Hasil untuk "Control engineering systems. Automatic machinery (General)"

Rodion V. Zakshevskii

Krishnamoorthy S., B. Jaganathan

Clustering methods are essential in medical and data-centric research, helping to reveal underlying patterns without the need for labelled data. This study introduces a gradient-based K-means framework that jointly refines centroids, sample weights, and covariance matrices. In contrast to traditional weighted K-means, which treats these components separately, the proposed method enables a more cohesive and adaptive optimization strategy. By incorporating Mahalanobis distance to account for feature correlations and applying dynamic weighting, the approach is well-suited for complex clinical datasets. Tests on real-world medical data show that this method outperforms standard clustering algorithms, offering improved accuracy and more clearly defined cluster structures.

Yuhuan Yue

This paper proposes a deep learning algorithm for solving the infinite-horizon optimal feedback control problem of a quadrotor unmanned aerial vehicle (UAV). The optimal control is represented by the stable manifold of the Hamilton–Jacobi–Bellman (HJB) equation in a 12-dimensional state space. Moreover, a deep learning algorithm is proposed to compute approximations of semiglobal stable manifold. The method is built on the geometric feature of the problem. The algorithm generates random data by solving the two-point boundary value problem of the characteristic Hamiltonian system of the HJB equation without discretizing the state space. The resulting data set lies on the stable manifold, and a deep neural network (NN) is trained to fit the data. The training process is conducted offline on a standard laptop without the use of a GPU. Generating feedback control for the quadrotor from the trained NN takes less than one millisecond, compared to several milliseconds required by existing methods for the same operation. The effectiveness of this approach is demonstrated by Monte Carlo tests and simulations in various scenarios.

Robert H. Moldenhauer, Dragan Nešić, Mathieu Granzotto et al.

We analyze the stability of general nonlinear discrete-time stochastic systems controlled by optimal inputs that minimize an infinite-horizon discounted cost. Under a novel stochastic formulation of cost-controllability and detectability assumptions inspired by the related literature on deterministic systems, we prove that uniform semi-global practical recurrence holds for the closed-loop system, where the adjustable parameter is the discount factor. Under additional continuity assumptions, we further prove that this property is robust.

Junjie Zhang, Fangfang Zhang, Jie Li et al.

Measuring element is always accompanied by uncertainty disturbances and multiple faults during the long time operation of industrial system in complex environment such as blade and pitch system of floating wind turbines. Timely detection of the fault and fault‐tolerant control (FTC) perform a significant part in ensuring the stable operation of the system and saving maintenance fees. Sliding mode control is extensively applied to FTC because of its good robustness. Therefore, a sliding mode controller is constructed to guarantee the stability of the industrial plant which suffers multiple faults and uncertain disturbances. At the same time, most existing literature does not take into account several faults and uncertain disturbances. Firstly, employing generalized sliding mode method, we devise a sliding mode observer for evaluating state vector, actuator fault and sensor fault of the system. Secondly, according to the state estimation, we construct a sliding mode controller and prove its validity by Lyapunov's theorem. Our controller achieves satisfactory performance, and it is easier to be implemented in practical engineering than other controllers. Finally, we establish a SIMULINK model of blade and pitch system and make simulation experiments. Simulation outcomes validate the availability and practicability of our controller, which also provides a general scheme for fault estimation and FTC of other industrial plants.

Degang Yang, Tao Liang, Wanli Zhang

Abstract This article discusses finite‐time lag bipartite (FETLB) synchronization of double‐layer networks with non‐linear coupling strength and multiple time delays. The cooperative and competitive interactions between nodes are considered based on signed graphs. To address the non‐linear couplings strength, the T‐S fuzzy logic theory is used. An intermittent control approach is introduced to achieve FETLB synchronization, effectively minimizing control costs. Moreover, by the Lyapunov functional method, we derive criteria for achieving FETLB synchronization and provide estimations for the synchronization settling time. In addition, numerical simulation affirms the validity of the theoretical findings, showcasing the practical application of the synchronization results in secure communication.

