This article introduces a multilayer navigation algorithm for a fleet of unmanned aerial vehicles (UAVs). The proposed architecture consists of a fusion of virtual point controllers and potential field techniques. On the one hand, a potential function is constructed for every agent such that its position smoothly and robustly converges to a virtual guidance point while avoiding collisions with other agents. The virtual points, on the other hand, are controlled to fulfill a swarm control goal such as target tracking, station keeping, or search and rescue missions. Therefore, the suggested system has two levels of hierarchy, but the algorithm can be generalized for multiple levels. The vehicle translational and rotational dynamics are controlled using an internal loop based on gradient tracking and sliding mode controllers. The architecture is validated in simulations and real-time experiments, showing good performance for the closed-loop system.

The cooperative output regulation problem of nonlinear multiagent systems has been intensively investigated in the control community under a standard assumption that the exact solution of regulator equations associated with each agent is available. However, this assumption is not realistic for many practical applications. To remove such restriction, this brief further studies a cooperative approximate output regulation problem of nonlinear multiagent systems. A novel distributed neural network (NN) control law is proposed, which integrates a distributed observer and NN controller. In contrast with existing results, the proposed algorithm leads to the solution of the cooperative output regulation problem for a general class of nonlinear multiagent systems, where the exact solution of regulator equations associated with each agent is unavailable. The proposed algorithm is applied to a benchmark problem of cooperative control of multiple inverted pendulum on a cart (IPC) systems and is verified by both simulation and experimental results.

This article investigates the modeling and control of a canting keel mechanism for roll reduction in marine vessels, examining two distinct configurations through simulation and experimental validation. A comprehensive nonlinear mathematical model of the system describing the roll motion of a vessel equipped with the canting keel, derived from first principles and accounting for the influence of unbalanced loading and waves, is provided. In the positively buoyant configuration of the keel, known as “airkeel (AK),” the system is shown to exhibit nonminimum phase (NMP) behavior arising from locally unstable zero dynamics, which, in turn, poses significant challenges for controller design. This issue is addressed by selecting proper coordinates that allow the supertwisting control (STC) algorithm to mitigate both matched and unmatched disturbances in conjunction with an extended state observer (ESO). The effectiveness of the proposed scheme is demonstrated in simulation through several case studies involving roll damping, unbalanced loading, and disturbances caused by waves generated by a stochastic model. The results are then objectively validated by experiments conducted on a small-scale model boat within an indoor test setting that emulates uneven loading conditions, as well as a field test examining the impact of sea waves on rolling behavior. The findings indicate that by appropriately selecting control parameters, the vessel’s roll response can be tailored to its operational mode, thereby optimizing system performance and enhancing disturbance rejection.

This brief presents a framework that delivers a certification-oriented dashboard of admissible nonlinear model predictive control (NMPC) implementation settings. This differs from the commonly adopted performance-centered tuning approaches by providing a dashboard of admissible setting options for which the optimal choice might be context-dependent. Some of the considered parameters are scarcely tuned in the literature on model predictive control (MPC)-parameter tuning such as the control updating period and the precision of the internal prediction. Moreover, a freely available <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Python</monospace>-based implementation is also proposed, and typical results on an illustrative example are discussed highlighting the relevance of the contribution.

This study presents a learning practical nonlinear model predictive controller (LPNMPC) designed to effectively control solar collector fields (SCFs), considering particular challenges due to nonlinear dynamics coupled with varying time delays, parameter uncertainties, and disturbances. The LPNMPC introduces an adaptive Oracle function computed via recursive least squares with exponential resetting (ER + RLSs) to estimate and correct model errors. Motivated by real-world plant settings, simulations using a validated SCF model from the CIESOL research center at the University of Almería, Spain, demonstrate the controller’s ability to overcome significant model parameters and delay uncertainties under challenging scenarios, with up to 27% fewer errors compared to the stat-of-the-art PNMPC methods. Experimental tests further validate the LPNMPC’s effectiveness, showing smooth control, accurate reference tracking, and reliable temperature regulation even under cloudy conditions, confirming its applicability in real-world SCF settings.

The development of the modular multilevel converter (MMC) enabled the efficient creation of high-power high-voltage direct current (HVdc) transmission systems. As a result, MMC-HVdc transmission systems became the main alternative to integrate remote renewable energy sources being deployed in accelerating rates to fight climate change. As the number of online classical synchronous generators (SGs) decreases while the one of converter-based power sources increases, power systems are suffering from lower inertia levels and from fewer providers of ancillary services. Therefore, new control strategies, such as the grid-forming (GFM) converter operation, were developed to address the ongoing power system transformation. The main contribution of this article is to propose a nonlinear (NL) control strategy compatible with GFM operation for an MMC-HVdc transmission system controlled as a virtual synchronous machine (VSM). The control strategy is developed using NL control tools, such as feedback linearization, dynamic feedback linearization, and backstepping. In addition, this article provides a rigorous mathematical stability analysis applying Lyapunov theory. The proposed control strategy is then validated by simulations using the MATLAB/Simscape Electrical package in three situations: active power tracking, converter energy tracking, and a frequency support scenario. Results show the good performance of the proposed NL controller for all situations considered, presenting a fast response and a faster disturbance rejection compared with the classical proportional integral (PI) controller.

