A method based on deep neural network (DNN) optimized model predictive control (MPC) and standoff fusion is proposed to address the problem of tracking moving target trajectory planning for UAV swarms in uncertain environments. Firstly, online UAV trajectory planning is carried out based on the optimised MPC algorithm, and the standoff algorithm is introduced to achieve formation keeping and online obstacle avoidance targets, which in turn solves the problem of high probability of tracking target loss due to the limitation of the detection range of the UAV swarm. To avoid the local saturation problem of the control method, an improved MPC model is constructed by combining anti-windup algorithms. At the same time, the integration of DNN with the improved MPC algorithm effectively addresses the large-scale, multi-constraint optimization problem of UAV swarm control. Based on an approximate optimal control strategy, a dynamic control system model is constructed, which can rapidly adapt to external disturbances and adjust the control inputs. This approach compensates for the limitations of traditional MPC, which relies on precise prior models. Simulation results show that the improved MPC fusion algorithm is more stable in formation maintenance, faster in convergence, and more effective in monitoring targets compared to the single MPC algorithm. It enables more precise control with higher robustness. This approach is more aligned with the real-world flight requirements of UAV swarms and serves as an effective pathway for applying improved MPC methods to multi-agent control.

In this paper, an intelligent algorithm integrating model predictive control and Standoff algorithm is proposed to solve trajectory planning that UAVs may face while tracking a moving target cooperatively in a complex three-dimensional environment. A fusion model using model predictive control and Standoff algorithm is thus constructed to ensure trajectory planning and formation maintenance, maximizing UAV sensors’ detection range while minimizing target loss probability. Meanwhile, with this model, a fully connected communication topology is used to complete the UAV communication, multi-UAV formation can be reconfigured and planned at the minimum cost, keeping off deficiency in avoiding real-time obstacles facing the Standoff algorithm. Simulation validation suggests that the fusion algorithm proves to be more capable of maintaining UAVs in stable formation and detecting the target, compared with the model predictive control algorithm alone, in the process of tracking the moving target in a complex 3D environment.

Deep reinforcement learning (DRL) allows unmanned aerial vehicles (UAVs) to learn control policies for tasks in complicated and unfamiliar environments, hence it is widely employed in the field of UAV flight control. However, the model and operational environment of UAVs are typically simplified, rendering them unrepresentative of the real world. Furthermore, using only a single sensory data to control UAV flight is difficult to realize autonomous decision-making of UAVs. In this paper, an end-to-end flight control method for UAVs based on multimodal data fusion and Soft Actor-Critic (SAC) algorithm is proposed, named MD-SAC. First, this paper constructs the UAV model that is basically consistent with the real physical model, and forms a UAV multidata fusion state space including UAV information, UAV and target information and UAV sensor sensing information. Then, the strategy of directly mapping the multimodal data fusion results to the UAV torque and thrust is proposed to construct an end-to-end UAV hierarchical control model, and the convergence of the control method is accelerated based on the empirical playback mechanism. The experimental results show that the UAV based on the MD-SAC algorithm can effectively complete autonomous trajectory planning and adapt to a variety of complex environments, and the performance is improved in terms of robustness and generalization compared with the PPO algorithm and the optimized SAC algorithm.

Addressing the path planning problem for multiple wheeled mobile robots (WMRs) in uncertain environments, this paper proposes a multi-WMR path planning algorithm based on the fusion of artificial potential field and model predictive control. Firstly, an artificial potential field model for uncertain environments is established based on the APF method. Secondly, an MPC optimal controller that considers the artificial potential field model is designed to ensure the smooth avoidance of moving and concave obstacles by multiple WMRs in uncertain environments. Additionally, a formation control algorithm based on an enhanced APF method and the leader–follower algorithm is proposed to achieve formation maintenance, intra-formation collision avoidance, and obstacle circumvention, thereby ensuring formation stability. Finally, two sets of simulation experiments in uncertain environments demonstrate the effectiveness and superiority of the proposed method compared to the APF-MPC algorithm, enabling the control of multiple WMRs to reach their target positions safely, smoothly, and efficiently. Furthermore, two sets of real-world experiments validate the feasibility of the algorithm proposed in this paper.

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.