Linear Quadratic Optimal Control of Nonlinear Dynamic Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-world systems are inherently nonlinear, making them too difficult to control. This thesis addresses that difficulty by using linear quadratic optimal controllers to efficiently control nonlinear dynamic systems. As such, the Linear Quadratic Regulator (LQR) controller is proposed to control the 3-DOF Helicopter, the Linear Quadratic Gaussian (LQG) controller is proposed for the 6-DOF aircraft landing gear, and the LQR and the LQG controllers are proposed to control the loudspeaker system. The state-space model of each nonlinear dynamic system is derived, and the mathematical models of the control strategies are calculated. The control strategies are also tested under various conditions and compared with an equally simple control strategy, the PID controller, and two quantitative tracking performance metrics are presented; i) the integral of the tracking errors, and ii) the integral of the control signals of the system. The results obtained affirm the robustness and competence of the proposed control strategies.
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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) | 0.001 | 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