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为了提高标准扩展卡尔曼姿态估计算法的精确度和快速性,将运动加速度抑制的动态步长梯度下降算法融入扩展卡尔曼中,提出一种改进扩展卡尔曼的四旋翼姿态估计算法。该算法在卡尔曼测量更新中采用梯度下降法进行非线性观测,消除标准扩展卡尔曼算法在线性化时带来的线性化误差,提高算法的准确性和快速性;对梯度下降法的梯度步长进行动态处理,使算法步长与四旋翼飞行器的运动合角速度成正比,增强微型四旋翼飞行器姿态解算的动态性能;对强机动运动过程中机体产生的运动加速度进行抑制处理,消除运动加速度对姿态解算的不利影响,提高了微型四旋翼飞行器姿态解算的跟踪精度。为了验证所设计算法的可行性和有效性,基于STM32单片机搭建四旋翼实验平台系统进行实时在线性能验证。结果表明,所设计算法能提高四旋翼飞行器在强机动、高速运动情况下的姿态跟踪精度、动态性能,增强姿态融合算法的抗干扰性,保证微型四旋翼飞行器的稳定飞行。
In order to improve the precision and fastness of the standard extended Kalman attitude estimation algorithm, a dynamic step gradient descent algorithm with motion acceleration suppression is incorporated into extended Kalman, and an improved quadruped rotor attitude estimation algorithm is proposed. This algorithm uses gradient descent method to do nonlinear observation in Kalman measurement and updating, which can eliminate the linearization error caused by the standardization extended Kalman algorithm in linearization and improve the accuracy and speed of the algorithm. The gradient step gradient step Length dynamic processing, the algorithm step size and the quadrotor are close to the corner of the angular velocity of the vehicle to enhance the dynamic performance of the attitude of the micro-quadrotor aircraft attitude; strong motor movement in the process of generating the body motion acceleration suppression, elimination of acceleration The unfavorable influence on the attitude solution improves the tracking precision of the attitude of the quadrotors. In order to verify the feasibility and effectiveness of the proposed algorithm, a real-time on-line performance verification of the quadrotor experimental platform system based on STM32 was implemented. The results show that the proposed algorithm can improve the attitude tracking accuracy and dynamic performance of the four-rotor aircraft under strong maneuver and high-speed motion and enhance the anti-jamming performance of the attitude fusion algorithm and ensure the stable flight of the micro-quadrotor.