Predictive Mechatronic Control With Barrier Functions at High Sampling Rates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mechatronic systems face challenges in maintaining stable, high sampling rate control due to environmental variations, high-level requirement changes, and manufacturing non-compliances, with current control methods being sub-optimal, costly, and unable to adapt self-adaptively.
Innovation Solution
A method for controlling mechatronic systems using a prediction model and a cost function with barrier functions, employing the Nelder-Mead method to optimize polynomial coefficients, ensuring compliance with constraints and high sampling rates through reformulated constraints integrated into the cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional control methods are used to maintain stable control, then system stability is preserved, but the sampling rate decreases and adaptability to environmental variations is lost
Solution Approach 1:
The control method dynamically adapts to environmental variations and system state changes by continuously updating the prediction model and recalculating optimal commands at each sampling period. This dynamic approach enables the system to maintain stability while operating at high sampling rates, resolving the contradiction between stability and sampling rate.
Solution Approach 2:
The invention changes the control parameters by using a prediction model that anticipates system behavior and adjusts control commands based on predicted future states. This parameter transformation from reactive to predictive control allows the system to achieve both high sampling rates and stability simultaneously.
2Reliability
If conventional control methods are used to ensure compliance with constraints, then constraint satisfaction is achieved, but calculation time increases and sampling rate decreases
Solution Approach 1:
The prediction model performs preliminary action by anticipating system behavior and constraint violations before they occur. By predicting future states and calculating optimal commands in advance, the system ensures constraint compliance while reducing actual real-time calculation time, thus resolving the contradiction between constraint satisfaction and calculation time.
Solution Approach 2:
The invention substitutes complex iterative optimization mechanisms with a prediction-based approach that directly calculates optimal commands. This substitution replaces time-consuming mechanical optimization processes with faster predictive computations, maintaining constraint compliance while significantly reducing calculation time.
3Adaptability or versatility
If adaptive control is implemented to handle environmental variations, then system adaptability improves, but device complexity and development cost increase
Solution Approach 1:
The control system performs self-service by automatically adapting to environmental variations through the prediction model without requiring external recalibration or complex reconfiguration. The system uses its own operational data to continuously improve its prediction accuracy, reducing the need for additional complexity while maintaining high adaptability.
Solution Approach 2:
The prediction model serves multiple functions simultaneously: it predicts system behavior, optimizes control commands, ensures constraint compliance, and adapts to environmental variations. This multi-functionality reduces the need for separate specialized components, thereby decreasing overall device complexity while maintaining high adaptability.
4Measurement precision
If high sampling rate control is implemented, then control precision improves, but calculation time per sample increases and system complexity increases
Solution Approach 1:
The invention substitutes complex iterative optimization calculations with a prediction-based direct calculation approach. This substitution dramatically reduces the computational burden per sampling period, enabling high sampling rates while maintaining control precision through the predictive nature of the control commands.
Data Source
AI summary
A method for controlling a mechatronic system, based on a model for predicting the behaviour of the mechatronic system and a cost function ensuring compliance with constraints by the mechatronic system, with a view to following path instructions to a prediction horizon, the method including reformulating the constraints into barrier functions and integrating the barrier functions into the cost function; and for each sampling period of a sequence of sampling periods: obtaining the path instructions and at least one measurement of the mechatronic system in a current state; determining coefficients of a polynomial of order m using a Nelder-Mead method optimizing the cost function based on the predicting model, this determining receiving as input the path instructions and the at least one measurement of the mechatronic system obtained; computing a command through evaluation of the polynomial; and applying the command to the mechatronic system.