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The introduction of new larger aeroplanes presents the ICAO Instrument Flight Procedures Panel (IFPP) with the need to review the requirements for the Obstacle Free Zone. To support future decisions, the IFPP took the initiative to ask for the development of pilot models which are capable to control the simulated aircraft during the approach — go-around manoeuvre. The aim of this development was to obtain a tool to perform Monte Carlo simulations for the determination of the flight path statistics of the manoeuvre. In 2001, both QinetiQ and the National Aerospace Laboratory (NLR) were invited to develop pilot models. The two pilot models are based on fundamentally different descriptions of a pilot’s control behaviour. The QinetiQ pilot model is based on a discrete-event representation of pilot control movements and has been developed in the Integrated Performance Modelling Environment (IPME). The NLR pilot model is based on control engineering and is a linear model with visual and motion feedback extended with stochastic disturbances. This development was supported by Boeing, which provided a simulation model of the B747–400 as the representative aircraft model. The integration of the pilot models with the aircraft model was performed by Boeing. Statistical data on the flight path tracking during the approach – go-around manoeuvre and on discrete pilot actions were obtained from simulations performed in a full flight simulator (FFS) at NASA Ames and a fixed-base simulator at Boeing. Both pilot models, the use of the statistical data from the simulations and the integration with the aircraft model are discussed in the paper.

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existing methods. The main computation work involved in the algorithm be its transfer matrix. Assume that { A , B, C, D } has no (purely) includes one Cholesky decomposition [step I], one singular value imaginary eigenvalues which are both controllable and observable. Then decomposition [step 21, and one matrix-pseudoinverse operation [step 31. the matrix-valued function G is bounded on the imaginary axis, and the All these operations are well known, and can be performed very infinity norm of G is, by definition, efficiently by the existing packages such as LINPACK, EISPACK, etc. Even when balancing is not required, this algorithm would hopefully IlGIIm=sup llG(b,ll provide us with an efficient method for obtaining a minimal realization U E R from a TFM.2) When evaluating F ( C 3 in step 2), we need the inverse of 6(Cl); however, this matrix-inverse operation can be avoided if we note the identity b(cb) = 0. By choosing two polyno_mials x(z) and y(z) such that x(z)6(z) + y(z)b(z) = 1, we then have b(Cb)-' = x(cb).3) It is known [ 11 that the controllability Gramian and the observability Gramian are the fundamental invariants under the bilinear transformation s = zl/z + 1. Hence, in (5), if ( A , B, C, D) has diagonal Gramians, then (a, B, e, d) will also have the same diagonal Gramians which, in turn, implies that (a, B, e, 8) is actually the balanced realization for w.

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