Self-Adaptive Braking Control for Aging Wear
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Solution Overview
Problem
Electromechanical-braking systems face challenges in ensuring safety and stability due to unsatisfactory control strategies that are not robust against structural changes caused by aging, wear, and malfunctioning components, which affects their precision, accuracy, and reliability.
Innovation Solution
A self-adaptive model-based predictive control system is implemented, utilizing a parametric identification of the braking system, a generalized predictive control method, and multi-objective optimization techniques to optimize endogenous parameters, enabling robust control against structural changes and improving long-term stability and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional PID control is used for electromechanical braking, then the control system is simple to implement, but the system lacks robustness against structural changes due to aging, wear, and component malfunctioning
Solution Approach 1:
The control system dynamically adapts to structural changes in the braking system through continuous parameter identification and online adjustment of controller parameters. The system transitions from a static PID controller to a dynamic self-adaptive controller that modifies its behavior based on real-time system state, thereby maintaining robustness against aging, wear, and component failures without requiring complete system redesign
Solution Approach 2:
The control system performs self-diagnosis and self-adjustment by continuously identifying system parameters and automatically tuning controller gains. The system serves itself by detecting structural changes and adapting its control strategy without external intervention, eliminating the need for manual recalibration and maintaining reliability under varying operational conditions
2Measurement precision
If model-based predictive control with parametric identification is implemented, then the precision and accuracy of braking control is improved, but the computational cost increases
Solution Approach 1:
The system performs parametric identification and controller optimization in advance during offline design phases, pre-computing control parameters and storing them for online use. This preliminary action reduces the computational burden during real-time braking operations, as the system only needs to retrieve and apply pre-optimized parameters rather than performing complex calculations in real-time
Solution Approach 2:
The patent replaces complex real-time computational models with simplified mathematical models that have been pre-characterized through offline identification. The system substitutes heavy numerical simulations with lightweight analytical models that maintain sufficient precision for braking control while dramatically reducing computational energy requirements during operation
3Stability of the object's composition
If the control system adapts to structural changes in real-time, then the long-term stability is improved, but the control algorithm complexity increases
Solution Approach 1:
The control system implements continuous feedback loops that monitor braking system performance and automatically adjust controller parameters in response to detected structural changes. The feedback mechanism enables the system to maintain long-term stability by constantly comparing actual performance with desired performance and making corrective adjustments, thereby compensating for aging, wear, and component failures without requiring complex predictive algorithms
Data Source
AI summary
A control system for an electromechanical-braking system provided with actuator elements configured to actuate braking elements for exerting a braking action has a control stage for controlling the braking action on the basis of a braking reference signal. The control stage comprises a model-based predictive control block, in particular of a generalized predictive self-adaptive control type, operating on the basis of a control quantity representing the braking action. The control system further has: a model-identification stage, which determines parameters identifying a transfer function of the electromechanical-braking system; and a regulation stage, which determines an optimal value of endogenous parameters of the control system on the basis of the value of the identifying parameters.