Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

To address the limitations of existing sliding mode-based integrated guidance and control (IGC) schemes, such as chattering, input saturation, and insufficient robustness, this paper proposes a three-dimensional IGC design method incorporating both terminal angle and attitude angle constraints. First, a control-oriented six-degrees-of-freedom model is established based on three-dimensional relative motion and vehicle dynamics, and the control objectives for maneuvering target interception under multiple constraints are clarified. Subsequently, a finite-time terminal sliding mode guidance law based on time-to-go (TGO) is integrated with dynamic surface control to construct the IGC framework. In this design, command filters are introduced to overcome the “explosion of complexity”, while amplitude saturation functions are employed to constrain system states and control inputs. Meanwhile, a generalized super-twisting extended state observer (GSTESO) is incorporated to estimate and compensate for lumped uncertainties in the system. Finally, by combining Lyapunov stability theory with an integral barrier Lyapunov (IBL) function, it is proven that the closed-loop system is uniformly ultimately bounded and satisfies the terminal angle constraints. Comparative simulations under multiple disturbance scenarios demonstrate that the proposed method meets the accuracy requirements in terms of miss distance and LOS angle error. Moreover, it alleviates high-frequency chattering and prevents control-input saturation, showing improved robustness and disturbance rejection capability compared with the baseline methods. Therefore, the proposed approach provides a valuable reference for engineering applications of three-dimensional IGC in maneuvering target interception.