Predictor feedback control of discrete-time systems with multiple state delays and distinct input delays is investigated in this paper. At first, a prediction scheme for the considered system is developed step by step. Then, a predictor-based state feedback law is designed based on this prediction scheme. In addition, the property of the closed-loop system under the designed control law is analyzed. When the system state is unavailable for feedback, a full-order state observer is designed such that the current state can be estimated. With this estimated state, the stabilization for this class of time delay systems can also be achieved under the designed observer-based predictor feedback control law.

Robust flight in complex and windy environments is critical for both single and multiple quadrotors. Existing methods either learn disturbance model at high computational cost or use error-based adaptive control with a speed-stability trade-off that makes tuning difficult. To address these issues, this paper proposes a future-trend-aware filter-based PD-MRAC (Proportional-Derivative Model Reference Adaptive Control) for single quadrotor and a distributed PD-MRAC for multiple quadrotor formation. By embedding a trend-aware derivative term in the adaptive update laws, the controller obtains anticipatory information about the error evolution, enabling rapid adaptation while mitigating oscillations. For more disturbance-sensitive multi-quadrotors, we design a robust distributed protocol under a directed graph, improving resilience to disturbances. The approach maintains low computational cost and supports fast adaptive updates. Extensive simulations and real-world experiments validate improvement. For single quadrotor, RMSE reduced by around 57% versus the baselines and by around 12% versus the DJI Mavic 2. For multi-quadrotors, formation results show enhanced robustness in simulation and effective real-world indoor/outdoor experiments under strong winds. Our project page is athttps://xiongtao-shi.github.io/PD-MRAC/.

Various studies on perception-aware planning have been proposed to enhance the state estimation accuracy of quadrotors in visually degraded environments. However, many existing methods heavily rely on prior environmental knowledge and face significant limitations in previously unknown environments with sparse localization features, which greatly limits their practical application. In this paper, we present a perception-aware planning method for quadrotor flight in unknown and feature-limited environments that properly allocates perception resources among environmental information during navigation. We introduce a viewpoint transition graph that allows for the adaptive selection of local target viewpoints, which guide the quadrotor to efficiently navigate to the goal while maintaining sufficient localizability and without being trapped in feature-limited regions. During the local planning, a novel yaw trajectory generation method that simultaneously considers exploration capability and localizability is presented. It constructs a localizable corridor via feature co-visibility evaluation to ensure localization robustness in a computationally efficient way. Through validations conducted in both simulation and real-world experiments, we demonstrate the feasibility and real-time performance of the proposed method. The source code is released for the reference of the community1

Efficient and safe trajectory planning plays a critical role in the application of quadrotor unmanned aerial vehicles. Currently, the inherent trade-off between constraint compliance and computational efficiency enhancement in UAV trajectory optimization problems has not been sufficiently addressed. To enhance the performance of UAV trajectory optimization, we propose a spatial-temporal iterative optimization framework. Firstly, B-splines are utilized to represent UAV trajectories, with rigorous safety assurance achieved through strict enforcement of constraints on control points. Subsequently, a set of QP-LP subproblems via spatial-temporal decoupling and constraint linearization is derived. Finally, an iterative optimization strategy incorporating guidance gradients is employed to obtain high-performance UAV trajectories in different scenarios. Both simulation and real-world experimental results validate the efficiency and high performance of the proposed optimization framework in generating safe and fast trajectories. Our source code will be released for community reference.1

Autonomous aerial vehicles have emerged as critical platforms for 3D environmental mapping, yet existing LiDAR-based systems struggle to balance efficiency and compactness in large-scale scenarios. Conventional designs rigidly mount LiDAR with a narrow vertical field of view, necessitating inefficient vertical maneuvers for coverage. While rotating LiDARs can mitigate this limitation, they are often burdensome for lightweight aerial platforms and require processing more expansive 3D data streams. To address these challenges, we present FLARE, a co-designed aerial exploration system integrating a lightweight actively rotated LiDAR with a hierarchical planning framework. The micro-servo-actuated LiDAR dynamically adjusts its orientation via online planning, effectively expanding its sensing field without incurring substantial system complexity. Moreover, FLARE employs a hierarchical frontier clustering method that supports multilayer coarse-to-fine planning, balancing computational load and exploration performance to ensure efficient operation even in large-scale scenarios. Both simulation and fully onboard real-world experiments validate the system’s effectiveness, demonstrating complete coverage with shorter trajectories and reduced flight time compared to existing methods.

In this article, the data-driven optimal control problem is addressed for discrete-time linear periodic systems with unknown system dynamics. To reduce the number of iterations required by existing data-driven control algorithms, two novel value iteration (VI)-based adaptive dynamic programming (ADP) algorithms are presented. In these two VI algorithms, the latest updated estimates are utilized to approximate the unique positive definite solution of the algebraic Riccati matrix equation (ARE), and the suboptimal controller is obtained. Since the latest estimation is generally closer to the optimal value than that of the last iteration step, the number of iterations is significantly reduced in the two proposed algorithms. Moreover, the backward VI algorithm requires fewer iteration steps compared to the forward VI algorithm. In addition, the proposed methods do not require an initial stabilizing controller. Finally, two examples are provided to demonstrate the effectiveness of the two proposed iterative algorithms.

