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

Sajid Habib Gill, Javed Ahmed Mahar, Shahid Ali Mahar et al.

Punjabi is an old Indo-Aryan language spoken across the world, particularly in Pakistan and India. Punjabi is a tonal and low-resourced language therefore; significant research work has not been done so far, especially in the South Punjab belt. This language is divided into different dialects and finding the diversity of tonal qualities in the Majhi Punjabi dialect is the core objective of this research. Speech-processing applications are usually influenced by prosodic properties such as pitch, amplitude, and duration. A speech corpus was collected from 241 native speakers, encompassing spoken words totaling 7712, and representing various age groups and genders. The proposed prosodic model using the Mel Frequency Cepstral Coefficients (MFCC) system is used to extract the prosodic features from collected speech utterances of the Majhi Punjabi dialect. The examination of the results suggests that tonal and dialectal word information demonstrates a considerable impact on the information delivered by the speaker. Gender-specific variations in tonal word amplitudes are shown by the model. The extracted prosodic information is classified with support vector machine, logistic regression, random forest, K nearest neighbor, gradient boost (GB), and extra tree classifier (ETC). The ETC and GB models performed well with the highest accuracy of 97%. The four deep learning models are also implemented for performance comparison with machine learning, however, deep learning models do not perform well on this dataset. The highest accuracy is gained by CNN which is 86%. This research endeavor will be beneficial for Punjabi speech-processing applications. Additionally, the impact of dialectal variations elucidates the rich diversity present in spoken language, hinting at the importance of considering regional nuances in future investigations.

Takahiro Hattori, Kento Kawaharazuka, Temma Suzuki et al.

Electronic devices are essential for robots but limit their usable environments. To overcome this, methods excluding electronics from the operating environment while retaining advanced electronic control and actuation have been explored. These include the remote hydraulic drive of electronics‐free mobile robots, which offer high reachability and long wire‐driven robot arms with motors consolidated at the base, which offer high environmental resistance. To combine the advantages of both, this study proposes a new system, “Remote Wire Drive.” As a proof of concept, the Remote Wire‐Driven robot “REWW‐ARM” is designed and developed, which consists of the following components: 1) a novel power transmission mechanism, the “Remote Wire Transmission Mechanism” (RWTM), the key technology of the Remote Wire Drive; 2) an electronics‐free distal mobile robot driven by it; and 3) a motor unit that generates power and provides electronic closed‐loop control based on state estimation via the RWTM. In this study, the mechanical and control performance of REWW‐ARM is evaluated through several experiments, demonstrating its capability for locomotion, posture control, and object manipulation both on land and underwater. This suggests the potential for applying the Remote Wire‐Driven system to various types of robots, thereby expanding their operational range.

Myungjin Jung, Sejeong Kim, Min‐Cheol Kim et al.

This study presents a novel cable‐driven parallel robot (CDPR) assisted needle insertion method for X‐ray guided remote interventional pain procedures. The CDPR employs flexible cables to actuate a robotic end‐effector, and the proposed system ensures compatibility with X‐ray imaging while facilitating precise remote needle insertion by achieving a virtual remote center of motion. The proposed system addresses challenges associated with conventional rigid‐link type needle insertion robots in terms of a limited workspace and X‐ray interference. Design, workspace analysis, prototyping, control, and experimental results for feasibility validation are conducted to demonstrate the effectiveness in achieving of accurate needle guidance under C‐arm imaging. The gelatin phantom experiments confirmed the motion accuracy and the cadaver experiment underscored the system's feasibility for clinical applications. The proposed approach to robotic assistance in interventional pain procedures may enhance precision and reduce radiation exposure for both patients and clinicians.

Hiromu Mori, Takayuki Tanaka, Akihiko Murai et al.

Abstract This paper presents a simple mechanical model capable of evaluating a running effectiveness index $$\epsilon _{EI}$$ ϵ EI based on mechanical energy. We extended a spring-loaded inverted pendulum model, considering the biomechanical determinants of running economy (RE), to develop a simplified mechanical model that can accurately represent the $$\epsilon _{EI}$$ ϵ EI calculated by a detailed running model. To assess the accuracy of the proposed model in estimating RE, we computed the $$\epsilon _{EI}$$ ϵ EI using both the proposed and detailed models, based on data obtained from running experiments. A linear regression analysis using the least squares method was performed to analyze the relationship between the $$\epsilon _{EI}$$ ϵ EI values calculated by the two models. The regression analysis results of the $$\epsilon _{EI}$$ ϵ EI values obtained from the two models yielded significant F-statistics ( $$p < 0.01$$ p < 0.01 ) for all four participants, demonstrating that the proposed model can sufficiently represent the running economy index calculated by the detailed model.

