In this paper, a health status assessment scheme is studied for the attitude control system (ACS) of fixed-wing unmanned aerial vehicle (FWUAV) based on an improved multivariate state estimation technology that incorporates a dynamic memory matrix. Firstly, the parameters of the FWUAV representing the health status of the ACS are selected as feature parameters, and the historical health data of the feature parameters for the FWUAV is compared with the real-time test data. At the same time, the multivariate state estimation technology is applied to obtain the abnormal degree of components for the ACS. Based on the analytic hierarchy process and the expert experience, the weights are obtained for the different elements at the same functional level of the ACS. Secondly, the functional structure of the ACS is analyzed, and the abnormal degree is calculated for the ACS by combining the concept of reconfigurability and the weight information of each element, and the health status assessment indicators are further established. The health status result for the ACS of the FWUAV is acquired according to the efficiency value of the indicators and the abnormal degree of the ACS. Finally, the effectiveness of the developed algorithm is verified by the simulation analysis.

In this paper, a resilient tracking control scheme with cooperative collision avoidance performance is studied for the fixed-wing unmanned aerial vehicle (UAV) leader-follower formation in the presence of actuator failures and external disturbances. Firstly, based on the control objectives of UAV formation tracking and collision avoidance, the transformation tracking errors are obtained using the prescribed performance control technique. Next, a fault detection mechanism is introduced to determine if there is the actuator fault. Subsequently, the event-triggered resilient fault observers are designed based on a dynamic event-triggered mechanism to estimate actuator faults. Furthermore, the prescribed performance functions and the H∞ performance index are employed to ensure the UAV formation collision-free and mitigate the impact of disturbances. Moreover, the resilient controller is designed to minimize the effect of the perturbations for the control gain and the fault observer gain on the system. The stability of the system is also proven through the Lyapunov stability analysis, and the controller gains are calculated by solving the linear matrix inequality. Finally, the validity of the proposed control strategy is demonstrated by the simulation analysis.

Aiming at the problem of cooperative combat problem for multiple unmanned autonomous helicopters (UAHs) in complex battlefield environments, a hybrid enhanced artificial bee colony (ABC) algorithm-based cooperative multi-task assignment method is proposed in this paper. Firstly, by analyzing the battlefield requirements of UAH clusters in multi-target scenarios, a complex constrained task assignment optimization model considering practical application scenarios is established. Furthermore, a hybrid enhanced ABC algorithm is proposed based on cognitive-psychological learning (CPL). The proposed CPL-ABC algorithm integrates swarm intelligence and human cognitive mechanisms to guide the evolutionary direction of the population by introducing individual expectation effects and personality differences. In addition, an introspection evolution mechanism based on individual variability is introduced to enhance the local optimal escape ability of the ABC algorithm. Through the reflection and learning of locally optimal individuals, the uncontrollable influence of direction caused by random evolution is reduced. Finally, the simulation and experiment results verify the effectiveness and feasibility of the proposed cooperative multi-task assignment method.

In this article, an adaptive event-triggered constrained control strategy is proposed for uncertain nonlinear systems with input constraints by using reinforcement learning (RL) technology and disturbance observer (DO). By constructing an actor-critic neural network (NN) framework, the unknown uncertainties can be tackled by online learning and more accurate compensation. The actor-NN is adopted for generating actions (regarded as compensation signals), and the critic-NN is employed to evaluate the performed actions (regarded as to monitor and assess the actor-NN performance, including the control performance). Moreover, a self-learning DO with learning ability is designed to estimate the external disturbance. On the basis of the backstepping control technology, the event-triggered control (ETC) method and the smooth approximation of input saturation nonlinearity, an improved event-triggered constrained control strategy is presented using RL technique, and the rigorous theoretical proofs of the closed-loop system stability and the avoidance of Zeno behavior are presented. The application for the quadrotor unmanned aerial vehicle (UAV) validates the effectiveness of the developed ETC approach.

Based on fully actuated technology, a sliding mode control approach is suggested for helicopter systems with all degrees of freedom. First, the degree of nonlinearity and coupling is lessened by applying the feedback linearization technique. And the helicopter systems transform into fully actuated systems. Secondly, a sliding mode controller is built with the assumption of system stability in order to achieve the intended tracking task. Third, the reliability of complete closed-loop structures is investigated using Lyapunov theory. Finally, this work presents simulation data to validate the effectiveness of the designed control technique.

