Actor-Critic PID Tuning With Anti-Windup for Stable Adaptive Control
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Solution Overview
Problem
Existing model-based control methods, such as PID and MPC, face performance degradation due to gradual changes in plant dynamics, and model reidentification is costly and time-consuming, while model-free RL approaches still rely on accurate model identification, leading to instability issues when switching control strategies.
Innovation Solution
An actor-critic framework using a neural network-based PID controller with anti-windup tuning, where PID gains are dynamically adjusted through reinforcement learning, allowing for model-free control and stabilization by initializing with stabilizing gains and incorporating anti-windup compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based control methods (PID/MPC) are used, then control performance is maintained under accurate models, but performance degrades when plant dynamics change and model reidentification is costly and time-consuming
Solution Approach 1:
The patent changes the fundamental parameter being controlled from fixed PID gains to dynamically adjustable gains. The reinforcement learning agent continuously learns optimal PID parameters by interacting with the plant, adapting to gradual changes in plant dynamics without requiring explicit model reidentification. This transforms the static parameter set (fixed gains) into dynamic parameters that evolve with plant conditions.
2Adaptability or versatility
If model-free RL approaches are used to dynamically adjust PID gains, then adaptability to plant changes is improved, but closed-loop instability may occur when switching control strategies
Solution Approach 1:
The patent applies preliminary action by pre-initializing the actor-critic RL framework with stabilizing PID gains obtained from traditional tuning methods. This initial stabilizing policy serves as a safe starting point before the RL agent begins exploration. The critic network is initialized with these stabilizing gains, ensuring that the system starts from a known stable configuration and only gradually learns improvements, preventing destabilizing switches.
Solution Approach 2:
The patent implements continuous feedback through the actor-critic architecture where the critic evaluates the current policy's performance and provides guidance to the actor for policy improvement. The feedback loop includes computing temporal difference errors between predicted and actual returns, updating the critic's value function estimates, and using this information to adjust PID gains. This closed-loop feedback ensures stable learning by constantly monitoring system performance and adjusting parameters based on actual outcomes rather than speculative changes.
3Adaptability or versatility
If PID gains are dynamically changed through RL, then control adaptability is improved, but switching between control strategies can destabilize the closed-loop system
Solution Approach 1:
The patent applies dynamics by transforming the static PID controller into a dynamic one where gains adapt continuously through reinforcement learning. The actor-critic framework enables the PID parameters to evolve over time based on real-time performance feedback, allowing the controller to adapt to changing plant conditions while maintaining stability through controlled exploration and exploitation of the parameter space.
Data Source
AI summary
A method and system for reinforcement learning can include an actor-critic framework comprising an actor and a critic, the actor comprising an actor network and the critic comprising a critic network; and a controller comprising a neural network embedded in the actor-critic framework and which can be tuned according to reinforcement learning based tuning including anti-windup tuning.