Mrinal Kanti Rajak, Rajen Pudur

This paper presents a novel mathematical framework for modelling and optimizing Phase-Locked Loop (PLL) dynamics in grid-connected systems using a hybrid optimization approach. The proposed model combines a state-space representation of PLL dynamics with an innovative dual-optimization algorithm integrating Particle Swarm Optimization (PSO) and Gradient Descent (GD). A comprehensive mathematical model is developed, incorporating the nonlinear dynamics of the PLL system through differential equations and transfer functions. The hybrid optimization framework is formulated as a constrained optimization problem, where PSO provides global search capabilities while GD ensures local convergence. Numerical simulations demonstrate the model's superior performance compared to conventional approaches including SRF-PLL, DDSRF-PLL, and MSOGI-PLL, achieving 40% faster convergence and maintaining phase tracking errors below 3 degrees during severe grid disturbances. The framework offers a systematic method for analyzing and optimizing dynamical systems in power electronics.

Xiaojun Wang, Shaolong Shu, Feng Lin

Cyber attacks are unavoidable in networked discrete event systems where the plant and the supervisor communicate with each other via networks. Because of the nondeterminism in observation and control caused by cyber attacks, the language generated by the supervised system becomes nondeterministic. The small language is defined as the lower bound on all possible languages that can be generated by the supervised system, which is needed for a supervised system to perform some required tasks under cyber attacks. In this paper, we investigate supervisory control for the small language. After introducing CA-S-controllability and CA-S-observability, we prove that the supervisory control problem of achieving a required small language is solvable if and only if the given language is CA-Scontrollable and CA-S-observable. If the given language is not CA-S controllable and/or CA-S-observable, we derive conditions under which the infimal CA-S-controllable and CA-S-observable superlanguage exists and can be used to design a supervisor satisfying the given requirement.

Addie McCurdy, Andrew Gusty, Emily Jensen

The optimal controller design problem for a linear, first-order spatially-invariant distributed parameter system is considered. Through a case study of the Linear Quadratic Regulator (LQR) problem for the diffusion equation over the torus, it is illustrated that the optimal controller design problem can be equivalently formulated as an optimization problem over the system's closed-loop mappings, analogous to the System Level Synthesis framework. This reformulation is solved analytically to recover the LQR for the diffusion equation, and an internally stable implementation of this controller is recovered from the optimal closed-loop mappings. It is further demonstrated that a class of spatio-temporal constraints on the closed-loop maps can be imposed on this closed-loop formulation while preserving convexity.

Lixin Tang, Y. Meng

Yingzhe Jia, Yiliang Li, Jun e Feng

In this paper, the demand response considering interactive decision making between residential users and utility companies is modelled and controlled using networked evolutionary game (NEG) theory. The NEG is achieved via a widely used mathematical tool, namely the semi-tensor product (STP) of matrices, through which, feedback controls can be implemented to dynamically regulate the game-based networks. Firstly, the dynamic interactions between utility companies who provide various energy consumption packs and residential users who can intelligently choose preferable packs are modelled in the state-space form, in which both sides are consistently pursuing their maximum payoffs. Then, a quantitive analysis of such system’s equilibria is conducted using rigorous mathematical derivations, followed by the design of a profile feedback controller when the existing equilibria are not satisfactory towards the required energy consumption. Finally, an illustrative example is demonstrated, where the dynamic gaming between utility companies and users is illustrated and the effectiveness of the proposed feedback control is validated. Note to Practitioners—This paper addresses a very practical engineering problem, which is the demand response in the modern smart grid. Different from general optimization approaches in the existing works, the method proposed by this paper improves the demand response via feedback control of networked systems, which is based on the rigorous matrix approaches via state space equations. In addition, the work in this paper considers very practical factors in actual engineering (i.e., users with different action choices and decision logics being all together), which also provides much value to practitioners than the relevant works with similar theoretical approaches. The method proposed in this paper provides an effective regulation of the demand response in practice and can be flexibly adjusted according to actual situations.

Andrea Marinelli, Nicoló Boccardo, F. Tessari et al.

The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish ADL. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of upper limb prostheses. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and toward the engineering of human-centered artificial limbs.

Alexander A. Nguyen, Faryar Jabbari, Magnus Egerstedt

This paper examines pairwise collaborations in heterogeneous multi-robot systems. In particular, we focus on how individual robots, with different functionalities and dynamics, can enhance their resilience by forming collaborative arrangements that result in new capabilities. Control barrier functions are utilized as a mechanism to encode the safe operating regions of individual robots, with the idea being that a robot may be able to operate in new regions that it could not traverse alone by working with other robots. We explore answers to three questions: &#x201C;Why should robots collaborate?&#x201D;, &#x201C;When should robots collaborate?&#x201D;, and &#x201C;How can robots collaborate?&#x201D; To that end, we introduce the safely reachable set &#x2013; capturing the regions that individual robots can reach safely, either with or without help, while considering their initial states and dynamics. We then describe the conditions under which a help-providing robot and a help-receiving robot can engage in collaboration. Next, we describe the pairwise collaboration framework, modeled through hybrid automata, to show how collaborations can be structured within a heterogeneous multi-robot team. Finally, we present case studies that are conducted on a team of mobile robots.

Wei Wei, Bowen Duan, Weicun Zhang et al.

