Hybrid estimation of complex systems

Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner.

[1]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[2]  Jane T. Malin,et al.  INTERACTIVE SIMULATION-BASED TESTING OF PRODUCT GAS TRANSFER INTEGRATED MONITORING AND CONTROL SOFTWARE FOR THE LUNAR MARS LIFE SUPPORT PHASE III TEST , 1998 .

[3]  Michael K. Ewert,et al.  Advanced Life Support--Baseline Values and Assumptions Document , 2005 .

[4]  Brian C. Williams,et al.  Model-Based Programming of Fault-Aware Systems , 2004, AI Mag..

[5]  Carla Limongelli,et al.  Design and Implementation of Symbolic Computation Systems , 1996, Lecture Notes in Computer Science.

[6]  P. Pandurang Nayak,et al.  Automated Modeling of Physical Systems , 1995, Lecture Notes in Computer Science.

[7]  Gautam Biswas,et al.  An Approach to Model-Based Diagnosis of Hybrid Systems , 2002, HSCC.

[8]  Peter S. Maybeck,et al.  Multiple-model adaptive estimation using a residual correlation Kalman filter bank , 2000, IEEE Trans. Aerosp. Electron. Syst..

[9]  John Beidler,et al.  Data Structures and Algorithms , 1996, Wiley Encyclopedia of Computer Science and Engineering.

[10]  Jitendra K. Tugnait,et al.  Detection and estimation for abruptly changing systems , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[11]  Peter Struss,et al.  "Physical Negation" Integrating Fault Models into the General Diagnostic Engine , 1989, IJCAI.

[12]  Feng Zhao,et al.  Distributed Monitoring of Hybrid Systems: A model-directed approach , 2001, IJCAI.

[13]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[14]  Brian C. Williams,et al.  Mode Estimation of Probabilistic Hybrid Systems , 2002, HSCC.

[15]  Sheila A. McIlraith.  Diagnosing Hybrid Systems: a Bayesian Model Selection Approach , 2005 .

[16]  X. Rong Li,et al.  Multiple-model estimation with variable structure. II. Model-set adaptation , 2000, IEEE Trans. Autom. Control..

[17]  A. Gehin,et al.  Structural Analysis of System Reconfigurability , 2000 .

[18]  K. Ito,et al.  On State Estimation in Switching Environments , 1970 .

[19]  Visakan Kadirkamanathan,et al.  Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[20]  X. Rong Li,et al.  Multiple-Model Estimation with Variable Structure—Part II: Model-Set Adaptation , 2000 .

[21]  B. Williams,et al.  Multi-Modal Particle Filtering for Hybrid Systems with Autonomous Mode Transitions , 2003 .

[22]  Philippe Dague,et al.  State Tracking of Uncertain Hybrid Concurrent Systems , 2002 .

[23]  Michael W. Hofbaur,et al.  Hybrid Diagnosis with Unknown Behavioral Modes , 2002 .

[24]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[25]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[26]  Brian C. Williams,et al.  Diagnosis with Behavioral Modes , 1989, IJCAI.

[27]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[28]  Feng Zhao,et al.  Estimation of Distributed Hybrid Systems Using Particle Filtering Methods , 2003, HSCC.

[29]  Richard Zippel,et al.  The Weyl Computer Algebra Substrate , 1993, DISCO.

[30]  Eduardo D. Sontag,et al.  Mathematical control theory: deterministic finite dimensional systems (2nd ed.) , 1998 .

[31]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[32]  Eduardo D. Sontag,et al.  Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .

[33]  Arjan van der Schaft,et al.  Analysis of hybrid systems , 2000 .

[34]  P. Pandurang Nayak,et al.  A Model-Based Approach to Reactive Self-Configuring Systems , 1996, AAAI/IAAI, Vol. 2.

[35]  Peter S. Maybeck,et al.  Reconfigurable flight control via multiple model adaptive control methods , 1990, 29th IEEE Conference on Decision and Control.

[36]  X. R. Li,et al.  Multiple-model estimation with variable structure. III. Model-group switching algorithm , 1999 .

[37]  Gautam Biswas,et al.  Bayesian Fault Detection and Diagnosis in Dynamic Systems , 2000, AAAI/IAAI.

[38]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[39]  Brian C. Williams,et al.  Decompositional, Model-based Learning and its Analogy to Diagnosis , 1998, AAAI/IAAI.

[40]  R. Pons.  Causal or-dering for multiple mode systems , 1997 .

[41]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[42]  Kurt Johannes Reinschke,et al.  Multivariable Control a Graph-theoretic Approach , 1988 .

[43]  Luca Console,et al.  Readings in Model-Based Diagnosis , 1992 .