In this article, we investigate the distributed optimization problem of heterogeneous general linear multiagent systems by the adaptive dynamic programming (ADP) approach over directed communication networks. A distinctive feature of this work is the development of a data-driven approach that eliminates the need for prior knowledge of system dynamics for all agents. To address the challenges posed by unknown system dynamics, we utilize the ADP-based data-driven approach to develop the distributed optimization control law. First, the feedback gain of the control law is determined based on the state and input data of the controlled systems. Next, the system dynamics are reconstructed using the solved feedback gain and the running data of the controlled systems. Then, the remaining parameters in the control law are designed by solving a series of steady-state equations. Under standard assumptions and through the application of the certainty equivalence principle, we prove that the proposed approach solves the distributed optimization problem, ensuring output consensus of all agents at the optimal solution of the global cost function. Finally, the viability of our proposed approach is demonstrated through its application to optimal output power sharing control of hydraulic turbine systems and their large-scale form.

Static Var Generators (SVG) have been widely adopted in renewable power systems to improve voltage stability. The capacity and location of SVG installation in a renewable power system both impact the improvement effect. In this paper, we investigate the optimal SVG configuration that cost-effectively improves the voltage stability of power systems with high renewable energy penetration. First, recognizing the short-circuit ratio as a key and simple indicator of system voltage stability levels, a multi-objective optimization model is established, with objectives of improving the short-circuit ratio of renewable generators and minimizing the overall SVG installation cost. Then, we propose a modified NSGA-II algorithm to solve this model, thus obtaining the number and location for installing the SVGs in a power system. Experiments conducted on the SG118 system, which integrates renewable energy, validate the effectiveness of our proposed method. Our work presents power system operators with an effective measure to maintain the voltage stability of power systems with high penetrations of renewable energy.

In this paper, we address the cooperative robust parallel operation problem of an electric drive shaft system. In contrast to prior research, this work explicitly incorporates system uncertainties and external disturbances affecting both the shaft and the motors, enhancing the robustness and practicality of the proposed approach. To address this challenge, we first establish a dynamic output feedback controller utilizing the internal model principle. Then, we demonstrate that the cooperative robust parallel operation of the electric drive shaft system can be achieved under directed communication networks, effectively overcoming the adverse effects of system uncertainties and external disturbances. Finally, the efficacy of our proposed distributed controller is rigorously validated through its application to an electric drive shaft system equipped with five actuator motors.

3D human pose estimation (3DHPE) from a single monocular RGB image is fundamental in many image-related fields, such as virtual reality, motion analysis, and human-computer interaction. To improve estimation accuracy, existing works typically integrate complex networks or divide monocular 3DHPE into multiple stages. However, complicating the estimation process to improve the estimation accuracy sacrifices the estimation speed and limits its application. To alleviate this, we propose AITEPose, an end-to-end model, which achieves higher monocular 3DHPE accuracy with a simpler model structure. Specifically, inspired by online knowledge distillation, we design an Auxiliary-Information-Driven Training Enhancement (AITE) framework. In the AITE framework, during training, an adjustment network is introduced between the prediction network and the loss function to incorporate auxiliary information and enhance the training process. Notably, the adjustment network is constructed by developing a novel cascaded Disturbance-Correction Module (DCM). It adjusts the poses to get more accurate results based on ground-truth bone lengths. Both AITE and DCM are employed only during training, thereby improving training outcomes without complicating the inference process. The AITEPose model achieves state-of-the-art performance for single-frame monocular 3DHPE on the most comprehensive dataset Human3.6M. To further validate the effectiveness of AITE and DCM, we design a monocular 2DHPE model, AITEPose2D, and conduct extensive ablation experiments on the COCO2017 dataset, demonstrating the robustness and generalizability of our proposed AITEPose.

Most RGB-D-based SLAM methods assume texture-rich environments, making them susceptible to significant tracking errors or complete failures in the absence of texture features. Moreover, many existing methods encounter substantial rotation estimation errors, leading to long-term drift in tracking. This paper proposes a novel structured-constrained RGB-D SLAM method (STL-SLAM) for texture-limited environments. Compared to the existing methods, STL-SLAM can deal with environments without abundant texture information and significantly reduce long-term drift caused by rotation estimation errors. We assess the distribution complexity of pixels in an image by calculating the information entropy and pre-processing accordingly. We also present an efficient Manhattan Frames (MF) detection strategy based on orthogonal planes and lines. If MF is detected, we decouple rotation and translation, estimate drift-free rotation based on the Manhattan World (MW) coordinate system, and then estimate translation by minimizing the re-projection error of point, line, and plane features. In non-Manhattan Frames, the 6-DoF pose estimation is performed holistically, with the incorporation of structural constraints of parallel and perpendicular planes, as well as parallel and vertical lines, into the optimization process. Finally, we evaluate our method on public datasets and in real-world environments, which shows that our proposed method achieves superior performance compared to its counterparts.