Multirobots tasks, such as collaborative motion planning or flexible parts assembly, are complex for autonomous robot operations. The robot system for such tasks would need a combination of multigoal motion control and collision avoidance in the shared workspace. For reinforcement learning (RL)-based methods with enhanced flexibility and broader applicability, these methods struggle to effectively handle intricate tasks that require collision-free motion planning and control across long time horizons. Current RL systems cannot achieve efficiently motion planning due to high degrees-of-freedom (DoF), large-scale continuous action space, and tightly coupled workspaces of multirobot system. Additionally, the exploration efficiency for multigoal tasks is typically low, particularly in scenarios where rewards are scarce. To tackle these issues, we propose a new method to decompose the large-scale continuous action space into the combination of discrete action space that enhances exploration and small-scale continuous action space that enhances accuracy of structured hybrid action space (SHAS). To train the model with SHAS efficiently, we develop a task-conditioned hierarchical RL (TC-HRL) framework to train both high-level (HL) policy and low-level (LL) policy in parallel. Comparative experiments from different perspectives demonstrate that our model can learn multirobots manipulation tasks more efficiently and stably, and with considerably less time consumption. Finally, our work is verified by the pick-grasp tasks and real manipulators experiments.

Collaborative localization (CL) in multiagent systems has become an increasingly prominent research area, particularly under an uncertain and partially connected dynamic communication environment. This article presents a CL framework based on a resilient sequential fusion approach that guarantees consistency in matrix Lie groups. The proposed method is inherited from the pipeline of the distribution Kalman filter, which utilizes invariant error defined on a manifold to establish the global state propagation and update process for estimating the pose of all agents in the predefined reference frame. Furthermore, the communication update process is treated separately by generalizing the covariance intersection (CI) fusion into the designed geometric group structure, enabling flexible updates while maintaining the consistency of estimates and ensuring the independence of the filter update process. Specifically, to address the potential issues in agents’ communication, a weighted fusion criterion with an analytical form is proposed, allowing communication fusion to be performed on the manifold with arbitrary information fusion order and structure. Extensive validation through simulations and real-world experiments demonstrates that the proposed method is resilient to varying communication conditions and achieves superior performance compared with state-of-the-art methods.

The hardness of the coal seam significantly influences the relationship between feeding speed and resistance at the drill bit. Due to the compressive deformation of the drill string, maintaining a stable feeding speed during drilling remains challenging. In this article, we propose a robust H-infinity dynamic output feedback controller, formulated to explicitly address uncertainties in formation hardness, thereby ensuring stable feeding speed and improved dynamic performance of the feed system under diverse drilling conditions. First, we develop a bitrock interaction model that incorporates the uncertainties in formation hardness and the rock-breaking threshold, which are key factors affecting drilling performance. By integrating this model with a finite element representation of the drill string, the feeding system is recast as a norm-bounded uncertain system. On-site data is utilized to replicate drilling conditions and validate the accuracy of the model. Subsequently, considering the uncertain parameters and industrial performance requirements, a dynamic output feedback controller is designed using robust H-infinity optimization with tailored weighting functions. This controller maintains stable feeding speed across varying formation hardness, effectively suppressing the impact of hardness fluctuations by attenuating resonance peaks. Both simulation and field experiments confirm that the proposed controller substantially reduces feeding speed fluctuations and achieves the desired robustness and control performance.

In this work, we address the problem of precise motion planning and control of flexible-link robots for throwing small objects. Thanks to lightweight materials and elastic bodies, flexible robots can perform fast motions with few actuators. However, they need a planning and control strategy capable of exploiting the robot’s elasticity, negotiating with the system’s underactuation, and compensating for the model’s uncertainties. To solve this challenging task, we 1) compare multiple discrete models for continuum robots’ dynamics and, after selecting a lumped-parameter (LPs) model, experimentally identify its parameters; 2) plan the robot motion via a differential-dynamic-programming-based strategy tailored for flexible-link robots; and 3) employ an iterative learning control (ILC) approach to close the reality-gap. Combining these three steps allows us to execute precise throwing tasks with flexible-link robots. The strategy’s effectiveness has been validated via simulations and experiments with varying trajectories and payloads. We applied the aforementioned approach to realize a throwing motion with a fishing rod for environmental monitoring.