In this paper, the stabilization problem of discrete-time systems with both input delays and multiple state delays is concerned. For this end, two interesting properties are derived for the bivariant fundamental matrix related to a discrete-time linear system with multiple state delays. A prediction scheme of the considered system is presented via the corresponding bivariant fundamental matrix, and then a predictor-based feedback control law is proposed for the considered systems with both input delays and multiple state delays. Furthermore, the characteristic equation of the closed-loop system of the considered system under the designed predictor feedback law is analyzed. Finally, the effectiveness of the proposed method is illustrated by numerical examples.

Mobile manipulators have recently gained significant attention in the robotics community due to their superior potential in industrial and service applications. However, the high degree of freedom associated with mobile manipulators poses challenges in achieving real-time whole-body motion planning. To bridge the gap, this paper presents a motion planning method capable of generating high-quality, safe, agile and feasible trajectories for mobile manipulators in real time. First, we present a novel environment-adaptive path searching method, which can generate paths in real-time in various environments by adaptively adjusting searching dimension based on environment complexity. Additionally, we propose a real-time spatial-temporal trajectory optimization method that takes into account the whole-body safety, agility and dynamic feasibility of mobile manipulators. Moreover, task constraints are applied to ensure that the trajectory can fulfill specific task requirements. Simulation and real-world experiments demonstrate that our method is capable of generating whole-body trajectories in real-time in challenging environments. We will release our code to benefit the community.

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online⋆.

We investigate the scaled position consensus of high-order multiagent systems with parametric uncertainties over switching directed graphs, where the agents' position states reach a consensus value with different scales. The intricacy arises from the asymmetry inherent in information interaction. Achieving scaled position consensus in high-order multiagent systems over directed graphs remains a significant challenge, particularly when confronted with the following complex features: 1) uniformly jointly connected switching directed graphs; 2) complex agent dynamics with unknown inertias, unknown control directions, parametric uncertainties, and external disturbances; 3) interacting with each other via only relative scaled position information (without high-order derivatives of relative position); and 4) fully distributed in terms of no shared gains and no global gain dependency. To address these challenges, we propose a distributed adaptive algorithm based on a acrlong MRACon scheme, where a linear high-order reference model is designed for every individual agent employing relative scaled position information as input. A new transformation is proposed which converts the scaled position consensus of high-order linear reference models to that of first-order ones. Theoretical analysis is presented where agents' positions achieve the scaled consensus over switching directed graphs. Numerical simulations are performed to validate the efficacy of our algorithm and some collective behaviors on traditional consensus, bipartite consensus, and cluster consensus are shown by precisely choosing the scales of the agents.

State responses for several classes of linear systems are investigated in this article. The involved systems include state-delayed linear systems, and high-order linear systems. At first, the single-fundamental-matrix-based approach is extended to these systems, and their state responses are expressed by their fundamental matrices (FMs). In addition, the multiple-FMs-based approach is presented for these systems. Based on a group of FMs, the state responses for the considered time-invariant systems are derived. For the considered time-variant systems, their state responses are explicitly expressed by their transition matrices. As an application of the fundamental-matrix-based approach, a stabilizing control law is designed for a class of high-order fully actuated continuous-time linear systems with a single input-delay.

This article studies the consensus problem of a class of multi-input–multi-output (MIMO) multiagent systems (MASs) subject to parametric uncertainties and external disturbances via a fully distributed model reference adaptive event-triggered control (MRA-ETC) protocol. Incorporate both the MRA and ETC, a reference model using the predicted relative state information initialized by intermittently collected state information of neighbors as input and a self-contained adaptive estimator of uncertainties are constructed, where the transmitted information is asynchronous and intermittent. We consider both the cases of matched and unmatched external disturbances, where each agent is assigned a reference model to track. Asymptotic consensus is achieved for the case of matched disturbances by using the sliding-mode control, while for the case of unmatched disturbances, the uniformly ultimately bound consensus is obtained via the adaptive$\sigma$-modification technique. The communication resources have been significantly saved via the proposed event-triggered mechanism (ETM) with strictly excluding the Zeno behavior. Moreover, with the help of the designed adaptive control gains for the reference models, the consensus algorithm can be implemented in a fully distributed fashion without employing any global information of the MASs. Finally, the simulation examples are illustrated to show the correctness of the proposed control schemes.

In this paper, we study the leaderless consensus problem for second-order uncertain multi-agent systems with absolute velocity damping under a directed graph. A fully distributed algorithm is proposed, in which each agent uses only the relative position measurements with respect to its neighbors and its own absolute velocity damping. It turns out that all agents achieve consensus asymptotically with zero final velocities. The proposed algorithm is fully distributed in the sense that different agents use their own control gains. Based on a system transformation method and an auxiliary variable, the leaderless consensus problem for second-order uncertain multi-agent systems is converted into that for a first-order linear multi-agent system with a vanishing term. The consensus convergence is then analyzed via the Lyapunov stability theory and input-to-state stability. Numerical simulations are provided to verify the effectiveness of the proposed algorithm.

In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.