A practical reinforcement learning control design for nonlinear systems with input and output constraints
Why this work is in the frame
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Bibliographic record
Abstract
In this work, a practically implementable reinforcement learning (RL)-based controller is designed to handle process input and output constraints. In a typical RL problem, an RL agent is employed to learn an optimal control policy through interactions with the environment. This is unimplementable in practical situations due to the excessive exploration needed by the RL-based controller and exacerbated by the possible violation of the input and output constraints. We previously proposed an implementable RL controller that can circumvent random exploration needs by leveraging existing model predictive control (MPC) to pre-train/warm start the RL agent. The pre-trained agent is subsequently employed in real-time to engage with the process to improve its performance by gaining more knowledge about the nonlinear behavior of the system. This work generalizes our previous method to handle constraints on the outputs and the rate of change of the inputs by modifying the reward function. The effectiveness of the proposed algorithm is illustrated through simulations conducted for control of a pH neutralization process. The findings indicate that the proposed RL method enhances closed-loop performance in comparison to the nominal MPC while satisfying all input and output constraints.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it