Aiming at the problem of external composite disturbance in the conversion flight mode of the tilt-rotor aircraft, a robust resilient switching flight control scheme based on the disturbance observer is developed. First of all, the linear longitudinal kinematic switching model of the aircraft is given, and the switching rule based on the average dwell time is designed. Then, the external disturbance is compensated by the designed disturbance observer, the H∞ control method is used to suppress the energy-bounded disturbance. In addition, the non-fragile control method is introduced to reduce the effect of controller gain perturbations. With the Lyapunov function, the controller gains are solved by the linear matrix inequality toolbox. Finally, the simulation results demonstrate the effectiveness of the proposed method.

The modeling and state tracking control for the conversion mode of tilt-rotor aircraft are investigated with a switching modeling method and a bumpless transfer technique. Based on the Euler equation and the Blade element theory, the nonlinear model of conversion mode is deduced to illustrate the features of the tilting process. Afterward, the switching modeling method settles the linearization problem of the nonlinear control with an uncontrollable nacelle inclination signal. Meanwhile, it also transfers the conversion mode control issue to a reference state tracking control problem of switched linear systems. In addition, the [Formula: see text] control is adopted to restrain the external disturbances, and the controller with bumpless transfer performance is designed to overcome the control jump at switching instants. Finally, an example of XV-15 tilt-rotor aircraft is proposed to illustrate the effectiveness of the developed method.

Aiming at the issue that the artificial potential field(APF) does not consider the velocity information of dynamic obstacles, which may lead to the issue that the obstacle avoidance path is too long when the speed of the obstacle is close to the unmanned vehicles, this paper designs a new artificial repulsion potential field function, which introduces the velocity information of the obstacle, so that the unmanned vehicle is more inclined to avoid from the opposite direction of the velocity of the obstacle. In addition, a distance factor is introduced on the basis of the new repulsion potential field function, to overcome the issue of unreachable targets in traditional algorithms. Finally, the simulation shows that the improved algorithm makes the unmanned vehicle choose a more reasonable route when avoiding dynamic obstacles, and achieve good dynamic obstacle avoidance effect when there are multiple different obstacles.

The tracking control problem of trajectory planning is studied in this paper based on prescribed performance method (PPM) for the small-scale unmanned autonomous helicopter (UAH) with wind-gust disturbances (WGDs) and unmeasurable states. For the purpose, the nonlinear model with flapping dynamics is established, and the transformation performance function is used to ensure that the errors of trajectory tracking satisfy the corresponding performance. The fractional-order observers are investigated to estimate the longitudinal and lateral flapping angles that are treated as unmeasurable states, and estimate the WGDs, respectively. Based on PPM and the designed observers, the fractional-order theory-based backstepping trajectory tracking control scheme is developed for the UAH system, and the three-dimensional trajectory is planned by the improved wolf pack algorithm. Then the stability of the entire system is proven through strict theoretical analysis. Finally, the simulation analysis on the UAH are presented to demonstrate the efficiency of the designed method.

In this paper, a fractional-order sliding mode control (FOSMC) method is designed for the permanent magnet synchronous motor (PMSM) based on the disturbance observer (DO). Fractional operators with the characteristics of memory and multiple degrees of freedom are combined with sliding mode control to improve the control performance. To handle the unknown disturbances, a DO is employed to estimate disturbance. The convergence of the tracking error while arriving sliding mode surface is proved by using Laplace transform method. All signals in the closed-loop system under the DO based FOSMC are bounded. Simulation and experiment based on the PMSM experimental platform verify the utility and persuasiveness of this control strategy.

The trajectory planning and tracking control problem is studied in this paper based on prescribed performance method (PPM) for the small-scale unmanned autonomous helicopter (UAH) with wind-gust disturbances (WGDs) and unmeasurable states. For the purpose, the nonlinear model with flapping dynamics is established, and the transformation performance function is used to ensure that the errors of trajectory tracking satisfy the corresponding performance. A fractional-order observer is designed for unmeasurable states to estimate the flapping angles in actual flight, and the fractional-order extended state observer (ESO) is constructed to estimate the WGDs, respectively. On the basis of PPM and the designed observers, the fractional-order theory-based backstepping trajectory tracking control scheme is developed for the UAH system, and the three-dimensional trajectory is planned by the improved wolf pack algorithm. Then the stability of the entire system is proven through strict theoretical analysis. Finally, the simulation analysis on the UAH is presented to demonstrate the efficiency of the designed method.

In this paper, the obstacle avoidance formation control problem of unmanned ground vehicles(UGVs) is discussed with a game-theoretic approach. Specifically, each UGV needs to track the target point and avoid obstacles in real-time while the UGVs system is in cooperative formation. Based on the Nash equilibrium seeking method, the UGVs formation problem is transformed into a non-cooperative game problem among UGVs, an obstacle avoidance term is added to the cost function and a controller is designed. It is proved that under the action of the designed controller, the UGVs system can achieve the formation mode of goal equilibrium. Finally, the validity of the results is attested by simulation.

