Yaw Rate PI Control Using Kalman-Based Noise Suppression
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Solution Overview
Problem
Existing PI control methods for yaw rate control in vehicles are susceptible to increased measurement noise, which affects the accuracy of control input values due to differentiation of output errors.
Innovation Solution
A control apparatus and method using a Kalman filter-based state space model to estimate deviation in the control model, suppressing measurement noise influence by defining the differential of the output value with control input and deviation from a normative model, and adjusting parameters Q and R for optimal Kalman gain settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PI control with differentiation of output error is used to suppress deviation between target and output values, then control accuracy is improved, but measurement noise influence increases
Solution Approach 1:
The patent introduces an intermediary model (Ultra-Local Model) that represents the relationship between control input and output without requiring direct differentiation of the output signal. This intermediary model acts as a mediator that estimates the deviation amount F based on the control input u and model parameters, thereby achieving accurate control while avoiding the noise amplification problem associated with direct output differentiation.
Solution Approach 2:
The patent changes the control approach from directly differentiating output signals to estimating parameters of an Ultra-Local Model. By transforming the control problem into parameter estimation and adaptation rather than signal differentiation, the method maintains control accuracy while suppressing measurement noise influence.
2Object-affected harmful factors
If Kalman filter is used to estimate deviation amount and suppress measurement noise, then measurement noise influence is reduced, but device complexity increases
Solution Approach 1:
The patent simplifies the complex Kalman filter implementation by changing the problem formulation to parameter adaptation in an Ultra-Local Model. Instead of implementing a full-state Kalman filter that would require complex matrices and computations, the method adapts simple model parameters (a, F, b) that capture the essential system behavior, thereby reducing computational complexity while maintaining noise suppression capabilities.
Solution Approach 2:
The patent extracts only the essential elements needed for noise suppression by formulating a simplified Ultra-Local Model that captures the dominant system characteristics. Rather than using a complete and complex dynamic model with all system states, the method extracts and utilizes only the critical parameters needed for control, thereby reducing complexity while maintaining effectiveness.
Data Source
AI summary
A control apparatus for PI-controlling a control input value to a control object on the basis of a difference between a target value to the control object and an output value from the control object, the control apparatus includes an estimation part that estimates an amount of deviation in a control model, in which a differential of an output value of the control object including measurement noise is defined by the control input value and an amount of deviation of the output value of the control object from an output value of a normative model, by means of a Kalman filter composed of a state space model, a calculation part that obtains the control input value on the basis of the estimated amount of deviation, a proportional gain, and an integral gain, and an adjustment part that adjusts a parameter for setting a Kalman gain of the Kalman filter.