This article introduces the planar vector field generator (PVFG) for a lower limb exoskeleton to achieve stable and adaptive periodic motion. Unlike traditional methods, which often rely on predefined gait trajectories, the PVFG-based control method leverages the natural dynamics of the system in its phase space, decouples the control from temporal constraints, and tracks the target periodic motion in the form of limit cycles. This article presents the theoretical framework of PVFG, and proposes the PVFG-based time-independent controller (PVFG-TIC) for the lower limb exoskeleton by integrating a master–slave phase coordination strategy. The stability of the proposed controller is proved through extended Lyapunov stability theory. The proposed control method improves upon existing limit cycle-based approaches by imposing fewer constraints on the target limit cycles. Experimental results on different types of exoskeletons validate the effectiveness of the proposed controller, which offers time independence, phase coordination, stability in wide range, and adaptability to switching target periodic motion, highlighting the potential of PVFG in the control of lower limb exoskeleton.

Nowadays, with the development of multi-sensor networks, the distributed cubature Kalman filter is one of the well-known existing schemes for state estimation, for which the influence of the non-Gaussian noise, abnormal data, and communication burden are urgent challenges. In this paper, a distributed cubature Kalman filter based on adaptive minimum error entropy with fiducial points (AMEEF) criterion (AMEEF-DCKF) is proposed to overcome the above limitations. Specifically, firstly, in order to solve the influence of various types of non-Gaussian noise and abnormal data, the AMEEF optimization criterion is designed, in which the kernels used are Cauchy kernels with adaptive bandwidth. At the same time, the designed optimization criterion has enhanced the numerical stability and optimized the kernel bandwidth value. Next, in order to address the communication burden problem in multi-sensor networks, where a leader and a follower are distinguished, a distributed algorithm is constructed to achieve an average consensus among these sensors, called leader-follower average consensus (LFAC). Additionally, the convergence proof of the average consensus algorithm and the computational complexity analysis of the AMEEF-DCKF algorithm are also presented. Finally, through a 10-node sensor network, the effectiveness of the proposed algorithm is demonstrated in estimating the state of the power system and navigating land vehicles in complex environments.

This article presents a distributed null-space-based behavioral (NSB) algorithm for the formation path-following problem of vehicles moving in three dimensions. The algorithm is applied to fleets of underactuated autonomous underwater vehicles (AUVs). The algorithm combines null-space-based control with consensus methods. First, we present a continuous-time version of the algorithm and prove its stability using Lyapunov analysis. Then, we present a discrete-time event-triggered version that, compared to similar formation path-following methods, can achieve the same steady state-error performance with fewer inter-vehicle transmissions. The effectiveness of both the continuous-time and the discrete-time algorithm is verified in numerical simulations. Furthermore, the discrete-time version is tested in experiments.

Hard-to-model, often nonlinear dynamics limit the tracking performance of physical-model-based feedforward control in medical interventional X-ray (IX) systems. In this article, these unknown dynamics are compensated using a physics-guided neural network (PGNN) feedforward controller. The PGNN consists of a parallel combination of a physical model describing the equations of motions of the IX and a feedforward neural network. To ensure that the neural network compensates only for the dynamics not included in the physical model, the neural network output in the subspace spanned by the physical model is penalized through regularization. As a result, the physical model serves as a baseline for performance, and the neural network contribution can be monitored or disabled. The PGNN feedforward controller is validated on an experimental IX setup, illustrating its superior performance over a physical-model-based feedforward controller.

Additive manufacturing (AM) processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the complex physics and various sources of uncertainty in the process. In this work, we investigate closed-loop control of the melt pool dimensions in a 2-D laser powder bed fusion (LPBF) process. We propose a trajectory optimization-based layer-to-layer (L2L) controller based on a linear parameter-varying (LPV) model that adjusts the laser power input to the next layer to track a desired melt pool depth and validate our controller by placing it in closed-loop high-fidelity multilayer smoothed particle hydrodynamics simulator of the 2-D LPBF process. Detailed numerical case studies demonstrate successful regulation of the melt pool depth on brick and overhang geometries and provide first of its kind results on the effectiveness of L2L input optimization for the LPBF process as well as detailed insight into the physics of the controlled process. Computational complexity and process performance results illustrate the method’s effectiveness and provide an outlook for its implementation onto real systems.

