Optimal sensor location for parameter estimation of distributed processes

The problem under consideration is to plan sensor movements in prescribed feasible regions in such a way as to maximize the accuracy of parameter identification of a distributed system defined in a two-dimensional spatial domain. A general functional defined on the Fisher information matrix is used as the design criterion. Central to the approach is the formulation of the problem as an optimal control one, possibly with state inequality constraints. Its solution is obtained numerically with the use of a method of successive linearizations which is capable of handling various constraints imposed on the sensors' motions. Simple numerical examples are included which clearly demonstrate the ideas and trends presented in the paper.

[1]  G. Goodwin,et al.  Optimum experimental design for identification of distributed parameter systems , 1980 .

[2]  Kazushi Nakano,et al.  Optimal measurement problem for a stochastic distributed parameter system with movable sensors , 1981 .

[3]  Carlos S. Kubrusly,et al.  Sensors and controllers location in distributed systems - A survey , 1985, Autom..

[4]  G. Raiconi,et al.  Optimal location of a moving sensor for the estimation of a distributed-parameter process , 1987 .

[5]  Guanrong Chen,et al.  Numerical Solution of Optimal Control Problems with State Constraints by Sequential Quadratic Programming in Function Spaces (K. C. P. Machielsen) , 1991, SIAM Rev..

[6]  S. Omatu,et al.  Parameter identification for distributed systems and its application to air pollution estimation , 1991 .

[7]  Sigeru Omatu,et al.  Distributed parameter identification by regularization and its application to prediction of air pollution , 1991 .

[8]  A. Yu. Khapalov Optimal measurement trajectories for distributed parameter systems , 1992 .

[9]  Józef Korbicz,et al.  Sensor Allocation for State and Parameter Estimation of Distributed Systems , 1994 .

[10]  Suresh P. Sethi,et al.  A Survey of the Maximum Principles for Optimal Control Problems with State Constraints , 1995, SIAM Rev..

[11]  Harvey Thomas Banks,et al.  Smart material structures: Modeling, estimation, and control , 1996 .

[12]  P. Malanotte‐Rizzoli Modern approaches to data assimilation in ocean modeling , 1996 .

[13]  Elijah Polak,et al.  Optimization: Algorithms and Consistent Approximations , 1997 .

[14]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[15]  Thomas Lux,et al.  The DYMOS Model System for the Analysis and Simulation of Regional Air Pollution , 1997 .

[16]  Measurement optimization with minimax criteria for parameter estimation in distributed systems , 1997, 1997 European Control Conference (ECC).

[17]  Richard B. Vinter,et al.  A Feasible Directions Algorithm for Optimal Control Problems with State and Control Constraints: Convergence Analysis , 1998 .

[18]  Miroslaw Galicki,et al.  The Planning of Robotic Optimal Motions in the Presence of Obstacles , 1998, Int. J. Robotics Res..

[19]  Richard B. Vinter,et al.  Feasible Direction Algorithm for Optimal Control Problems with State and Control Constraints: Implementation , 1999 .

[20]  Steven E. Rigdon,et al.  Model-Oriented Design of Experiments , 1997, Technometrics.

[21]  Józef Korbicz,et al.  On robust design of sensor trajectories for parameter estimation of distributed systems , 1999 .