Combining expert system and analytical redundancy concepts for fault-tolerant flight control

A technique for rule-based fault-tolerant flight control is presented. The objective is to define methods for designing control systems capable of accommodating a wide range of aircraft failures, including sensor, control, and structural failures. A software architecture that integrates quantitative analytical redundancy techniques and heuristic expert system concepts for the purpose of in-flight, real-time fault tolerance is described. The resultant controller uses a rule-based expert system approach to transform the problem of failure accommodation task scheduling and selection into a problem of search. Control system performance under sensor and control failures using linear discrete-time deterministic simulations of a tandem-rotor helicopter's dynamics is demonstrated. It is found that the rule-based control technique enhances existing redundancy management systems, providing smooth integration of symbolic and numeric computation, a search-based decision-making mechanism, straightforward system organization and debugging, an incremental growth capability, and inherent parallelism for computational speed.

[1]  William B. Rouse,et al.  Studies of planning behavior of aircraft pilots in normal, abnormal, and emergency situations , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Robert F. Stengel,et al.  An Architecture for Real-Time Rule-Based Control , 1987, 1987 American Control Conference.

[3]  Robert F. Stengel.  Artificial Intelligence Theory and Reconfigurable Control Systems , 1984 .

[4]  T. H. Rockwell,et al.  Combining destination diversion decisions and critical in-flight event diagnosis in computer aided testing of pilots , 1984 .

[5]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[6]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[7]  D. J. Young,et al.  Redundancy Management of Shuttle Flight Control Rate Gyroscopes and Accelerometers , 1982 .

[8]  Joe W. Duran,et al.  A General Expert System Design for Diagnostic Problem Solving , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  A. J. Ostroff,et al.  A technique using a nonlinear helicopter model for determining trims and derivatives , 1976 .

[10]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[11]  Bernard Friedland.  Maximum Likelihood Failure Detection of Aircraft Flight Control Sensors , 1982 .

[12]  Robert F. Stengel.  COMBINING QUANTITATIVE AND QUALITATIVE REASONING IN AIRCRAFT FAILURE DIAGNOSIS. , 1985 .

[13]  Christopher D. Wickens,et al.  Failure Detection in Dynamic Systems , 1981 .

[14]  R. Stengel.  Stochastic Optimal Control: Theory and Application , 1986 .

[15]  David A. Handelman.  An Application of Artificial Intelligence Theory to Reconfigurable Flight Control. , 1987 .

[16]  B. K. Walker,et al.  A fault-tolerant approach to helicopter swashplate control , 1985 .

[17]  Edward H. Shortliffe,et al.  Production Rules as a Representation for a Knowledge-Based Consultation Program , 1977, Artif. Intell..

[18]  K. R. Ames.  A relational approach to the development of expert diagnostic systems , 1984 .