Yaw Rate PI Controller Using Kalman Estimation Against Noise
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Solution Overview
Problem
Existing PI control methods for vehicle yaw rate controllers are susceptible to increased measurement noise due to differentiation of output, leading to oscillations in control inputs.
Innovation Solution
A controller and control method that utilize a Kalman filter to estimate the deviation amount from a state space model, suppressing the influence of measurement noise by not differentiating the actual yaw rate, thereby stabilizing the control input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the output is differentiated when estimating an error in PI control, then the divergence between target value and output is reduced, but the influence of measurement noise increases
Solution Approach 1:
The patent introduces an ultra-local model as an intermediary between the actual system output and the error estimation process. Instead of directly differentiating the noisy output signal, the model serves as a mediator that relates the output to the control input and deviation amount, allowing error estimation without direct differentiation of measurement noise
Solution Approach 2:
The patent replaces the mechanical differentiation operation (which amplifies noise) with an algebraic calculation based on the ultra-local model. The error is estimated through model-based computation of the deviation amount rather than through numerical differentiation of the measured output signal
2Ease of operation
If measurement noise is differentiated to estimate error, then PI control can be implemented, but oscillations in control input occur
Solution Approach 1:
The ultra-local model acts as an intermediary that decouples the PI control implementation from direct differentiation of noisy signals. The model provides a structured relationship between control input, output, and deviation amount, enabling PI control through model-based estimation rather than signal differentiation
Solution Approach 2:
The patent implements feedback through the estimation of the deviation amount using the ultra-local model. The estimated deviation amount feeds back into the PI controller to adjust the control input, creating a stable closed-loop system that does not rely on differentiating noisy measurement signals
Data Source
AI summary
A controller that performs PI control on a control input to a controlled object on the basis of a difference between a target value and an output, the controller including a model converting part that converts an ultra-local model obtained by modeling the controlled object into a state space model by defining a differential of an output of the controlled object including measurement noise by the control input and a deviation amount with respect to a reference model, an estimation part that estimates the deviation amount of the ultra-local model on the basis of a Kalman filter constructed from the state space model, and a steering angle calculation part that obtains the control input on the basis of the estimated deviation amount, a proportional gain, and an integral gain.