Model Predictive Controller Weight Tuning and Real-Time Learning-Based Weight Selection
Notice bibliographique
Résumé
A variety of control systems with specific goals are designed and utilized in every vehicle system. Optimal performance of each of these control systems is essential to keep the vehicle in a safe and desirable driving condition. A model predictive controller (MPC) is a type of control system that employs an internal model of the system being controlled to predict its future behavior and determine the optimal control actions to achieve desired outcomes. The controller works by continuously updating its predictions based on the current state of the system and using an optimization algorithm to calculate the best control actions while satisfying any constraints on the system. \n \nIn each MPC controller, there is an objective function with a set of weights. These weights can directly affect the response of the system. The appropriate selection of weights results in the generation of an effective control action, which reduces tracking errors to a minimum. In the conventional MPC controllers, the focus is solely on optimizing the control actions, and weight values remain fixed or scheduled for different ranges of system operations. Therefore, the effects of real-time selection of optimum weights in the controller performance are overlooked. \n \nThis research aims to improve the performance of MPC control systems by developing a weight tuning and real-time weight selection scheme that considers the dynamic system's state. The proposed approach is applied to the vehicle stability control under a variety of environmental and/or driving conditions. The weight tuning is performed by using the prediction model of the vehicle and the Bayesian optimization (BO) technique. The weight selection is carried out in real-time by learning the adjusted weights through Gaussian process regression (GPR). These are two main modules developed to be used for selecting and tuning the weights of an MPC controller. Hence, in addition to optimizing control actions through the MPC controller's optimization problem, the weights of the MPC controller are also assessed and adjusted to achieve the highest level of optimality in the vehicle control system. \n \nFurthermore, an authentication process is proposed to evaluate the tuned weights after being selected in the tests. This way, unnecessary increases or decreases in the weights stored in the weight selection dataset can be avoided. To further enhance the model predictions, a blending-based multiple model approach is utilized. In this approach, instead of considering a fixed prediction model with invariant parameters, a combination of finite number of models with different parameters are considered. Based on the prediction error of each model, a weighted sum of matrices of these models are utilized both in the MPC controller and weight tuning modules. \n \nTo verify the proposed methodology, MATLAB/Simulink and CarSim co-simulations as well as experimental tests are carried out. Comparing the vehicle responses with and without the proposed weight tuning and real-time weight selection approach strongly corroborates the proposed technique in enhancing the controller performance. The capability of the proposed multiple model technique in improving the weight tuning has been demonstrated in the simulations and experimental results.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».