Since collision can change the velocity in a very short time, a collision-inclusive trajectory planning algorithm for unmanned aerial vehicle (UAV) can utilize collision to get a fast and energy-efficient trajectory in complex environments. We proposed a lightweight collision-inclusive trajectory planner, which can be integrated into a UAV system easily. The trajectory segments that need to be optimized are recognized by the curvature of the collision-free trajectory. After getting the pre-collision information by forward integration of sampled control, the optimal collision position and time will be generated by the collision-inclusive optimizer in 40ms. The experiments verify the effectiveness and efficiency of our method.

In this study, we analyze a multi-UAV target-attacker-defender (TAD) differential game framework in which multiple defenders is tasked with shielding a target from several attackers. The attackers are driven by the objective of seizing the target, while the defenders focus on intercepting their advances. Hierarchical reinforcement learning (HRL) framework offers a promising strategy for the TAD problem. This work introduces a two-level goal-conditioned HRL method. At the first level, defenders are dynamically paired with target attackers through an assignment mechanism guided by differential game theory, where optimal intercept trajectories are computed to inform the matching process. The second level employs a multi-agent deep deterministic policy gradient (MADDPG) algorithm to derive coordinated policies for both teams. Experimental validation demonstrates the framework’s effectiveness compared to baseline method.

This paper proposes a prescribed-time disturbance estimation method for nonlinear systems with unknown nonlinear disturbances, such as aerial manipulators. Taking full account of the fast convergence characteristics of prescribed-time theory and the stability and realizability of the Extended State Observer (ESO) in nonlinear disturbance estimation, this paper integrates the two by introducing bounded time-varying gains. This approach solves the spike problem of traditional ESO at the initial moment and extends the applicability of prescribed-time theory to the full time interval, effectively enhancing the real-time performance and accuracy of disturbance estimation. The effectiveness of the proposed method is verified through simulations.

Autonomous tracking of dynamic targets by unmanned ground vehicles (UGVs) is a crucial problem in various applications. However, existing approaches often suffer from low computational efficiency, inadequate handling of target visibility and limited adaptability to the rapid motion of the target. To address these limitations, this paper proposes a planner that combines target prediction-based topological path generation with a spatial-temporal trajectory optimization method. The future topological paths of target are predicted via motion primitives, and local topological search generates multiple candidate tracking paths for the tracker. Then an optimal path is selected on the basis of visibility cost, energy cost, and topological consistency. The spatial-temporal optimization method generates a smooth, dynamically feasible trajectory with minimum squared jerk and time. Considering both tracking distance and visibility constraints, the effectiveness of the proposed method we validate is through various simulation experiments.

This paper introduces a novel hybrid unmanned aerial vehicle (UAV) configuration named as the hybrid QuadPlane with all-moving wings (HQWAW), which features two wings capable of dynamic adjustment during flight. Compared with conventional QuadPlane, the HQWAW can optimize lift generation and reduce rotor thrust by altering its angle of attack. But the additional degrees of freedom and nonlinear dynamics pose challenges for control strategy design. We proposed an end-to-end control strategy using deep reinforcement learning (DRL). This approach enables the HQWAW to discover an optimal control policy that simultaneously improve tracking accuracy and energy efficiency, with the simulation results illustrating the effectiveness of the proposed method.

When a manipulator moves in a highly dynamic scenario with a large range of rapid motion, the coupling disturbances between the manipulator and the UAV in the aerial manipulator system (AMS) become very strong, which directly affects the ability of the AMS to perform aerial manipulation and even poses a threat to the safety of the system. The aim of this article is to address the strong coupling disturbance problem in the AMS through precise coupling disturbance modeling and compensation. First, considering the rapid changes in the center of mass (CoM) and the moment of inertia (MoI) of the system under a highly dynamic scenario, this article delves into the generation mechanism of the coupling disturbances and models them based on the variable inertia parameters. The proposed precise coupling disturbance model (CDM) makes good use of the state information of the system, which enables one to achieve accurate estimation of the coupling disturbances without the aid of external force and torque sensors. With the proposed model, the strong coupling disturbances in the AMS are compensated in a feedforward way during the controller design process. An indoor AMS experimental platform is developed for validation purposes. The experiments and simulation are conducted in a highly dynamic scenario, involving rapid movements of the manipulator across a large range. The experimental and simulation results demonstrate the effectiveness and advantages of the proposed method for suppressing the strong coupling disturbances.

