Modeling and Control of Aerial Manipulation Systems: From Conventional to Continuum Manipulation
Bibliographic record
Abstract
<p>Aerial manipulation systems (AMSs) are highly coupled nonlinear systems which have attracted significant attention of researchers and industries due to their applications. However, the progress has been slow in part due to the extreme level of nonlinearities which makes their modeling and control quite challenging. </p> <p>In the first phase, to get insight into the dynamics and control of AMSs, conventional AMSs with rigid-link arms were modeled. Next, different control approaches for conventional AMSs control were proposed and the behavior of the system in the presence of different control schemes was compared in terms of accuracy, efficiency, stability and robustness. Four proposed control methods for conventional AMSs included (i) inverse dynamic, (ii) hierarchical linear-quadratic regulator (LQR), (iii) sliding mode, and (iv) semi-optimal nonlinear control techniques. Based on this preliminary study, a controller was selected for its formulation for the next phase of the project. </p> <p>In the next phase, the research focused on modeling and control of aerial continuum manipulation systems (ACMSs) that are distinguished from conventional aerial manipulation systems (AMSs). In ACMS, typical rigid-link arms are replaced with continuum robotic arms to boost their advantages. Using continuum arm extends the capability of AMSs by increasing their compliance and dexterities. Also, AMSs with continuum arms are more compatible to work in cluttered and less structured environments. However, modeling and control of such complex and nonlinear system is much more challenging compared to those of conventional rigid AMSs. </p> <p>The reported research in this thesis is continuation of the ACMS initiative in Robotics, Mechatronics and Automation Laboratory at Ryerson University. In this research, a decoupled model for ACMS is formulated for the first time followed by a decoupled control technique for this system. Cosserat rod theory was adopted for decoupled dynamic modeling of ACMS. Also, a robust adaptive control approach was proposed to cope with the problem of complexity and high level of modeling uncertainties. The stability of the proposed control method was proven using Lyapunov stability theorem. </p> <p>Subsequently, to consider interactions between aerial vehicle and continuum arm, coupled model and control for ACMS were developed. Coupled dynamic modeling for ACMSs was formulated based on Euler-Lagrange theory. For this purpose, a general vertical take-off and landing (VTOL) vehicle equipped with a tendon-driven continuum arm was considered. The modeling approach was complemented with a control technique to demonstrate the validity of the proposed method for such a complex system. Both simulation and experimental results were reported to verify the effectiveness of the proposed modeling technique. </p> <p>Finally, design of the first vision-based adaptive control for ACMSs circumventing the need for a priori knowledge of system dynamic model was proposed. For this purpose, a vision based reduced-order adaptive control scheme was developed. It was shown that using vision feedback in combination with adaptive control method enables effective treatment of nonlinearities, coupling and uncertainties present in typical ACMSs. The method was verified using simulation results. </p>
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".