Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning
Abstract
:1. Introduction
- (1)
- The proposed algorithm estimates the RLS-based error dynamics coefficients and does not require information regarding the system. Therefore, it can be used as a universal-purpose controller in various unknown systems.
- (2)
- In this study, a virtual test drive simulator, CarMaker, and an actual DC motor platform were used to evaluate the reasonable performance of the proposed universal controller. In the case of the CarMaker-based evaluation, this study attempted to verify the performance of the proposed algorithm in various systems using front-wheel steering vehicles and front-and-rear-wheel steering vehicles.
2. Adaptive Control Algorithm Using RLS and Parameter Self-Tuning
2.1. MIMO System Error Dynamics
- (A1)
- All the control errors have a complex influence on each other.
- (A2)
- At this stage, and the number of control errors and control inputs are the same.
2.2. RLS-Based Coefficient Estimation
2.3. Derivation of Control Input Based on the Lyapunov Direct Method
3. Performance Evaluation
3.1. Performance Evaluation of DC Motor-Based Adaptive Speed Control
3.2. Performance Evaluation of CarMaker-Based Adaptive Path Tracking Control
3.2.1. Front-Wheel Steering Vehicle-Based Adaptive Path Tracking Control
3.2.2. Front-and-Rear-Wheel Steering Vehicle-Based Adaptive Path Tracking Control
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Unit | Value |
---|---|---|
Resistance | ||
Torque constant | ||
Motor back-EMF constant | ||
Rotor inductance | 1.16 | |
Inertia | 4.6 |
Parameter | Unit | Value |
---|---|---|
Decay rate of the Lyapunov function () | - | 0.0001 |
Reachability factor (η) | - | 0.0001 |
Initial value of estimated states () | - | |
Initial value of covariance () | - | |
Forgetting factor | - | |
Lower scale factor threshold ( | - | 0.01 |
Upper scale factor threshold ( | - | 0.08 |
Normalized threshold | - | 1.001 |
Parameter | Unit | Value |
---|---|---|
Mass () | 2108 | |
Wheelbase () | 2.97 | |
Distance between CG * and front/rear axle () | ||
Z-axis rotational inertia () | 3170.6 | |
Estimated cornering stiffness of front/rear wheels () |
Parameter | Unit | Value |
---|---|---|
Decay rate of the Lyapunov function () | - | 1 |
Reachability factor (η) | - | 0.01 |
Initial value of estimated states () | - | |
Initial value of covariance () | - | |
Forgetting factor | - | |
Lower scale factor threshold ( | - | 0.03 |
Upper scale factor threshold ( | - | 0.1 |
Normalized threshold | - | 1.008 |
Parameter | Unit | Value |
---|---|---|
Decay rate of the Lyapunov function () | - | 1.2 |
Reachability factor (η) | - | 0.01 |
Initial value of estimated states () | - | |
Initial value of estimated states () | - | |
Initial value of covariance () | ||
Initial value of covariance () | ||
Forgetting factor | - | |
Forgetting factor | - | |
Lower scale factor threshold ( | - | 0.03 |
Upper scale factor threshold ( | - | 0.1 |
Normalized threshold | - | 1.008 |
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La, H.; Oh, K. Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning. Actuators 2024, 13, 167. https://doi.org/10.3390/act13050167
La H, Oh K. Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning. Actuators. 2024; 13(5):167. https://doi.org/10.3390/act13050167
Chicago/Turabian StyleLa, Hanbyeol, and Kwangseok Oh. 2024. "Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning" Actuators 13, no. 5: 167. https://doi.org/10.3390/act13050167