This article proposes a novel safety-critical control (SCC) framework based on embedded control barrier functions (EMB-CBF-SCC) for high-order strict-feedback nonlinear affine control systems. It is aimed at reconciling the potential conflict between predesigned desired trajectory and multiple safety constraints that could have different high relative degrees. Compared with existing CBF-based SCCs, our method can significantly reduce differential order and computational burden. Specifically, we first propose a novel concept of embedded control barrier function (EMB-CBF), which can reduce an arbitrary high-relative-degree safety constraint to relative degree one, and ensure safety of high-relative-degree systems. Further, EMB-CBF-SCC divides the original system into a top-level and a bottom-level subsystem. Then, it embeds between the two subsystems a quadratic program based on EMB-CBF, and introduces command filters to smooth virtual control inputs and obtain differential signals. Coordination performance of safety and stability is analyzed, considering the impact of filter errors. Finally, we present two real safety-critical robotic application scenarios with different safety constraint settings, namely, multiple state constraints for a single-link manipulator numerical model and dynamic obstacle avoidance constraints for a self-developed micro mobile robot experimental platform, respectively. The effectiveness of the proposed framework is demonstrated in both scenarios.

This article introduces a novel composite aerial vehicle configuration called hybrid quadrotor with all-moving wings (HQWAW), consisting of a conventional quadrotor combined with two independently all-moving wings. A nonlinear geometric controller in the special Euclidean group SE(3) is proposed as the basic controller for the HQWAW, achieving high maneuverability and energy-efficient flight. Lyapunov stability criterion is used to prove that the proposed control scheme can track the reference trajectory almost globally ultimately uniformly bounded. A deep reinforcement learning compensator, based on the twin delayed deep deterministic policy gradient algorithm, is designed to fine-tune all-moving wing angles, ensuring that wing surfaces remain at optimal angles, thereby maximizing aerodynamic efficiency and reducing rotor consumption. Tracking results for a trajectory involving high-speed dive followed by spiral ascent demonstrate that the proposed algorithm achieves both high maneuverability and improved energy efficiency of the HQWAW.

Thrust-vectoring multirotor is the current cuttingedge focus of research in the field of multirotor. This technology promises advancements such as over-actuated dynamics, decoupling of position and attitude control, and the ability to track 6-Dof trajectories. Building upon our previous work on thrust-vectoring quadrotor, this paper introduces a novel thrust-vectoring hexarotor configuration. We propose a nonlinear generalized predictive control method utilizing the super-twisting extended state observer (STESO). The stability of our closed-loop system is rigorously established through theoretical analysis. To validate the practical applicability of our approach, simulation for 6-Dof trajectory tracking of the thrust-vectoring hexarotor is conducted using sophisticated high-fidelity dynamics simulation software. This simulation serve to affirm the effectiveness of our proposed system in achieving precise trajectory control and maneuverability in various flight scenarios.

This article presents a novel compound aerial vehicle configuration called the hybrid quadplane with all-moving wings (HQWAWs), and provides a six-degrees-of-freedom (6-DOF) flight dynamics modeling and nonlinear controller design for this new configuration. Compared to traditional quadrotors, the HQWAW add two wings on left and right sides of the quadrotor. Each wing is a single structure that can rotate to any angle of attack independently, which is referred to as all-moving wing (AW) in this article. The dynamic modeling of this configuration takes into account a complete description of flight dynamics, including wing aerodynamics and the dynamics of motors and propellers. A comprehensive nonlinear flight controller is proposed using model feedforward and state feedback for the HQWAW that supports the whole flight envelope. The proposed HQWAW configuration is compared with a traditional quadrotor with all parameters being the same except for the absence of the AWs. A set of numerical results demonstrate that the proposed configuration can obtain flight flexibility beyond quadrotors, while effectively reducing energy consumption.

