An extension of linear-quadratic regulator trend to determine near optimal performance of nonlinear systems using evolutionary algorithms
Bibliographic record
Abstract
The optimal control theory is focused on operating dynamic systems at minimum cost where cost would be defined as a function of time, the control effort, or a combination of both. Linear-quadratic regulator (LQR), as one of the well-known methods in this field, deals with obtaining an optimum control input for linear systems. In this study, we have proposed a novel method to employ the linear-quadratic regulator solution of a linearised system towards determining near-optimal performance for the corresponding nonlinear system. The LQR solution is used in this method to determine either the starting point or boundaries of the search domain. Next, an optimisation technique such as particle swarm optimisation (PSO), genetic algorithm (GA) or ant colony optimisation (ACO) can be used to find the near-optimal parameters for the employed controller unit. It should be noted that the controller unit can operate based on any modern control concept such as sliding mode control (SMC) or primitive partial-integral-derivative (PID) control commonly used in industrial applications for the ease-of-use and reliability it provides. Performance of the proposed technique is evaluated for the attitude control of a flexible micro-satellite. Numerical simulations are employed in conjunction with experimental results from a hardware-in-the-loop (HITL) test-bed. Results show superior performance of the proposed methodology compared to existing literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".