College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.
Math Biosci Eng. 2021 Feb 19;18(2):1774-1793. doi: 10.3934/mbe.2021092.
In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy.
本文采用优化的径向基函数神经网络（RBFNN）为带有未知干扰的四旋翼飞行器构建滑模控制（SMC）策略。首先，建立被控四旋翼飞行器的动力学模型，其中明确考虑了一些未知的外部干扰。然后，对四旋翼飞行器的位置和姿态进行 SMC 控制。然而，在得到的控制器中存在未知干扰，因此使用 RBFNN 来逼近控制器的未知部分。进一步，采用基于最小化绝对逼近误差的粒子群优化算法（PSO）来提高控制器的性能。此外，还证明了四旋翼飞行器的状态跟踪误差的收敛性。为了说明所提出的控制策略的优越性，对基于 RBFNN 的 SMC 控制器与带 PSO 和不带 PSO 的控制器进行了比较。结果表明，带 PSO 的策略实现了更快、更平滑的轨迹跟踪，验证了所提出控制策略的有效性。