Muhammad Shafiq, Israr Ahmad

ABSTRACTThe inherent randomness in economic factors causes complex and irregular behaviour that affects financial system stability and economic growth. Such chaotic behaviour can make it difficult to synchronize financial systems. The chaotic finance system synchronization precision maintains financial stability and economic growth. In this paper, the controller design procedure assumes that the financial system is exposed to unknown bounded exogenous disturbances and model uncertainties. This research proposes a novel direct adaptive control strategy that achieves robust synchronization of two identical four-dimensional finance chaotic (FDFC) systems. The proposed controller establishes a faster, smoother synchronization error vector convergence to zero. The controller design procedure does not eliminate the closed-loop's nonlinear terms and is independent of the financial system parameters. These controller's attributes accomplish the closed-loop robust performance. Further, this controller uses real-time estimates of unknown model uncertainties and bounds to compensate for unknown exogenous disturbances. Computer simulation results and proofs of theoretical analysis based on the Lyapunov stability theory confirm that the proposed control technique compels the error vector trajectories to the origin in a short transient time with less active oscillations for all signals. The paper includes comparative computer simulations for verifying the theoretical findings.

Huda Talib Najm, Nur Syazreen Ahmad, Ahmed Sabah Al-Araji

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

Hocine Benslimane

T. M. J. T. Baltussen, A. Katriniok, E. Lefeber et al.

The control of a single agent in complex and uncertain multi-agent environments requires careful consideration of the interactions between the agents. In this context, this paper proposes a dual model predictive control (MPC) method using Gaussian process (GP) models for multi-agent systems. While Gaussian process MPC (GP-MPC) has been shown to be effective in predicting the dynamics of other agents, current methods do not consider the influence of the control input on the covariance of the predictions, and hence lack the dual control effect. Therefore, we propose a dual MPC that directly optimizes the actions of the ego agent, and the belief of the other agents by jointly optimizing their state trajectories as well as the associated covariance while considering their interactions through a GP. We demonstrate our GP-MPC method in a simulation study on autonomous driving, showing improved prediction quality compared to a baseline stochastic MPC. The results show that GP-MPC can learn the interactions between the agents online, demonstrating the potential of GPs for dual MPC in uncertain and unseen scenarios.

Tianyu Zhou, Qi Zhu, Jing Du

Abstract Robotic teleoperation, i.e., manipulating remote robotic systems at a distance, has gained its popularity in various industrial applications, including construction operations. The key to a successful teleoperation robot system is the delicate design of the human-robot interface that helps strengthen the human operator’s situational awareness. Traditional human-robot interface for robotic teleoperation is usually based on imagery data (e.g., video streaming), causing the limited field of view (FOV) and increased cognitive burden for processing additional spatial information. As a result, 3D scene reconstruction methods based on point cloud models captured by scanning technologies (e.g., depth camera and LiDAR) have been explored to provide immersive and intuitive feedback to the human operator. Despite the added benefits of applying reconstructed 3D scenes in telerobotic systems, challenges still present. Most 3D reconstruction methods utilize raw point cloud data due to the difficulty of real-time model rendering. The significant size of point cloud data makes the processing and transfer between robots and human operators difficult and slow. In addition, most reconstructed point cloud models do not contain physical properties such as weight and colliders. A more enriched control mechanism based on physics engine simulations is impossible. This paper presents an intelligent robot teleoperation interface that collects, processes, transfers, and reconstructs the immersive scene model of the workspace in Virtual Reality (VR) and enables intuitive robot controls accordingly. The proposed system, Telerobotic Operation based on Auto-reconstructed Remote Scene (TOARS), utilizes a deep learning algorithm to automatically detect objects in the captured scene, along with their physical properties, based on the point cloud data. The processed information is then transferred to the game engine where rendered virtual objects replace the original point cloud models in the VR environment. TOARS is expected to significantly improve the efficiency of 3D scene reconstruction and situational awareness of human operators in robotic teleoperation.

Shuqi Wang, M. Leung, S. Leung et al.

