This article proposes a trajectory tracking strategy for nonuniform quay cranes to suppress flexible cable vibration and attenuate payload swing and rotation, thereby improving tracking accuracy and transport efficiency. To address the challenges posed by time-varying and spatially distributed partial differential equation models, we propose a Bayesian physics-informed neural network (BPINN) framework that integrates tension constraints into the loss function to suppress flexible cable vibrations. In the Bayesian setting, the BPINN acts as a prior model, and Hamiltonian Monte Carlo (HMC) sampling is employed to infer the posterior distribution of the system states. To handle the underactuated nature of the quay crane, differential flatness is exploited to map BPINN-predicted states into a flat output space, where an adaptive backstepping controller is designed to guarantee global uniform ultimate boundedness. Moreover, a multistrategy improved quantum-behaved particle swarm optimization (MIQPSO) scheme is introduced for online tuning of control parameters, achieving a favorable tradeoff between global exploration and fast convergence. Lyapunov analysis establishes closed-loop stability, and simulations and experiments demonstrate fast and accurate tracking as well as robust vibration suppression under external disturbances.

Load transportation using unmanned aerial vehicles (UAVs) presents both intriguing possibilities and significant challenges in research and practical applications. This study aims to present a comprehensive literature review of recent progress in the development of multirotor UAVs transporting cable-suspended loads. A secondary objective is to assist researchers and engineers in the design and development of flight control systems for UAV-slung-load applications. To this end, the survey begins by providing a historical overview of flight control hardware platforms and load swing measurement systems used in UAV-slung-load systems. Subsequently, representative modeling approaches for UAV-slung-load systems are introduced. The survey then reviews a range of existing flight control strategies, highlighting their key characteristics and advantages. Finally, general challenges and potential future research directions for UAV-slung-load systems are discussed.

This paper presents a comprehensive mathematical approach to address the multi-player and multi-objective pursuit-evasion games problem, incorporating coalition structure constraints from a cooperation-competition perspective. Social interaction networks are developed to approximate priority communication alliances based on individual preferences, establishing a multi-connected topology and decision space for the games. An N-player variable-sum differential game model, featuring autonomous obstacle avoidance, is formulated by integrating kinematic constraints and the social forces method. Rigorous proofs are provided for the uniqueness of payoff distribution, the stability of alliance structures, and the convergence of many-to-many differential games to Nash Equilibrium. Simulation and experimental results are presented to validate the effectiveness and performance of the proposed method.

Seeking the Pareto-Nash equilibrium in multi-agent, multi-objective general-sum games (MMGSG) poses a significant challenge, particularly in accurately capturing individual preferences and adhering to the fairness principle of the solution. To address this issue, this paper introduces, for the first time, a multi-level distributed fuzzy optimum policy iteration (MDFOPI) method for identifying the Pareto-Nash equilibrium point in MMGSG. This approach is grounded in fuzzy optimal membership degrees, and employs fuzzy measures and$\lambda$-mean classification to construct the coupled multi-objective optimum matrix, utilizing the strategy space as the foundation. The Pareto-Nash equilibrium point is sought through the MDFOPI method, with the multi-objective optimal membership degree matrix used to organize the sampled data and integrate the results of multi-objective evaluations. This work rigorously proves the existence of Nash equilibria in MMGSG and establishes the convergence of the MDFOPI method to a fixed point, specifically a Pareto-Nash equilibrium point. The accuracy and practical applicability of the research findings are verified through simulation experiments.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.

The integration of cooperation and competition strategies in game theory emphasizes the systematic nature of strategy spaces and group interactions, forming the basis for achieving win-win scenarios. This is particularly crucial under coalition constraints and incomplete information. Addressing these challenges, this article introduces a comprehensive mathematical method for policy formation using co-opetition topological networks. This method enables autonomous decision-making and game equilibrium in group decision scenarios, considering individual preferences amidst constraints like incomplete information and alliance limitations. Leveraging the complementary entropy theorem on superiority, inferiority, and fuzzy measures, we propose a cognitive model for information interaction and attribute fusion. Utilizing the ordered weighted averaging operator and average tree solutions aids in identifying optimal alliance structures. We subsequently discuss evaluating missing information to complete the topological network. Updating the cognitive model and value function, we develop a Gaussian oscillation heuristic algorithm to explore alliance and component strategy spaces. Simulation results are provided and analyzed to illustrate the performance and effectiveness of our approach.

