric optimization problems.

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

A generic scale reduction framework to reduce the online computational burden of MPC controllers by combining an existing “move blocking ” strategy with a “constraint-set compression” strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon.

Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators

Experimental results show that the proposed NMPC approach for the displacement tracking problem of PEAs has satisfactory modeling and control performance and avoids the inversion imprecision problem encountered in the widely used inversion-based control algorithms.

On the Computation of Linear Model Predictive Control Laws

A neural network model predictive controller

Move blocking strategies in receding horizon control

A Novel Recurrent Neural Network for Manipulator Control With Improved Noise Tolerance

A novel recurrent neural network is proposed to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type and has a low tracking error comparable to noise-free situations.