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Neural critic learning with accelerated value iteration for nonlinear model predictive control.

Peng Xin1, Ding Wang1, Ao Liu1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China; Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing 100124, China.

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Summary
This summary is machine-generated.

This study introduces the accelerated value iteration predictive control (AVI-PC) algorithm to solve complex nonlinear model predictive control (NMPC) problems. The novel AVI-PC algorithm enhances optimization efficiency for industrial processes.

Keywords:
Accelerated mechanismAdaptive critic designsNeural networksNonlinear model predictive controlValue iteration

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Area of Science:

  • Control Engineering
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Nonlinear Model Predictive Control (NMPC) presents significant computational challenges in practical industrial applications.
  • Existing methods for solving the receding optimization problem in NMPC are often complex and inefficient.

Purpose of the Study:

  • To develop an efficient algorithm for solving the receding optimization problem in NMPC.
  • To introduce the Accelerated Value Iteration Predictive Control (AVI-PC) algorithm based on adaptive dynamic programming.

Main Methods:

  • The AVI-PC algorithm integrates iterative learning with the receding horizon mechanism of NMPC.
  • It utilizes a single critic network with multiple linear regression for approximating the accelerated value iterative function.
  • Convergence and admissibility conditions are established and analyzed.

Main Results:

  • The AVI-PC algorithm provides a novel pattern for receding optimization within each prediction horizon.
  • Convergence and admissibility properties are analyzed under specific accelerated factor conditions.
  • Simulation experiments demonstrate the effectiveness and progressiveness of the AVI-PC algorithm.

Conclusions:

  • The AVI-PC algorithm offers an effective solution for the computationally intensive receding optimization problem in NMPC.
  • The proposed method shows significant potential for improving the performance of industrial control systems.
  • The integration of adaptive dynamic programming and NMPC provides a promising direction for advanced control strategies.