Compared with the grasping of the manipulator on the fixed base, the dynamic grasping of the aerial manipulator on the UAV floating base is a more challenging task. The strong coupling disturbance caused by the manipulator and the load can seriously affect the position tracking performance of the UAV base, leading to the inability of the manipulator's end-effector to accurately reach the grasping position, resulting in the failure of dynamic grasping. To address the issue, this paper presents a coupling disturbance compensation method that comprehensively considers the motion of the manipulator and the load on the end-effector. It can effectively compensate the strong coupling disturbance caused by the manipulator and the load and greatly improve the position tracking performance of the UAV base. In addition, considering that the aerial manipulator is also affected by lumped disturbances such as various uncertainties and wind disturbances, we propose an end-effector position compensation method based on inverse kinematics, so that the end-effector can reach the target position more accurately during the dynamic grasping process. Finally, three sets of comparative simulation results under two scenarios demonstrate the effectiveness of the proposed method.

In this paper, the attitude tracking control problem for quadrotor with unknown dynamics and input saturation is investigated. First, characteristic model is applied to the quadrotor modeling with unknown dynamics. Second, a novel discrete-time sliding mode controller (SMC) built in characteristic model is proposed to achieve attitude tracking and eliminate the negative effect caused by input saturation. Besides, the proposed characteristic-model based discrete-time SMC has a strong robustness against disturbance. It is proved that the convergence of tracking error can be guaranteed. Finally, the effectiveness and superiority of the proposed scheme are illustrated by simulation.

In this paper, we propose four offline data augmentation methods to improve the performance of convolutional neural network(CNN) on defect detection of sanitary ceramics. In recent years, based on big data, deep learning has begun to become a popular way for sanitary ceramics defect detection. Comparing with traditional vision inspection system, deep learning method is more robust and convenient without manual design of feature extraction. As a data-driven detection way, data plays a vital roll, however, sometimes we could not obtain a high-quality and large dataset. Consequently, we consider data augmentation to improve the quality of original dataset. Here, we use image generation, image mosaic, image fusion and image rotation mosaic. According to the experiment results, with these methods, the enhanced datasets perform well compared with the original one.

Aerial manipulator is a kind of robot with broad application prospects, which is suitable for high altitude operation and other dangerous application scenarios. This paper presents a trajectory tracking control algorithm for the aerial parallel manipulator based on Stewart platform. By modeling the overall dynamics of the aerial parallel manipulator, the expression of the influence of Stewart platform is given, and it is proved that this type of influence can be combined with the unmodeled error and external disturbance into the comprehensive disturbance of the flight platform. The flight platform trajectory tracking control is carried out by using the backstepping method, and the nonlinear disturbance observer is used for disturbance estimation and compensation in the control output. Numerical experiments show that the proposed control method can realize the trajectory tracking control of the flight platform of the aerial parallel manipulator.

The robust multimodel solution for multiple-input single-output nonlinear time-delay systems identification with polluted outputs is derived in this article. First, all the local autoregressive exogenous models are preidentified at the working points; then, the global system model is built by interpolating the local models with a smoothing strategy. The outliers and input time-delays which often increase the nonideality of process data are both considered. To cope with the outliers, the Laplace distribution is reutilized to describe the output measurement process and the negative impact resulted from each outlier imposed on parameter estimation can be suppressed through automatically assigned small weight. The parameter estimation procedure is realized with the expectation-maximization algorithm and the joint posterior probability of all input delays is also maximized to calculate the unknown input time-delays. With the verifications on a numerical example and the continuous fermentation process, the validity of the proposed approach is proved.

