GRU-ARX model-based adaptive error compensation predictive control strategy with application to quadrotor

For a class of nonlinear dynamic systems, accurately characterizing the dynamic characteristics by building their physical models is still challenging. To deal with this issue, a novel deep learning network architecture, gated recurrent unit (GRU) neural network-based ARX model (GRU-ARX model), is developed in this study. In this model, the GRU network is executed to capture potential nonlinear mapping features of the system. And the pseudo linear ARX structure is adopted for making controller design easier, with the state-dependent parameters updated at each execution point. In view of this model, the model predictive control (MPC) algorithms for controlling the real nonlinear plant can be availably designed. However, faced with the appearance of sensibility with respect to internal or/and external factors in practical applications, the time-varying model may not perform well in control accuracy and robustness specification. Consequently, the operation of selecting the correction coefficients adaptively is combined with the MPC strategy to establish the adaptive MPC protocol focused on the error compensation, allowing for achieving the improved control accuracy and performance. Especially, the designed GRU-ARX model-based control algorithms, without and with the adaptive error compensation law are successfully applied to a practical quadrotor system, and the effectiveness of the accessed algorithms can be demonstrated by comparative results of real-time control experiments. These outcomes showcase that the proposed adaptive error compensation MPC algorithm exhibits superior control performance compared to other model-based controllers in trajectory tracking and anti-interference experiments, revealing its advantages over the traditional MPC algorithm.