Using adaptive dynamic programming (ADP), this paper presents a novel attitude-tracking scheme for over-actuated tailless unmanned aerial vehicles (UAVs) that integrates control and control allocation while accounting for nonlinearity and nonaffine control inputs. The proposed method uses the idea of nonlinear dynamic inversion to create an augmented system and converts the optimal tracking problem into an optimal regulation problem using a discounted performance function. Drawing inspiration from incremental control, this method achieves optimal tracking control for the nonaffine system by simply using a critic-only structure. Moreover, the unique design of the performance function ensures robustness against model uncertainties and external disturbances. The ADP method was found to outperform traditional control architectures that separate control and control allocation, achieving the same level of attitude-tracking performance through a more optimized approach. Furthermore, unlike many recent optimal controllers for nonaffine systems, our method does not require any model identifiers and demonstrates robustness. The superiority of the ADP-based approach is verified through two simulated scenarios, and its internal mechanism is further discussed. The theoretical analysis of robustness and stability is also provided.
This paper aims to identify the main cause of the hose whipping phenomenon (HWP) in air refueling, and come up with effective preventive measures. The system-theoretic accident model and process (STAMP), i.e., the system-theoretic process analysis (STPA), was adopted to evaluate the safety of air refueling. Then, the evaluation results were verified with a self-designed simulation validation model. The results show that the HWP is controlled by the docking speed, reel mechanism, and designed hose length; the swing range and tension change of the hose increased under inappropriate speed control; reel control could end the hazardous state of the hose within 50s after docking; the HWP occurred after the hose length was shortened from 22m to 14m. The research findings provide a reference for the prevention of the HWP.
This paper presents an incremental backstepping sliding-mode (IBS) controller for trajectory control of a tailless aircraft with unknown disturbances and model uncertainties. The proposed controller is based on a nonlinear dynamic model of the tailless aircraft. A stability enhancer (SE) that limits both the rate and amplitude of the virtual control input is proposed. The stability enhancer consists of two layers. When the virtual control input approaches the edge, the first layer SE would be activated to modify the trajectory tracking error; when the virtual control input exceeds the edge, the second layer SE would reduce the control gains to make sure the virtual control input drops within the edge as soon as possible. With the help of SE, the incremental control method could be extended to outer-loop control without considering the dynamics of the inner-loop system. In addition, an adaptive estimator for state derivatives is proposed, together with IBS, allowing the controller to show excellent robustness. Finally, two simulations are presented. The first simulation shows that the system is insensitive to external disturbances and model uncertainties, and the effectiveness of SE is proved in the second simulation.
To address the control allocation problem caused by the redundant arrangement of control surfaces with nonlinear effectiveness for tailless aircraft, a novel multiobjective incremental control allocation (MICA) strategy is proposed. Firstly, the incremental nonlinear control allocation (INCA) method together with the active set quadratic programming algorithm is adopted to precisely allocate the virtual control commands. Secondly, a series of normalized objective functions in the form of increment are designed. Combining these functions by means of linear weighted sum, an incremental multiobjective function is constructed. Then, an improved nondominated sorting genetic algorithm (INSGA) is introduced to offline determine a set of weights that best meets the requirements of each flight phase. In this way, the dependence on subjective experience is minimized based on the theory of Pareto optimal. Meanwhile, the huge computational burden that the intelligent optimization algorithm brings can also be avoided. Finally, combined with the nonlinear dynamic inversion (NDI) control method, a closed-loop validation for the effectiveness of this control allocation strategy is carried out on the tailless aircraft model.
The slow convergence rate and large cost of the initial solution limit the performance of rapidly exploring random tree star (RRT*). To address this issue, this paper proposes a modified RRT* algorithm (defined as FF-RRT*) that creates an optimal initial solution with a fast convergence rate. An improved hybrid sampling method is proposed to speed up the convergence rate by decreasing the iterations and overcoming the application limitation of the original hybrid sampling method towards concave cavity obstacle. The improved hybrid sampling method combines the goal bias sampling strategy and random sampling strategy, which requires a few searching time, resulting in a faster convergence rate than the existing method. Then, a parent node is created for the sampling node to optimize the path. Finally, the performance of FF-RRT* is validated in four simulation environments and compared with the other algorithms. The FF-RRT* shortens 32% of the convergence time in complex maze environment and 25% of the convergence time in simple maze environment compared to F-RRT*. And in a complex maze with a concave cavity obstacle, the average convergence time of Fast-RRT* in this environment is 134% more than the complex maze environment compared to 12% with F-RRT* and 34% with FF-RRT*. The simulation results show that FF-RRT* possesses superior performance compared to the other algorithms, and also fits with a much more complex environment.
To address the faults of the tailless aerial vehicle control surface with nonlinear control effectiveness, a reconfiguration incremental control allocation method is proposed. Under the framework of incremental control allocation, four types of typical control surface faults, such as floating, jamming, damaged, and median offset, are modeled. The isolation of faulty control surfaces and the reconfiguration of the flight control system is realized by adjusting the control surface deflection increment limit and changing the local control effectiveness matrix. The results show that the method can effectively take advantage of the redundant configuration of the control surface, prevent the system from losing control caused by the fault of the control surface, and enhance the safety and reliability of the system.
This paper presents a fault-tolerant attitude control scheme, incorporating reconfiguration control allocation for supersonic tailless aircraft subject to nonlinear characteristics, actuator constraint, uncertainty, and actuator faults. The main idea is to propose an incremental reconfiguration closed-loop control allocation scheme, coupled with a basic backstepping attitude controller, to achieve attitude control. Based on the virtual control input generated by the basic backstepping attitude controller, firstly, the incremental nonlinear control allocation method is adopted to deal with the nonlinear characteristics and actuator constraint. Secondly, a distribution error feedback loop is constructed in the incremental nonlinear control allocation method to enhance the robustness against the uncertainty of the control effectiveness matrix. Thirdly, the control effectiveness matrix is reconstructed by different kinds of fault information to deal with actuator faults, and the proper combination of actuator deflections is generated to achieve accurate command tracking. The stability of the proposed scheme is guaranteed by the Jury stability criterion and the Lyapunov stability analysis. Finally, in comparison with the three existing approaches, the simulation results of two cases are provided to show the effectiveness of the proposed scheme.
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