Optimal Control and Estimation Strategies for Nonlinear \nand Switched Systems
Bibliographic record
Abstract
This dissertation includes two main parts. In the first part, the main contribution is \nto use an inverse optimality approach to analytically solve the Hamilton-Jacobi-Bellman \nequation of a third order nonlinear optimal control problem for which the dynamics are \naffine and the cost is quadratic in the input. One special advantage of this work is that \nthe solution is directly obtained for the control input without finding a value function \nfirst. However, the value function can be obtained after one solves for the control input \nand it is shown to be at least a local Lyapunov function. Furthermore, the developed \ncontroller is combined with a Continuous-Discrete Extended Kalman Filter (CDEKF) as \nan approach to deal with noisy measurements and provide an estimate of the states for \nfeedback. The proposed technique is illustrated by its application to a path following \nproblem of a Wheeled Mobile Robot (WMR). \nThe main contribution of the second part of this thesis is the development of two \nrecursive state estimation algorithms for discrete-time piecewise affine (PWA) singular \nsystems with simulation evidence that the idea works for both uncorrelated and correlated \nprocess and measurement noise. The proposed algorithms are derived based on successive \nQR decompositions and Maximum Likelihood (ML) estimation theory. Numerical examples \nare presented for the case of a PWA system with an unknown input, transformed to a \nPWA singular system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".