Implanted devices are increasingly used in chronic disease monitoring, but face challenges in energy autonomy. This article presents a novel wireless power transfer (WPT) method for self-sustained medical implants using Halbach array-based magnetic plucking and piezoelectric transduction. The wearable-implantable coupled system consists of a piezoelectric receiver within the implant to receive power and a near-field magnetic power transmitter as a wearable device. To deliver power over greater distances through the human body, the transmitter features a rotating magnetic Halbach array powered by a miniature motor, or by human motion, to generate an alternating magnetic field. The use of low-frequency rotating magnetic fields periodically excites a cantilevered piezoelectric beam with a tip magnet to realize WPT. A theoretical model that includes magnetic coupling, piezoelectric transduction and receiver beam dynamics has been established to study the electro-magneto-mechanical dynamics of this WPT system. The effectiveness of the Halbach array for extended power transfer is examined through theoretical modeling and numerical simulation, showing a 37.2% enhancement of the magnetic forces. A prototype was also fabricated and tested to examine the WPT performance. The established wireless power link can provide sufficient power ($\sim 32~\mu $W) over a large transmission distance (22 mm), providing a potential battery-free solution for the self-sustained Internet of Implanted Things (IoIT) for personalized healthcare.

In this article, the robust output regulation problem of the linear uncertain system is investigated by the event-triggered control approach. Recently, the same problem is addressed by an event-triggered control law where the Zeno behavior may happen when time tends to infinity. In comparison, a class of event-triggered control laws is developed to achieve output regulation exactly, and meanwhile, explicitly exclude the Zeno behavior for all time. In particular, a dynamic triggering mechanism is first developed by introducing a dynamic changing variable with specific dynamics. Then, by the internal model principle, a class of dynamic output feedback control laws is designed. Later, a rigorous proof is provided to show that the tracking error of the system converges to zero asymptotically while prohibiting the Zeno behavior for all time. Finally, we give an example to illustrate our control approach.

Recently, the explosive development of unmanned aerial vehicles (UAVs) promotes its wide application in various services such as package delivery, traffic monitoring. However, due to the high-speed movement, current UAVs usually adopts the unsecure channel without the complicated authentication mechanism to ensure real-time communication. In this paper, we introduce a certificateless authentication scheme with fuzzy batch verification (CLFBV) to achieve once-for-all verification of parallel messages and ensure the real-time secure communication of UAVs. CLBFV defines the error tolerance property for authenticated communication, which allows a tolerance threshold for the messages that are unable to pass authentication. In addition, our proposed scheme is proved to be secure and existentially unforgeable under the chosen message attack and fuzzy identity attack in the random oracle model. The efficiency analysis shows that CLBFV is more efficient and feasible than other existing batch verification schemes.

In this article, we address the synchronization problem of networked uncertain Euler-Lagrange systems subject to disturbances, network delays, and uniformly connected switching networks. Compared with existing works, the current problem setting is more practical and technically more challenging. First, to tackle the disturbances under switching networks, we establish one lemma to show the convergence of a piecewise continuous function. Then, we establish the input-to-state stability (ISS) property of a class of perturbed time-delay systems to enable the distributed estimation of the system matrix and the output matrix of the leader system through delayed and switched network communication. Based on the certainty equivalence principle, we design an adaptive distributed control law. The synchronization control of four three-link cylindrical arms is used to demonstrate the effectiveness of the proposed approach.

In this study, we investigate the leader-following consensus problem of a class of heterogeneous secondorder nonlinear multi-agent systems subject to disturbances. In particular, the nonlinear systems contain uncertainties that can be linearly parameterized. We propose a class of novel distributed control laws, which depends on the relative state of the system and thus can be implemented even when no communication among agents exists. By Barbalat’s lemma, we demonstrate that consensus of the second-order nonlinear multi-agent system can be achieved by the proposed distributed control law. The effectiveness of the main result is verified by its application to consensus control of a group of Van der Pol oscillators.

In this paper, the leader-following consensus problem of multiple uncertain Euler-Lagrange systems is studied by developing a new adaptive distributed control law based on sensory feedback. In comparison with existing results, the developed distributed control law depends on the relative position of the Euler-Lagrange systems instead of the relative internal state of the controller. In the case that all systems are only equipped with sensors rather than communication devices, the developed distributed control law shows its distinct advantage. Moreover, the communication cost can be reduced by the new adaptive control law. The effectiveness of the main result is demonstrated by its application to cooperative control of multiple two-link robot arms.

In this paper, the consensus problem for linear multi-agent systems subject to non-uniform time-varying communication delays and jointly connected switching networks is investigated. Both distributed dynamic state feedback control law and distributed dynamic output feedback control law are proposed. By establishing some technical lemmas, it is shown that the proposed distributed control laws can solve the consensus problem.