In this paper, a resilient flight control scheme is proposed for the unmanned aerial vehicle (UAV) with system uncertainties, external disturbances, actuator faults and controller gain perturbations. Radial basis function neural network (RBFNN) is employed to tackle actuator faults and system uncertainties. The nonlinear disturbance observer (NDO) is used to estimate the approximation errors of RBFNN and the external disturbances. By using RBFNN and NDO, the resilient flight control scheme is developed for the UAV attitude system based on the command filtered backstepping control method. An optimization method using the linear matrix inequality is studied for the feedback gain matrix design under gain pertubations. Under the resilient flight control scheme, the uniformly ultimate bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results show the effectiveness of the proposed resilient flight control scheme.

In this article, a robust discrete‐time fractional‐order tracking control scheme based on a discrete‐time disturbance observer (DTDO) is proposed for an unmanned aerial vehicle (UAV) system in the presence of unknown bounded variation disturbances. A DTDO is first constructed to estimate the unknown bounded disturbance. On the basis of the output of the DTDO and the discrete‐time fractional‐order theories, a robust discrete‐time fractional‐order control scheme is then designed by using the backstepping control technique for a discrete‐time UAV system with disturbances. The proposed control scheme is expected to guarantee the stability of the closed‐loop system with all system signals bounded. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed control scheme.

针对三维地图中的无人机航迹规划问题,提出了一种基于改进精英蚁群算法的航迹规划算法.将算法中的状态转移策略与人工势场法进行融合设计,为障碍物和目标点分别设置斥力场和引力场,指导航迹搜索方向.添加约束条件限制,使航迹能实际可飞.随后当信息素更新时,设置双精英蚂蚁策略和混沌扰动,提高算法的全局搜索能力.引入视线算法减少航迹节点数,平滑航迹.仿真结果表明,搜索所得的无人机航迹均符合需求.

针对无人机飞行过程中遇到威胁需要及时安全规避的问题,本文提出一种基于经验直觉的无人机机动规避决策算法.首先将无人机威胁安全规避机动过程中所获取的态势信息,按照规避机动区域进行结构化处理并标注相应的机动决策标签,建立基于数据的无人机情景决策知识库.其次,利用无人机情景决策知识库中的数据训练长短时记忆网络以实现情景与机动决策之间的正确映射.然后,根据人类基于直觉进行行动决策的原理,设计无人机基于经验直觉的机动规避算法.最后,通过仿真分析表明所提出无人机威胁规避算法的有效性.

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针对存在非线性、强耦合、外部未知有界干扰和建模不确定性的平面运动下无人直升机吊装系统,研究了一种基于径向基函数神经网络(radial basis function neural networks,RBFNNs)和干扰观测器的无人直升机吊装系统滑模减摆控制方法.首先将系统模型转换成仿射非线性形式,利用RBFNNs逼近系统不确定性,设计干扰观测器估计神经网络逼近误差与外界未知有界干扰的复合值.然后基于RBFNNs和干扰观测器设计了滑模减摆控制器,并用Lyapunov方法证明闭环系统稳定性;最后通过仿真验证了所设计控制器的有效性.

In this paper, based on a disturbance observer (DO), an adaptive neural network (ANN) tracking control scheme is proposed for the multi-input and multi-output (MIMO) strict-feedback discrete-time system (SFDTS). The unknown nonlinear functions, dead-zone input and external disturbance are all considered in the studied SFDTS. Before starting to design the controller, the MIMO SFDTS is transformed into a maximum N-step ahead predictor to solve the noncausal problem. Then, the backstepping method is successfully used to design the control scheme for the new system. The unknown nonlinear functions are approximated by radial basis function neural networks. The external disturbance is estimated based on the DO, and the ANN controller is designed on the basis of the outputs of the DO. By applying the Lyapunov stability theory, all the signals in the whole closed-loop system are ensured bounded. Finally, a numerical simulation is provided to verify the validity of the proposed control scheme.

In this article, an adaptive prescribed performance tracking control (PPTC) is studied for the high angle of attack (AOA) maneuver of unmanned aerial vehicle (UAV) system by considering full-state constraints and disturbances. The radial basis function neural networks (RBFNNs) are employed to approximate uncertain functions, and a disturbance observer design is constructed to deal with the unknown disturbance based on an auxiliary system. Time-varying Barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lying in control error sets. To solve the potential singularity problem that the denominator of the Barrier function term approaches zero in controller design, the adaptive prescribed performance tracking control scheme is proposed. In the end, the effectiveness of the designed control scheme is verified by simulation experiments.