Constrained Policy Iteration for Safe Data-Driven Machine Control
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Solution Overview
Problem
Conventional reinforcement learning methods are not suitable for data-driven control of constrained systems as they fail to guarantee state and input constraint satisfaction in continuous state-action spaces, leading to potential system instability and constraint violations.
Innovation Solution
The development of a model-free data-driven control system that formulates the control problem as a constrained control problem within a constraint admissible invariant set (CAIS), using reinforcement learning principles to iteratively update the control policy and CAIS, ensuring constraint satisfaction without requiring a dynamical model of the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional reinforcement learning methods are used for data-driven control, then the control policy can be learned from operational data, but state and input constraint satisfaction cannot be guaranteed
Solution Approach 1:
The control problem is segmented into two distinct components: (1) learning the system dynamics model from operational data, and (2) solving the constrained control problem using the learned model. This segmentation allows each component to be optimized independently, with the model learning phase focusing on accuracy and the control synthesis phase focusing on constraint satisfaction.
Solution Approach 2:
A learned system dynamics model serves as an intermediary between the operational data and the constrained control policy. The model acts as a bridge that translates raw operational data into a form that can be used for reliable constrained control, enabling constraint satisfaction guarantees while maintaining data-driven adaptability.
2Measurement precision
If large quantities of data are used in the model-building phase, then the control policy accuracy is improved, but the data requirement increases significantly
Solution Approach 1:
The method uses partial action by leveraging existing operational data that is already being collected during normal system operation. Instead of requiring extensive dedicated data collection, the approach utilizes the data that would be generated anyway, reducing the additional data burden while still achieving accurate control policies through iterative refinement.
Solution Approach 2:
The system performs self-service by continuously learning from its own operational data during normal operation. The control policy is refined using the data generated by the system itself, eliminating the need for separate, large-scale data collection phases and enabling continuous improvement without external data inputs.
3Quantity of substance
If direct control policies are constructed from data without intermediate model-building, then the data requirement is reduced, but handling state and input constraints becomes difficult
Solution Approach 1:
The learned system dynamics model serves as an intermediary that simplifies constraint handling. By having an explicit model of system behavior, the control synthesis can use model predictive control or similar techniques to systematically handle state and input constraints, avoiding the complexity of directly learning constrained policies from data.
Solution Approach 2:
The system performs preliminary action by first learning the dynamics model before solving the constrained control problem. This preliminary modeling step prepares the system by capturing essential dynamics, making the subsequent constrained control synthesis more tractable and less complex than attempting to learn constrained policies directly.
4Measurement precision
If boundary-valued control inputs are used to better learn the system, then the learning effectiveness is improved, but the risk of violating state constraints increases
Solution Approach 1:
The learned system dynamics model provides beforehand cushioning by predicting the consequences of boundary-valued control inputs before they are applied. The model allows the control synthesis to anticipate potential constraint violations and adjust the control inputs accordingly, enabling effective learning exploration while preventing harmful constraint violations.
Solution Approach 2:
The system uses feedback from the learned model to guide the selection of control inputs during the learning phase. By continuously monitoring predicted outcomes through the model, the system can feedback-adjust control inputs to maintain learning effectiveness while staying within safe operating boundaries, preventing constraint violations.
Data Source
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AI summary
A machine subject to state and control input constraints is control, while the control policy is learned from data collected during an operation of the machine. To ensure satisfaction of the constraints, the state of machine is maintained within a constraint admissible invariant set (CAIS) satisfying the constraints and the machine is controlled with corresponding control policy mapping a state of the system within the CAIS to a control input satisfying the control input constraints. The machine is controlled using a constrained policy iteration, in which a constrained policy evaluation updates CAIS and value function and a constrained policy improvement updates control policy that improves the cost function of operation according to the updated CAIS and the corresponding updated value function.