Hypertension (HT) continues to be a leading cause of cardiovascular death and an enormous burden on the healthcare system. Although telemedicine may provide improved blood pressure (BP) monitoring and control, it remains unclear whether it could replace face-to-face consultations in patients with optimal BP control. We hypothesized that an automatic drug refill coupled with a telemedicine system tailored to patients with optimal BP would lead to non-inferior BP control. In this pilot, multicenter, randomized control trial (RCT), participants receiving anti-HT medications were randomly assigned (1:1) to either the telemedicine or usual care group. Patients in the telemedicine group measured and transmitted their home BP readings to the clinic. The medications were refilled without consultation when optimal control (BP < 135/85 mmHg) was confirmed. The primary outcome of this trial was the feasibility of using the telemedicine app. Office and ambulatory BP readings were compared between the two groups at the study endpoint. Acceptability was assessed through interviews with the telemedicine study participants. Overall, 49 participants were recruited in 6 months and retention rate was 98%. Participants from both groups had similar BP control (daytime systolic BP: 128.2 versus 126.9 mmHg [telemedicine vs. usual care], p = 0.41) and no adverse events. Participants in the telemedicine group had fewer general outpatient clinic attendances (0.8 vs. 2, p < 0.001). Interviewees reported that the system was convenient, timesaving, cost saving, and educational. The system could be safely used. However, the results must be verified in an adequately powered RCT. Trial registration : NCT04542564.

Mustafa Atakan Afşar, Hilal Arslan

PID controllers are important control methods that are widely used in industrial processes. Proper tuning of PID gains is critical for achieving the state-of-the-art system performance. Therefore, optimizing PID gains is an important research topic in the field of control engineering. In this study, PID controller gains are automatically tuned using metaheuristic optimization methods. These methods use an iterative approach to calculate optimal values of PID controller gains based on different optimization techniques. The interaction between artificial intelligence and control systems requires a multidimensional approach across different disciplines. In the study, we perform Particle Swarm Optimization, Gray Wolf Optimization, Whale Optimization Algorithm, Firefly Algorithm, Harris Hawks Optimization, Artificial Hummingbird Algorithm and African Vulture Optimization Algorithm to determine PID gains. In the simulation, step input is applied to the dynamic equation of the unmanned free-swimming submersible vehicle. The fitness function is determined with respect to controller integral square error, settling time value, and maximum percent overshoot value. We also evaluate the optimization time of the selected algorithms based on the fitness function. Experimental results present that Artificial Hummingbird Algorithm, Gray Wolf Optimization and Particle Swarm Optimization achieve significant performance. This underlines that using metaheuristic methods in PID gain optimization increase overall system performance.

Xunzhao Yin, Franz Müller, Qingrong Huang et al.

Content addressable memory (CAM) is widely used in associative search tasks due to its parallel pattern matching capability. As more complex and data‐intensive tasks emerge, it is becoming increasingly important to enhance CAM density for improved performance and better area efficiency. To reduce the area overheads, various nonvolatile memory (NVM) devices, such as ferroelectric field‐effect transistors (FeFETs), are used in CAM design. Herein, a novel ultracompact 1FeFET CAM design that enables parallel associative search and in‐memory hamming distance calculation is used, as well as a multibit CAM for exact search using the same CAM cell. The proposed CAM design leverages the 1FeFET1R structure, and compact device designs that integrate the series resistor current limiter into the intrinsic FeFET structure are demonstrated to turn the 1FeFET1R structure into an effective 1FeFET cell. A two‐step search operation of the proposed binary and multibit 1FeFET CAM array through both experiments and simulations is proposed, showing a sufficient sensing margin despite unoptimized FeFET device variation. In genome pattern matching applications, using the hyperdimensional computing paradigm, the design results in a 89.9× speedup and 66.5× improvement in energy efficiency over the state‐of‐the‐art alignment tools on GPU.

