论文信息 - MODEL PREDICTIVE NEURAL CONTROL OF A HIGH-FIDELITY HELICOPTER MODEL

MODEL PREDICTIVE NEURAL CONTROL OF A HIGH-FIDELITY HELICOPTER MODEL

In this paper we present a method for optimal control of a nonlinear highly realistic helicopter model based on a combination of a neural network (NN) feedback controller and a state-dependen t Riccati equation (SDRE) controller. Optimization of the NN is performed within a receding horizon model predictive control (MPC) framework, subject to dynamic and kinematic constraints. The SDRE controller utilizes a simplified 6DOF rigid body dynamic model, and augments the NN controller by providing an initial feasible solution and improving stability. While the SDRE control provides robustness based on a pseudo-linear formulation of the dynamics, the MPNC utilizes the highly accurate numerical helicopter model.

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