Decentralized Estimation and Control for Multisensor Systems

By Arthur G. O. Mutambara. 2019

496 citations recorded by the source corpus.

Introduction Background Motivation Problem Statement Approach Principal Contributions Book Outline Estimation and Information Space Introduction The Kalman Filter The Information Filter The Extended Kalman Filter (EKF) The Extended Information Filter (EIF) Examples of Estimation in Nonlinear Systems Summary Decentralized Estimation for Multisensor Systems Introduction Multisensor Systems Decentralized Systems Decentralized Estimators The Limitations of Fully Connected Decentralization Summary Scalable Decentralized Estimation Introduction An Extended Example The Moore-Penrose Generalized Inverse: T+ Generalized Internodal Transformation Special Cases of Tji(k) Distributed and Decentralized Filters Summary Scalable Decentralized Control Introduction Optimal Stochastic Control Decentralized Multisensor Based Control Simulation Example Summary Multisensor Applications: A Wheeled Mobile Robot Introduction Wheeled Mobile Robot (WMR) Modeling Decentralized WMR Control Hardware Design and Construction Software Development On-Vehicle Software Summary Results and Performance Analysis Introduction System Performance Criteria Simulation Results WMR Experimental Results Summary Conclusions and Future Research Introduction Summary of Contributions Research Appraisal Future Research Directions Bibliography

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4bedf9643200b5cf6f05a9d1f26b6cf737a150a4

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Citations in the corpus

Distributed particle filters for sensor networks (2004, background): “The object’s initial position was determined by a Gaussian distribution centred at [2,2] and diagonal covariance entries set to 1.”
Distributed model predictive control: A tutorial review and future research directions (2013, unknown): “In Mutambara (1998), a solution based on reduced-order and decoupled models for each subsystem was proposed, while subsystems with overlapping states were considered in the fully distributed schemes”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, methodology): “Relying on the methods for distributed state estimation and control presented in [70,69], distributed algorithms have been described in [106,105], while an extension of these techniques based on MPC has been described in [64].”
On Optimal Sensor Placement and Motion Coordination for Target Tracking (2005, background): “For works dealing with multisensor fusion possibly under communication constraints we refer to [8], [9], [10], [11] and references”
Optimal sensor placement and motion coordination for target tracking (2006, background): “Some standard computations [2, 9] allow us to say that P e (t) = P −1 p (t) + (∇qHt|qp(t))R(t)∇qHt|qp(t) or, denoting (∇qHt|qp(t))R(t)∇qHt|qp(t) = JNR(t), P e (t) = P −1 p (t) + JNR(t) , t ≥ 0 .”
Distributing the Kalman Filter for Large-Scale Systems (2007, methodology): “When the observations are distributed among the sensors, see (11), the CIF can be implemented by collecting all the sensor observations at a central location; or with observation fusion by realizing that the global observation variables in (20)−(21), can be written as, see [3], [11], [7], ik = HRyk ”