This article proposes an event-based asymptotic tracking control method for nonlinear strict-feedback systems with multiple unknown control directions. The system is characterized by multiple unknown control directions, which pose challenges to its performance. In contrast to traditional Nussbaum-type methods, we propose a novel Nussbaum-type function to handle multiple Nussbaum-type gains, ensuring robust asymptotic tracking. Additionally, two event-triggered mechanisms are developed to alleviate the computational complexity of adaptive Nussbaum design. The static event-triggered mechanism significantly improves the system’s responsiveness to dynamic changes by employing dynamically decreasing thresholds. Building on this, a dynamic event-triggered mechanism is introduced, incorporating an internal variable that continuously adjusts the triggering conditions over time. Furthermore, the proposed design not only achieves asymptotic tracking control but also ensures that both event-triggered mechanisms avoid the Zeno phenomenon. To validate the proposed design schemes, a simulation example of a marine surface vehicle is presented.

Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded datasets without online environment interaction. It is unfortunate that extrapolation errors can cause over-optimistic Q-value estimates when learning with a fixed dataset, limiting the performance of the learned policy. To tackle this issue, this article proposes an offline actor-critic with behavior value regularization (OAC-BVR) method. In the policy evaluation stage, the difference between the Q-function and the value of the behavior policy is considered as the regularization term, driving the learned value function to approach the value of the behavior policy. The convergence of the proposed policy evaluation with behavior value regularization (PE-BVR) and the value function difference are analyzed, respectively. Compared with existing offline actor-critic methods, the proposed OAC-BVR method integrates the value of the behavior policy, thereby simultaneously alleviating over-optimistic Q-value estimates and reducing Q-function bias. Experimental results on the D4RL MuJoCo and Maze2d datasets demonstrate the validity of the proposed PE-BVR and the performance advantage of OAC-BVR over the state-of-the-art offline RL algorithms. The code of OAC-BVR is available at https://github.com/LongyangHuang/OAC-BVR.

Dynamic target tracking technology has a broad application prospect in marine transportation, intelligent marine monitoring, border and coastal defense, etc. However, most target tracking guidance systems designed for unmanned surface vehicles (USVs) lack automatic obstacle avoidance capabilities, which limits their tracking performance. To address this challenge, this paper investigates target tracking guidance for USVs in the presence of obstacles. In order to track the target, the sensors fixed on the bow of the USVs need to be oriented toward the target, especially when the USV is sufficiently close to the target. For this purpose, a bias proportional navigation guidance law with look angle constraints is presented for guiding the follower USVs to orient and approach the moving target. In order to navigate the USVs along a safe route to avoid obstacles, the obstacle profile angle constraint is formulated into the guidance law by solving the bias function with final angle boundary conditions. The field experimentation takes place in a 40-meter-wide and 80-meter-long section of the Huchuntang River. Here, a USV equipped with the proposed guidance law effectively tracks a moving target while navigating around obstacles. Results indicate that the proposed guidance law is capable of tracking the object, avoiding obstacles, and orienting the USV to the target at the final time. The experimental test video is presented in (https://youtu.be/l5SQf2ZgcxM).

This paper investigates the tracking control of a multi-quadrotor slung-load system (MQSLS), incorporating a dynamic model that simultaneously accounts for underactuation, nonlinearity, dynamic coupling, and unmodeled dynamics. We introduce a novel force distribution algorithm, which bifurcates the cooperative control to two distinct components: slung-load position control; and cable configuration control along with quadrotor attitude control. Employing this strategy results in a cooperative controller that: (i) relaxes the constraints on cable configuration; and (ii) requires only up to the third time derivative of the reference trajectory. The proposed control scheme ensures almost asymptotic stability of the overall closed-loop error system under unknown constant disturbances affecting both quadrotors and payload. A comprehensive set of simulations and experimental results validates the effectiveness of the proposed control strategy.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

This article proposes an event-triggered quantitative prescribed performance neural adaptive control method for autonomous underwater vehicles (AUVs). At kinematic level, to achieve a quantitative predetermined tracking performance without violating user-defined transient indices, a quantitative prescribed performance control (QPPC) scheme is devised, where the overshoot of the transient tracking response can be specified by a quantitative design relationship. To pursue a tradeoff between tracking accuracy and resource saving, a hybrid threshold-based event-triggered mechanism (HTETM) is designed and incorporated into the AUV controller design procedure. Additionally, a modified echo state neural network (MESNN) is employed for disturbance estimation, where intermittent system information produced by the HTETM is used for online learning, resulting in that both the communication data throughput between the controller and actuators and the online computational load can be diminished synchronously. Finally, a control law is devised at dynamic level to compensate for the triggered error induced by the aperiodic sampling of HTETM. Simulation results are provided and analyzed to validate the effectiveness of the proposed control strategy with application to an omni directional intelligent navigator.

