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 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.

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.

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.

This work investigates the pseudo-command restricted problem for tailless unmanned aerial vehicles with snake-shaped maneuver flight missions. The main challenge of designing such a pseudo-command restricted controller lies in the fact that the necessity of control allocation means it will be difficult to provide a precise envelope of pseudo-command to the flight controller; designing a compensation system to deal with insufficient capabilities beyond this envelope is another challenge. The envelope of pseudo-command can be expressed by attainable moment sets, which leave some open problems, such as how to obtain the attainable moment sets online and how to reduce the computational complexity of the algorithm, as well as how to ensure independent control allocation and the convexity of attainable moments sets. In this article, an innovative algorithm is proposed for the calculation of attainable moment sets, which can be implemented by fitting wind tunnel data into a function to solve the problems presented above. Furthermore, the algorithm is independent of control allocation and can be obtained online. Moreover, based on the above attainable moment sets algorithm, a flight performance assurance system is designed, which not only guarantees that the command is constrained within the envelope so that its behavior is more predictable, but also supports adaptive compensation for the pseudo-command restricted controller. Finally, the effectiveness of the AMS algorithm and the advantages of the pseudo-command restricted control system are validated through two sets of independent simulations.

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.