基于自调整神经元的无人直升机航向控制方法

直升机航向动力学包含输入非线性、时变参数和主－尾旋翼之间的强耦合，传统的比例积分微分(Proportional integral differential, PID)方法很难达到良好的控制性能。基于以上原因，通过把自调整神经元与滑模控制相结合，提出一种能够解决带有输入非线性的航向自适应控制方法。与常规自适应控制相比，用滑模条件代替误差函数作为目标函数，使控制器在保证闭环稳定性的同时，能够进一步使跟踪误差满足期望精度。证明了该方法的稳定 性，针对实际模型直升机试验平台航向动力学模型的仿真结果，以及与传统PID方法的比较都表明了该方法的有效性。

ADAPTIVE-NEURON-BASED CONTROL FOR THE YAW TRACKING OF UNMANNED HELICOPTER

The yaw dynamics of helicopter involves input nonlinearity, time-varying parameters and the couplings between main and tail rotor. With respect to such complicated dynamics, the normal proportional integral differential (PID) control is difficult to realize good tracking performance while maintaining stability and robustness simultaneously. A valid control is proposed by integrating adaptive neuron into sliding mode control. The stability of the proposed method can beguaranteed by the sliding mode condition, and the time-varying parameters and other uncertainties in the helicopter dynamics can be rejected by the adaptive neuron to achieve small tracking error. The stability of the proposed algorithm is proved and the simulations results with respect to the dynamics identified from a real helicopter-on-arm testbed are presented. The simulation results are further compared with those obtained by normal PID control to demonstrate the improvements of the proposed algorithm.