The nonlinear system control is a classical problem in control engineering. In this paper, rather than try to get accurate nonlinear dynamics, the nonlinear and uncertain dynamics are viewed as a signal. It can be estimated by an extended state observer, and compensated by a control law. Accordingly, the nonlinear uncertain system is linearized. Based on the linearized system and the key point of the U-model control, a controller can be designed to obtain predetermined closed-loop system dynamics. To get a more satisfactory performance, a compensation signal of the total disturbance estimation (CSTDE) is designed. Based on the CSTDE, a compensation of the total disturbance estimation based extended state observer (CTDESO) and a fast-response active disturbance rejection control (FRADRC) are proposed. Convergence of the CTDESO and the closed-loop stability of the FRADRC are analyzed. Four nonlinear systems are considered to testify the proposed approaches. Numerical results show that, no matter disturbances exist or not, the proposed CTDESO can linearize a nonlinear system better, and the predetermined closed-loop responses can also be achieved more satisfactorily by the FRADRC.

Nesrine Belarif, Mohamed Tadjine, Messaoud Chakir

Fethi Bencherki, Anders Rantzer

An adaptive controller is proposed and analyzed for the class of infinite-horizon optimal control problems in positive linear systems presented in (Ohlin et al., 2024b). This controller is derived from the solution of a "data-driven algebraic equation" constructed using the model-free Bellman equation from Q-learning. The equation is driven by data correlation matrices that do not scale with the number of data points, enabling efficient online implementation. Consequently, a sufficient condition guaranteeing stability and robustness to unmodeled dynamics is established. The derived results also provide a quantitative characterization of the interplay between excitation level and robustness to unmodeled dynamics. The class of optimal control problems considered here is equivalent to Stochastic Shortest Path (SSP) problems, allowing for a performance comparison between the proposed adaptive policy and model-free algorithms for learning the stochastic shortest path, as demonstrated in the numerical experiment.

Alessandro Scagliotti

In this paper, we consider ensembles of control-affine systems in $\mathbb{R}^d$, and we study simultaneous optimal control problems related to the worst-case minimization. After proving that such problems admit solutions, denoting with $(Θ^N)_N$ a sequence of compact sets that parametrize the ensembles of systems, we first show that the corresponding minimax optimal control problems are $Γ$-convergent whenever $(Θ^N)_N$ has a limit with respect to the Hausdorff distance. Besides its independent interest, the previous result plays a crucial role for establishing the Pontryagin Maximum Principle (PMP) when the ensemble is parametrized by a set $Θ$ consisting of infinitely many points. Namely, we first approximate $Θ$ by finite and increasing-in-size sets $(Θ^N)_N$ for which the PMP is known, and then we derive the PMP for the $Γ$-limiting problem. The same strategy can be pursued in applications, where we can reduce infinite ensembles to finite ones to compute the minimizers numerically. We bring as a numerical example the Schrödinger equation for a qubit with uncertain resonance frequency.

WANG Shenghui, TIAN Qin, LIU Lihao et al.

Nickel-cadmium alkaline batteries are the core energy source for auxiliary devices of electric multiple units. Hence, an accurate estimation of their state of charge (SOC) is significantly important for prolonging battery life and improving energy efficiency. Given the limitations of existing SOC estimation methods when dealing with small-sample battery cycling data, this paper proposes an attention mechanism integrated convolutional neural network-gated recurrent unit (CNN-GRU) model for battery SOC estimation, and experimental validation is conducted on the LPH140A model nickel-cadmium batteries used in electric multiple units. The model employs a convolutional neural network (CNN) to extract short-term feature dependencies from long sequences within the battery cycling data. Then, an attention mechanism-integrated gated recurrent unit (GRU) is adopted to capture long spatial distance dependencies of the extracted feature data, resulting in more precise battery SOC estimation. To precisely estimate the SOC of small-sample battery cycling data, this paper transforms the continuous regression model into a classification problem, discretizes the battery SOC ranges, and converts the final prediction result into discrete SOC values. The experimental results show that compared with the CNN-GRU algorithm, the proposed approach improves three key metrics — root mean square error, mean absolute error, and mean relative error by 18.90%, 17.92% and 19.78%, respectively, demonstrating impressive prediction accuracy and stability.

Xiaodan Wang, Peng Wang, Yafei Song et al.

Radar high‐resolution range profile (HRRP) is widely used in radar automatic target recognition due to its advantages such as easy availability, convenient processing, and small storage space. Current recognition methods for HRRP sequences mainly focus on the temporal information of HRRP sequences, which cannot fully utilize the temporal and spatial information contained in HRRP sequences. Moreover, most of these methods fail in long‐range modeling and global information extraction of HRRP sequences. To solve above problems, a HRRP sequence recognition method based on transformer with temporal–spatial fusion and label smoothing (TSF–transformer–LS) is proposed. TSF–transformer–LS contains temporal transformer blocks and spatial transformer blocks, which are used to extract deep global features of HRRP sequences in the time domain and space domain, respectively. Then, an attention fusion mechanism is developed to realize the adaptive fusion of temporal and spatial features. Moreover, label smoothing is used to add noise to sample labels, which can solve the overfitting problem of transformer caused by a large amount of noise hidden in HRRP in real scenes. Experiments on MSTAR, a standard dataset, show that the proposed method outperforms other methods in recognition performance. Furthermore, the effectiveness and interpretability of the method are explored.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.