A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control
Abstract
:1. Introduction
- Taylor expansion was used to solve kinematics singular solution problems.
- Universal dynamic modeling for the three-chamber continuous pneumatic manipulator is presented based on the modal method.
- A hybrid controller integrating the model predictive control method and iterative learning control method was proposed.
2. System Description
3. System Modeling
3.1. Kinematic Modeling
3.2. Dynamic Modeling
4. Control Algorithm
4.1. Parameters of the Control Model
4.2. Hybrid Controller
4.3. Model Parameters Iterative Learning Law
4.4. Parameter-Adaptive-Learning Control Algorithm
Algorithm 1. Model parameters learning adaptive control |
Step1: Initialize the model parameter , Plan length trajectory ; |
Step2: Obtain the initial pressure based on MPC; |
Step3: Compare with length tolerance and . |
Step4: Obtain trough model parameters iterative learning law and MPC |
Step5: Obtain based trough ILC |
Step6: Obtain total input pressure , real chamber length and model estimated length trough real system and estimated model, respectively. Then go to step 3. |
5. Simulation
5.1. Control Algorithm Simulation
5.2. Comparison with Traditional Model-Free Algorithms
5.3. Effect of Initial Model Parameter Value
5.4. Effect of Reference Length Change Trajectory
6. Experiments
6.1. Verification Experiment of the Control Algorithm for Single-Chamber Manipulator
6.2. Verification Experiment of the Control Algorithm for Multi-Chamber Manipulator
6.3. Trajectory Tracking Experiment for the Multi-Chamber Manipulator
6.4. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Robot Mass m | Chamber Length L0 | |||
---|---|---|---|---|
0.006 | 0.03 | 0.15 | 400 N/m | 0.1 N/rad |
Min chamber length | 0 m | Feedforward input coefficient | 10 |
Max chamber length | 0.04 m | Sampling time | 0.1 s |
Output weight factor Q | 100 | Total time | 10 s |
Input weight factor R | 1 | Error tolerance | 0.001 |
Prediction horizon H | 10 | Error tolerance | 0.001 |
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Dai, Y.; Deng, Z.; Wang, X.; Yuan, H. A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control. Sensors 2023, 23, 1272. https://doi.org/10.3390/s23031272
Dai Y, Deng Z, Wang X, Yuan H. A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control. Sensors. 2023; 23(3):1272. https://doi.org/10.3390/s23031272
Chicago/Turabian StyleDai, Yicheng, Zhihao Deng, Xin Wang, and Han Yuan. 2023. "A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control" Sensors 23, no. 3: 1272. https://doi.org/10.3390/s23031272
APA StyleDai, Y., Deng, Z., Wang, X., & Yuan, H. (2023). A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control. Sensors, 23(3), 1272. https://doi.org/10.3390/s23031272