The problem of H∞filtering to estimate the unmeasurable states is investigated in this paper and the most general multirate measurements condition is taken into account. The main result of this paper is to give a method to design a filter, which can guarantee the stability of the resultant filtering error system with H∞performance. For a given system with multirate measurements, this paper first provides a method to convert this multirate design problem into an equivalent single-rate design problem. Then, it can be proved that the design approach of a required filter based on a linear matrix inequality is a sufficient and necessary condition. Lastly, two examples are utilized to demonstrate that this design approach is effective and applicable to estimate the unmeasurable states for the given system with multirate measurements.

The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

This paper is concerned with the design of fault detection for sampled-data systems, which are based on multirate sampling, with frequency specifications. A general multirate system is considered in this paper, where not only the inputs and outputs but also their different channels have different sampling rates. The purpose of this paper is to make this residual system with multirate sampling satisfy a given disturbance attenuation level over a restricted frequency range. With the use of the lifting technique, this paper reformulates a single-rate linear time-invariant system, which is equivalent to the multirate time-varying system. For a given restricted frequency range, convex conditions are obtained in designing a required fault detection filter. Then, the restricted frequency ranges problem are also solved specifically via the generalized Kalman-Yakubovic̆-Popov lemma. Finally, this paper uses a continuous-stirred tank reactor system to illustrate the effectiveness and advantages of the fault detection filter design method.

This paper investigates the coupled disturbance reconstruction, state estimation and trajectory tracking problems for a class of nonlinear systems. The sliding mode observer and the state feedback approaches are utilized to solve these problems. Considering only a partial of the system states are measured but the disturbances couple with time-varying parameters that the observer is designed by transforming the original system into a descriptor one. Hence, from the estimated values of system states and the decoupled disturbance, one can reconstruct the coupled disturbance. Upon these estimates, a state feedback based fault tolerant controller is designed such that the system states converge to the desired trajectory. Finally, to verify the validity of our scheme, a numerical simulation together with an experiment of 3-DOF robot are offered.

The purpose of this paper is to design an adaptive reliable H∞controller for multirate sampled-data systems. Sampled-data systems are widely adopted in the industrial process and multirate sampling is abundant in such systems. Sensor failure is one of the faults exist in the control systems, which can result in the instability. A reliable controller is proposed in this article to guarantee the stability and H∞performance of the multirate sampled-data systems when sensor failures happen. The lifting technique is used to convert a multirate system to an equivalent discrete system and the idea of the substitution is utilized to address the controller design problem based on adaptive mechanism. With the use of the mathematical model of the practical F-404 engine, an example is illustrated to prove the applicability of the proposed method.

This paper deals with the problem concerning the design method of synchronous motion controllers in form of output feedback for dual linear actuators with load differences, dynamic nonlinear friction and force ripples. The authors focus on not only nonlinear friction and disturbance but also the dual motors synchronous objective. The energy upper bound for conquering disturbances is estimated and used as compensation. After that, in order to improve the robustness of the dual-motor motion plant in the presence of external disturbances, an interference rejection approach is presented. Furthermore, due to load variation which degrades synchronous tracking performance for dual motors, the controller design method based on the convex optimization scheme is proposed. The illustrative examples show that the controller can significantly improve the tracking performance under nonlinear frictions. In the meanwhile, the disturbances with known model and random one can be restrained well.

In this note, we study the problems of stability analysis and H∞controller synthesis of discrete-time switched systems with time-varying delay. The system under consideration is firstly transformed into an interconnection system. Based on the system transformation and the scaled small gain theorem, the asymptotic stability of the original system is examined via the version of the bounded realness of the transformed forward system. The aim of the proposed approach is to reduce conservatism, which is made possible by a precise approximation of the time-varying delay and the input-output approach. The proposed stability condition is demonstrated to be much less conservative than most existing results. Moreover, the problem of H∞controller synthesis involving convex optimization is further solved based on the stability condition, whose effectiveness are also illustrated via numerical examples.