Abstract
Receding-Horizon Estimation (RHE) is an optimal filterling approach which uses past series of the plant's measured output and input and finds estimated states based on linear programming or quadratic programming. It is known that RHE can estimate the plant state to which the Kalman filter cannot be applied due to modeling errors. This paper considers the new computational form of RHE based on the principal dual gradient algorithm. The proposed form is expressed by the dynamical system, so we can consider the computational stability based on the dynamical system theory. This paper discusses the continuous-time representation of the RHE algorithm (continuous-time RHE) and filter characteristics to improve the convergence performance of the estimation. On the basis of the small gain theorem, the dynamics of RHE is analyzed. Also, the characteristics of continuous-time RHE is demonstrated via a vehicle path tracking control problem.
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