In this article, the problem of finite-time$H_{\infty }$filtering design is studied for Markov jump systems (MJSs) with deception attacks and delays. Considering that data transmission networks between the system and filter will be subject to deception attacks and delays, a switching filter is designed by current states and modes as well as delayed states and modes. Since there are non-Markov jumps caused by the current mode and the delayed mode in the error dynamic systems, an extended state space method is employed to reconstruct it as switched error dynamic MJSs. By selecting multiple Lyapunov functionals, nonlinear sufficient conditions are given to ensure the finite-time boundedness and$H_{\infty }$performance of the switched error dynamic MJSs. In order to deal with the derived nonlinear conditions without introducing conservatism, a combination of genetic algorithms and linear matrix inequality tools is used to solve filter gains. Simultaneously, a multiobjective optimization between the finite-time boundary of system states and$H_{\infty }$performance index can be realized by the obtained filter gains. Simulation results are provided to illustrate the feasibility and effectiveness of the proposed approach.

In this paper, we study the quantitative design paradigm (QDP) of tracing control for a class of second-order system and further extend this to solve the trajectory tracking problem of autonomous underwater vehicles. The key merit of QDP is the capability of assigning some quantitative indices (e.g., overshoot and convergence time). To pursue performance enhancement with regard to tracking performance and bandwidth saving, a sectionalized event-triggered mechanism (SETM) incorporated with prespecified convergence time is proposed. To recognize the peculiarities of the lumped disturbances, an estimation-triggered neural network (ETNN) is designed via a property indicator, such that the disturbances that deteriorate the performance of the closed-loop system will be compensated and beneficial disturbances will be reserved otherwise, enabling less energy consumption without sacrificing the tracking performance. Theoretical analysis and simulation results are provided and analyzed, validating the performance and efficiency of the proposed solution.

In this work, we consider the problem of finite-region asynchronous dissipative control and pay more attention to the transient behavior of a class of two-dimensional fuzzy Markov jump systems (MJSs). First, the considered plant is modeled based on a well-known Fornasini–Marchesini equation. The asynchronization phenomenon between the system modes and controller modes is characterized by a hidden Markov model. Then, by a fuzzy-basis-dependent and mode-dependent Lyapunov function, sufficient conditions are established, which can make the overall closed-loop fuzzy dynamic MJSs be finite-region bounded with a strictly$(T, S, R)$-$\theta $-dissipative performance. Finally, a numerical example concerning the Darboux equation is employed to validate the effectiveness and performance of the presented control scheme.

In the presence of static and moving obstacles, this article investigates an output-feedback finite-time safety-critical coordinated control method of multiple under-actuated marine surface vehicles (MSVs) subject to velocity and input constraints. Specifically, based on robust exact differentiators, a finite-time state observer (FTSO) is first developed to recover the unavailable velocities while estimating the total disturbances containing model uncertainties and environmental disturbances. Next, with the aid of estimated velocities from FTSO, a nominal finite-time guidance law is designed for achieving the distributed formation of MSVs at the kinematic level. By the forward invariance principle, finite-time control barrier functions (FTCBFs) are used to construct the collision-free velocity sets for the multi-MSV system. To unify the control and safety objectives, quadratic optimization problems are formulated under collision-free velocity sets and velocity constraints. To facilitate real-time implementations, one-layer recurrent neural networks are employed to solve the quadratic optimization problem. Then, a nominal finite-time control law based on FTSO is presented at the kinetic level. The optimal control laws are solved within the input constraints. All error signals of the closed-loop system are proved to be uniformly ultimately bounded, and the distributed formation of multiple MSVs is ensured to be safe. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed FTCBF-based method.

This paper addresses the flexible formation problem for unmanned surface vehicles in the presence of obstacles. Building upon the leader-follower formation scheme, a hybrid line-of-sight based flexible platooning method is proposed for follower vehicle to keep tracking the leader ship. A fusion artificial potential field collision avoidance approach is tailored to generate optimal collision-free trajectories for the vehicle to track. To steer the vehicle towards and stay within the neighborhood of the generated collision-free trajectory, a nonlinear model predictive controller is designed. Experimental results are presented to validate the efficiency of proposed method, showing that the unmanned surface vehicle is able to track the leader ship without colliding with the surrounded static obstacles in the considered experiments.

In this article, we address the task of cooperative path following control of multiple autonomous quadcopters in the presence of unknown external disturbances. Under the assumption that the communications among the vehicles are bidirectional and continuous, a synchronized path following strategy is proposed that regulates the speed of each vehicle along its path to reach consensus in relative position. Moreover, collision-free transient paths from the vehicle initial positions to a group of suitable selected positions along the desired paths are designed. Building on the backstepping technique, the proposed path following controller for each individual vehicle drives the quadcopter toward, and to stay within an arbitrarily small neighborhood of its corresponding desired path, achieving global uniformly ultimately boundedness. In addition, the devised controller guarantees that the actuations are bounded with respect to the position error. The controller is also made robust to external constant or slowly time-varying disturbances by the introduction of dynamic estimators for the disturbances. A projection operator is used to ensure that the estimates remain within the prescribed bounds and are sufficiently smooth to be backstepped. In order to validate the effectiveness and performance of the proposed methodology, we present and analyze both simulation and experimental results.