Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control
where is the constant matrix related to vehicle parameters, is the nonlinear input part of the intelligent vehicle platoon Equation of state, is the output variable constant matrix, as shown in Equations (17)–(19) for details, and is the input parameter of the intelligent vehicle platoon.
where , , , is the sampling time, and is the observation matrix. and are process noise and measurement noise, respectively; the two are independent of each other and accord with normal distribution. The process noise covariance of , and the measurement noise covariance . The initial state value of the system is , and the initial covariance matrix is . This paper calculates the estimated state according to the Kalman filter, as shown in Equation (21):
where is the difference between the observed and predicted values of the system, is the predicted value of the system covariance, is the Kalman gain, and is the updated value of the system covariance. After de-perturbation, the state parameter value shown in Equation (21) is finally obtained and input to the controller.
where
where , , , See Equations (25)–(28) for details:
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