Hybrid neural adaptive control for bank-to-turn missiles

A novel hybrid neural adaptive bank-to-turn (BTT) lateral autopilot is described for a short-range command-to-line-of-sight (CLOS) surface-to-air missile. This employs a multiinput-multioutput Gaussian radial basis function (RBF) network in parallel with a constant parameter, independently regulated lateral autopilot, to adaptively compensate for roll-induced cross-coupling time-varying aerodynamic derivatives and control surface constraints, in order to achieve consistent tracking performance over the flight envelope. The hybrid neural autopilot is evaluated in three dimensional (six-degree of freedom) simulation studies against realistic pitch acceleration and roll rate profiles generated from a typical CLOS guidance scenario, and its performance compared with a carefully designed gain scheduled autopilot. The results are found to be encouraging and clearly demonstrate the potential advantages of the neurocontrol scheme