Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots
Abstract
:1. Introduction
2. Structure of the Competitions
2.1. Computer Vision
2.2. Communication and Triggering
3. DC Motor Mathematical Modeling
- Armature winding: it is located in the rotating part of the DC motor (rotor) and is responsible for producing the torque that moves it and the output voltage when in generator mode.
- Field winding: this is a fixed part that is responsible for the constant magnetic flux passing through the armature; in small DC motors, such as those used in this work, the field winding is often replaced by placing permanent magnets around the armature, which are responsible for generating a constant magnetic field.
- Commutator: It keeps the armature current circulating in the same direction, causing the torque to maintain its direction for a constant input voltage.
- Brushes: these are where the armature winding contacts the power supply.
4. Control Strategies
4.1. Local Discrete PI Controllers
4.2. Gain Scheduling Control
- Obtain a valid representation or approximation of the nonlinear system for each operating point and perform its linearization;
- Apply linear control techniques to develop a controller that meets the needs of the linearized system;
- Choose the architecture of the gain scheduler (rules used in transitions);
- Validate the performance of the controller.
4.3. Fuzzy Multimodel Control
5. Results
5.1. Local Models and PI Controllers
5.2. Gain Scheduling
5.3. Fuzzy Multimodel Control with Two Models
5.4. Fuzzy Multimodel Control with Three Models
5.5. Fuzzy Multimodel Control with 4 Models
5.6. Performance Criteria
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Positive Direction | ||||||
---|---|---|---|---|---|---|
PWM Step (%) | RW Model | Kp | Ki | LW Model | Kp | Ki |
30 | 0.754 | 4.85 | 0.643 | 4.14 | ||
40 | 0.446 | 2.98 | 0.377 | 2.56 | ||
50 | 0.340 | 2.36 | 0.313 | 2.20 | ||
60 | 0.280 | 2.04 | 0.269 | 1.97 | ||
70 | 0.248 | 1.88 | 0.240 | 1.84 | ||
80 | 0.215 | 1.72 | 0.208 | 1.69 | ||
90 | 0.194 | 1.64 | 0.194 | 1.66 | ||
100 | 0.156 | 1.44 | 0.147 | 1.41 | ||
Negative Direction | ||||||
PWM Step (%) | RW Model | Kp | Ki | LW Model | Kp | Ki |
30 | 0.680 | 3.19 | 0.643 | 3.62 | ||
40 | 0.377 | 2.11 | 0.400 | 2.22 | ||
50 | 0.294 | 1.72 | 0.313 | 1.81 | ||
60 | 0.271 | 1.63 | 0.289 | 1.71 | ||
70 | 0.234 | 1.49 | 0.242 | 1.52 | ||
80 | 0.214 | 1.42 | 0.216 | 1.43 | ||
90 | 0.180 | 1.30 | 0.196 | 1.36 | ||
100 | 0.154 | 1.19 | 0.157 | 1.20 |
PWM (%) | Right Wheel (rpm) | Left Wheel (rpm) | ||
---|---|---|---|---|
Positive | Negative | Positive | Negative | |
30 | 645 | −665 | 665 | −657 |
40 | 797 | −791 | 792 | −803 |
50 | 896 | −894 | 897 | −903 |
60 | 965 | −953 | 952 | −966 |
70 | 1016 | −991 | 992 | −1010 |
80 | 1053 | −1018 | 1017 | −1044 |
90 | 1112 | −1072 | 1070 | −1101 |
100 | 1184 | −1138 | 1126 | −1176 |
Right Wheel | ||||
---|---|---|---|---|
Positive Direction | Negative Direction | |||
ISE | ITAE | ISE | ITAE | |
Gain Scheduling | ||||
Multimodel Control: 2 Models | ||||
Multimodel Control: 3 Models | ||||
Multimodel Control: 4 Models |
Left Wheel | ||||
---|---|---|---|---|
Positive Direction | Negative Direction | |||
ISE | ITAE | ISE | ITAE | |
Gain Scheduling | ||||
Multimodel Control: 2 Models | ||||
Multimodel Control: 3 Models | ||||
Multimodel Control: 4 Models |
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Miquelanti, M.G.; Pugliese, L.F.; Silva, W.W.A.G.; Braga, R.A.S.; Monte-Mor, J.A. Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots. Appl. Sci. 2024, 14, 6675. https://doi.org/10.3390/app14156675
Miquelanti MG, Pugliese LF, Silva WWAG, Braga RAS, Monte-Mor JA. Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots. Applied Sciences. 2024; 14(15):6675. https://doi.org/10.3390/app14156675
Chicago/Turabian StyleMiquelanti, Mateus G., Luiz F. Pugliese, Waner W. A. G. Silva, Rodrigo A. S. Braga, and Juliano A. Monte-Mor. 2024. "Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots" Applied Sciences 14, no. 15: 6675. https://doi.org/10.3390/app14156675