State quantity estimation device, control device, and state quantity estimation method

A control device which estimates a state quantity that acts on a vehicle having a suspension device and which controls a damping force of the suspension device in accordance with the state quantity, comprising:

• a data storing section in which the state quantity that acts on the vehicle and that includes at least a vehicle weight, state covariance that is covariance of the state quantity, and process noise covariance that is covariance of computation noise are stored;
• a predictive quantity computing section which computes a predictive state quantity from the state quantity and which computes, from the state covariance and the process noise covariance, predictive covariance that is covariance of the predictive state quantity;
• an obtaining section which obtains a sensor value of the vehicle;
• a Kalman gain computing section which computes Kalman gain with use of the sensor value and the predictive covariance;
• an estimated quantity computing section which computes an estimated state quantity from the Kalman gain, the predictive state quantity, and the sensor value with use of the Kalman filter and which computes estimated covariance from the Kalman gain, the predictive covariance, and the sensor value with use of the Kalman filter;
• and
• a weight component extracting section which extracts a weight component of the vehicle from the estimated state quantity;
• and
• a process noise covariance correcting section which corrects the process noise covariance with use of the weight component which has been extracted by the weight component extracting section,
• the estimated state quantity being written as the state quantity in the data storing section, the estimated covariance being written as the state covariance in the data storing section, the process noise covariance which has been corrected being written as the process noise covariance in the data storing section.

A method of estimating a state quantity, comprising the steps of:

• storing, in a data storing section, a state quantity that acts on a vehicle and that includes at least a vehicle weight, state covariance that is covariance of the state quantity, and process noise covariance that is covariance of computation noise;
• computing a predictive state quantity from the state quantity and computing, from the state covariance and the process noise covariance, predictive covariance that is covariance of the predictive state quantity;
• obtaining a sensor value of the vehicle;
• computing Kalman gain with use of the sensor value and the predictive covariance;
• computing an estimated state quantity from the Kalman gain, the predictive state quantity, and the sensor value with use of the Kalman filter;
• computing estimated covariance from the Kalman gain, the predictive covariance, and the sensor value with use of the Kalman filter;
• extracting a weight component of the vehicle from the estimated state quantity;
• and
• correcting the process noise covariance with use of the weight component of the vehicle;
• and
• writing, in the data storing section, the estimated state quantity as the state quantity, the estimated covariance as the state covariance, the process noise covariance which has been corrected as the process noise covariance.