Trajectory and Parameter Optimization in Robust Tracking Control of a Quadrotor

Research on combined control methods for quadrotors has focused on trajectory tracking, robust control, neural networks, parameter optimization, and path planning. While previous studies have not fully integrated all of these aspects, this study presents a comprehensive control framework that seamlessly combines robust control strategies, neural network-based uncertainty approximation, path planning, and optimization to achieve precise and reliable trajectory tracking of quadrotors operating under significant model uncertainties and external disturbances. At the heart of the framework is the Integrated sliding mode control (Intergrated SMC), a design that merges the inherent robustness of sliding mode control with the adaptive approximation capability of radial basis function (RBF) neural networks. The fusion of these two elements not only ensures stability but also strengthens the system’s resilience, delivering high-precision tracking even in the presence of unmodeled dynamics and external disturbances. The framework also incorporates the rapidly-exploring random tree star (RRT*) algorithm for trajectory planning, allowing the generation of collision-free and asymptotically optimal reference paths capable of navigating environments with complex obstacle distributions. In addition, particle swarm optimization (PSO) is employed to systematically tune the controller gains and neural network parameters, thereby enhancing overall control performance. Extensive simulations under varying conditions of model mismatch and disturbances confirm the superior performance of the proposed integrated approach, demonstrating significant improvements in tracking accuracy and disturbance rejection compared to conventional control methods. This unified architecture thus provides a robust and computationally efficient solution for quadrotor trajectory tracking, maintaining high performance even in the presence of model uncertainties and external disturbances.