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Updated: Jul 3, 2026
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
Control of nonaffine nonlinear discrete-time systems using reinforcement-learning-based linearly parameterized neural
Qinmin Yang1, Jonathan Blake Vance, S Jagannathan
1Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409-0040, USA.
Summary
A novel actor-critic neural network (NN) controller is developed for nonaffine discrete-time systems. This method enables near-optimal control and ensures system stability without requiring an affinelike representation.
Area of Science:
- Control Engineering
- Artificial Intelligence
- Nonlinear System Dynamics
Background:
- Nonaffine discrete-time systems present challenges for control due to unknown nonlinear dynamics.
- Existing methods often require complex system transformations, limiting applicability.
Purpose of the Study:
- To develop a reinforcement-learning-based neural network (NN) controller for general nonaffine nonlinear discrete-time systems.
- To achieve near-optimal control for trajectory tracking while minimizing a cost function.
- To ensure closed-loop system stability using Lyapunov methods.
Main Methods:
- A nonlinear autoregressive moving average with exogenous input (NARMAX) model is considered.
- An actor-critic NN architecture with a critic NN and an action NN is employed.
- Online weight tuning and Lyapunov stability analysis are utilized.
Main Results:
- A supervised actor-critic NN controller scheme is successfully developed.
- The controller is applicable to general nonaffine nonlinear discrete-time systems.
- Simulation results confirm satisfactory controller performance and closed-loop stability.
Conclusions:
- The proposed actor-critic NN controller effectively manages nonaffine nonlinear discrete-time systems.
- The method bypasses the need for an affinelike system representation.
- This approach offers a robust solution for complex control problems.
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