Model Predictive Neural Control for Aggressive Helicopter Maneuvers

This chapter shares with Chapter 9 the adoption of a model predictive control (MPC) framework for flight control applications, but the details differ substantially. In particular, the control feedback in this case is a superposition of a neural-network-based nonlinear mapping and a nonlinear state-dependent Riccati equation (SDRE) controller. The neural network is optimized (trained) online for high performance using a high-fidelity dynamic simulation model of the vehicle. The SDRE controller design, repeated at every sample time, provides initial local asymptotic stability. The relative contributions of each controller vary depending on the training error of the neural network. The application considered is maneuver control of autonomous helicopters. The controller is multivariable with five actuator command outputs. Simulation results for a variety of maneuvers are presented, including rapid take-off and landing and a difficult ”elliptic” maneuver. The authors also incorporate wind effects in the optimization. Instead of the conventional tactic of treating environmental conditions as a random disturbance, this allows control commands to be optimized for localized wind flows. This approach relies on wind predictions over the optimization horizon; sensors and models to support such predictions at the appropriate scale and resolution are the object of intense research today. Both the SDRE design and the neural network training are computationally demanding. The authors consider possible tradeoffs between computational effort and controller performance in order to approach real-time feasibility. Various approximations to some of the especially complex calculations are considered. Computing time and control performance data are presented, comparing different neural