Accurate and physiologically interpretable postprandial glucose prediction is of importance in diabetes self-management. In this work, the problem of personalized glucose prediction is considered, and an interpretable postprandial glucose trajectory prediction framework is proposed based on kernel-based system identification under physiological constraints. Considering treatment requirements in inpatient scenarios, an online prediction update mechanism is developed to deal with intrasubject variability. Through incorporating physiological constraints abstracted from linearized compartmental models, a posterior performance assessment and adaptation mechanism is designed to guarantee the interpretability of the predicted glucose responses. The proposed method is evaluated through clinical data from type 1 diabetes mellitus (T1DM) subjects, and the results indicate that the proposed method can achieve physiologically interpretable postprandial glucose trajectory prediction with satisfactory performance.

This article presents a visual-inertial-based control strategy to address the task-space control problem for robot manipulators. To achieve this, a hybrid observer is introduced for visual-inertial odometry, enabling the control loop to be closed directly in Cartesian space by estimating the end-effector pose. The end-effector is equipped with an inertial measurement unit (IMU) and a stereo camera, which provide task-space feedback information to the proposed observer. A hybrid controller is then designed to utilize the estimated end-effector pose for accurate motion control. The Lyapunov stability theorem is employed to demonstrate that the resulting closed-loop system, using the proposed observer-based controller, is globally and asymptotically stable. Besides this notable merit (global asymptotic stability), the proposed method also reduces the impact of robot kinematics on the controller’s performance. Real-world experiments and computer simulations using a six-degrees-of-freedom (DoFs) long-reach hydraulic robot manipulator are conducted to evaluate the performance of the proposed observer-based control scheme.

Control allocation is essential for managing MIMO dynamic systems, such as autonomous vehicles and robotics, which operate across varying temporal scales, posing challenges in optimizing control actions. A common approach involves traditional solvers for overdetermined systems---most notably the singular value decomposition (SVD), the least squares QR algorithm (LSQR), and Arnoldi-type methods---, which, while effective for homogeneous temporal scales, struggle to handle mixed dynamics. Their inability to explicitly account for temporal variations and uncertainties often results in suboptimal performance and high computational costs. To address these challenges, this work introduces a novel combination of the Dynamic Confined Space of Velocities (DCSV) method with Krylov subspace exploration, significantly enhancing control allocation in mixed-temporal-scale systems. The DCSV method generates optimized initial estimates, which are iteratively refined using Krylov methods, achieving precise control with reduced computational costs. Comparative analysis against traditional solvers demonstrates that DCSV consistently outperforms these methods, achieving higher accuracy with fewer computational resources, particularly in systems with mixed dynamic scales. Since LSQR is closely related to quadratic programming (QP) and linear programming (LP) through its cost function, and SVD directly computes generalized inverses, their evaluation provides a basis for comparison with conventional control allocation strategies. The proposed method surpasses these approaches by achieving a better balance between computational efficiency and control precision. In addition, the uncertainty principle embedded in the DCSV framework establishes theoretical limits that ensure robustness in the allocation process, supported by a structured sequential exploration process formulated via pseudocode. By optimizing initial estimates through DCSV, fewer refinement iterations are needed, leading to faster and more accurate control allocation, as confirmed by formal analysis. This methodology offers a robust and efficient solution to the challenges posed by mixed temporal scales, with enhanced adaptability for real-world applications in dynamic and computationally constrained environments.

Many new methodologies for the control of large-scale multiagent systems are based on macroscopic representations of the system dynamics, in the form of continuum approximations of large ensembles. These techniques, developed in the limit case of an infinite number of agents, are usually validated only through numerical simulations. Here, we introduce a mixed reality setup for testing swarm robotics techniques, focusing on the macroscopic collective motion of robotic swarms. This hybrid apparatus combines real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We also extend continuification-based control methods for swarms to higher dimensions and experimentally assess their validity in the new platform. Our study demonstrates the effectiveness of the platform for conducting large-scale swarm robotics experiments, and it contributes new theoretical insights into control algorithms exploiting continuification approaches.

This manuscript presents a novel distributed greedy framework applicable to a network of thermostatically controlled loads (TCLs) to desynchronize the network's aggregated power consumption. Compared to the existing literature, our proposed framework offers two distinct novelties. First, our proposed algorithm relaxes the restrictive assumptions associated with the communication graph among TCLs. To elaborate, our algorithm only requires a connected graph to execute control, a condition less demanding than its counterpart algorithms that mandate a star architecture, K -regular graphs, or undirected connected graphs. Second, a significant novel feature is the relaxation of the obligation to share private information, such as each unit's local power consumption and appliance temperatures, either with a central coordinator or neighboring TCLs. The findings presented in this brief are validated through simulations conducted over a network comprising 1000 TCLs.