Simeon C. Calvert, Stig Johnsen, Ashwin George

Ensuring operational control over automated vehicles is not trivial and failing to do so severely endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part including drivers, occupants, vehicle designers and governments. While progress is being made, a comprehensive approach to the problem is being ignored, which can be solved in the main through considering Meaningful Human Control (MHC). In this research, an Integrated System Proximity framework and Operational Process Design approach to assist the development of Connected Automated Vehicles (CAV) under the consideration of MHC are introduced. These offer a greater understanding and basis for vehicle and traffic system design by vehicle designers and governments as two important influencing stakeholders. The framework includes an extension to a system approach, which also considers ways that MHC can be improved through updating: either implicit proximal updating or explicit distal updating. The process and importance are demonstrated in three recent cases from practice. Finally, a call for action is made to government and regulatory authorities, as well as the automotive industry, to ensure that MHC processes are explicitly included in policy, regulations, and design processes to ensure future ad-vancement of CAVs in a responsible, safe and humanly agreeable fashion.

Xiaoyan Dai, Claudio De Persis, Nima Monshizadeh et al.

The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers from data via approximate nonlinearity cancellation," IEEE Transactions on Automatic Control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.

Steven X. Ding, Linlin Li

Tzu-Hsuan Hsia, Shogo Okamoto, Yasuhiro Akiyama et al.

Abstract Hum-noise-based touch sensors (HumTouch) are capable of recognizing human touch on semiconductive materials using the current leaking from the finger to the surface. Thus far, calibration for these hum-noise-based touch sensors has been performed for individual users because of the individual differences in hum-driven electric currents in human bodies. However, for applications designed for unknown users, time-consuming calibration for individual users is not preferred, and a new user should be able to use the sensor immediately. For this purpose, we propose a new calibration method for HumTouch. In this method, learning datasets collected from multiple people and a few extra samples from a new user are collectively used to establish a touch localization estimator. The estimator is computed using the kernel regression method with weighted samples from the new user. For a 20 $$\times $$ × 18 cm $$^2$$ 2 paper, the mean localization error is reduced from 1.24 cm to 0.90 cm with only one sample from the new user. Hence, a new user can establish a semipersonalized localization estimator by touching only one point on the surface. This method improves the localization performance of HumTouch sensors in an easy-to-access manner.

Taisuke Kobayashi, Emmanuel Dean-Leon, Julio Rogelio Guadarrama-Olvera et al.

For physical human–robot interaction (pHRI) where multi‐contacts play a key role, both robustness to achieve robot‐intended motion and adaptability to follow human‐intended motion are fundamental. However, there are tradeoffs during pHRI when their intentions do not match. This paper focuses on bipedal walking control during pHRI, which handles such tradeoff when a human and a humanoid robot having different footsteps locations and durations. To resolve this, a force‐reactive walking controller is proposed by adequately combining ankle and stepping strategies. The ankle strategy maintains the robot's intention based on an analytically‐optimal center of pressure, leading the robot to oppose resistance to multiple contacts from the human. Based on the robot's kinodynamic constraints and/or the confidence of the robot's intention, the stepping strategy updates the robot's footsteps based on the human's intention implied by the multiple contact forces. Consequently, the proposed walking control on pHRI mutually exchanges human–robot intentions in real‐time, thereby achieving coordinated steps. With a full‐sized humanoid robot that is able to detect multi‐contacts in real‐time, we succeeded in performing a long‐term “box‐step” with multi‐contacts pHRI, demonstrating the robustness of our approach.

Marija Ilic, Rupamathi Jaddivada

This paper points out some key drawbacks of today's modeling and control underlying hierarchical electric power system operations and planning as the hidden roadblocks on the way to decarbonization. We suggest that these can be overcome by enhancing today's information exchange and control. This can be done by revealing and utilising inherent structure-preserving features of complex physical systems, and, based on this, by establishing multi-layered energy modeling. Each module (component, control area, non-utility-owned entities) can be characterized in terms of its interaction variable, and higher level models can be used to understand the interaction dynamics between different modules. Once the structure is understood, we propose nonlinear energy control for these modules which supports feed-forward self-adaptation to ensure feasible interconnected system. Based on these technology agnostic structures it becomes possible to expand today's Balancing Authorities (BA) to multi-layered interactive intelligent Balancing Authorities (iBAs) and to introduce protocols for flexible utilization of diverse technologies over broad ranges of temporal and spatial conditions.