In this article, a novel adaptive sliding mode control strategy is presented for attitude and altitude stabilization of a quadrotor unmanned aerial vehicle (UAV), where the input saturation is taken into consideration. The nonlinear dynamic model of quadrotor UAV subject to system uncertainty and external disturbance is first derived using the Euler–Lagrange mechanical equation. To ensure system stability, as well as eliminating the negative effects of saturation features, a saturated adaptive sliding mode control law (ASMCSAT) is proposed, which takes advantages of the sliding mode control and the adaptive control. Compared with conventional control strategies, better tracking performance and disturbance and uncertainty rejection can be achieved with the root mean square of the tracking error being reduced by 34.91% in vertical takeoff and 30.11% in trajectory tracking. Simulations and experiments are provided to verify the effectiveness and practicability of the proposed control method.

In this article, a fixed-time observer-based sliding-mode control strategy is proposed for a permanent-magnet synchronous motor. Both the current regulation loop (inner loop) and the speed-tracking loop (outer loop) are controlled by the designed fixed-time convergence sliding-mode controller, improving the control performance and guaranteeing the convergence in a fixed-time manner. To guarantee the robustness of the proposed control algorithm, an observer is constructed to estimate and attenuate the parameter uncertainties and load disturbance in the outer loop. The fixed-time convergence of the inner and outer loop error systems are proved. Moreover, a parameter tuning method of the designed controller is proposed to provide guidance for practical applications. Finally, experiments are conducted by using three different control schemes, that is, PI control, super-twisting sliding-mode control, and the controller designed in this article, the results of which show the effectiveness and advantages of the proposed control scheme.

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing–bundling–bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to solve with some practical constraints and requirements. To improve the efficiency of the solution for the penetration path, a novel variable-time mechanism has been constructed to adapt to the updated delay time of unknown target search (UTS) and dynamic trajectory planning (DTP) two stages. The occupancy grid maps are established by a Gaussian probability field (GPF) for predicting the positions of enemy UAVs. To fully consider the hostile obstacle constraint, a hybrid adaptive obstacle avoidance approach dynamic window PRM (DW-PRM) is designed to shorten the planned path. The penetration strategy algorithm (SG) is developed based on the proposed strategy set and decision tree. To improve the ability of dynamic obstacle avoidance, the multiple coupled penetration homotopy trajectory is addressed with a turning radius constraint. The simulation results indicated that the penetration homotopy framework for multi-constraints can solve the multi-UAV PMP problem.