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

Simon Schmidt, Nicole Gehring, Abdurrahman Irscheid

The paper presents an approach to flatness-based control design for hyperbolic multi-input systems, building upon the hyperbolic controller form (HCF). The transformation into HCF yields a simplified system representation that considerably facilitates the design of state feedback controllers for trajectory tracking. The proposed concept is demonstrated for a Timoshenko beam and validated through numerical simulations, demonstrating trajectory tracking and closed-loop stability.

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Yu Zheng, Ningtao Peng, Hao Qi et al.

The classical distributed hybrid flow shop scheduling problem (DHFSP) only considers static production settings while ignores operation inspection and reprocessing. However, in the actual production, the manufacturing environment is usually dynamic; and the operation inspection and reprocessing are very necessary to avoid unqualified jobs from being transported to other production units and to make reasonable arrangements for unqualified and unprocessed jobs. In this paper, we propose a DHFSP with operation inspection and reprocessing (DHFSPR) for the first time, in which the operation inspection and reprocessing as well as the processing time and energy consumption are considered simultaneously. An improved memetic algorithm (IMA) is then designed to solve the DHFSPR, where some effective crossover and mutation operators, a new dynamic rescheduling method (DRM) and local search operator (LSO) are integrated. A total 60 DHFSPR benchmark instances are constructed to verify the performance of our IMA. Extensive experiments carried out demonstrate that the DRM and LSO can effectively improve the performance of IMA, and the IMA has obvious superiority to solve the DHFSPR problem compared with other three well-known algorithms. Our proposed model and algorithm here will be beneficial for the production managers who work with distributed hybrid shop systems in scheduling their production activities by considering operation inspection and reprocessing.

Panagiotis Rousseas, Haejoon Lee, Dimos V. Dimarogonas et al.

Control synthesis under constraints is at the forefront of research on autonomous systems, in part due to its broad application from low-level control to high-level planning, where computing control inputs is typically cast as a constrained optimization problem. Assessing feasibility of the constraints and selecting among subsets of feasible constraints is a challenging yet crucial problem. In this work, we provide a novel theoretical analysis that yields necessary and sufficient conditions for feasibility assessment of linear constraints and based on this analysis, we develop novel methods for feasible constraint selection in the context of control of autonomous systems. Through a series of simulations, we demonstrate that our algorithms achieve performance comparable to state-of-the-art methods while offering improved computational efficiency. Importantly, our analysis provides a novel theoretical framework for assessing, analyzing and handling constraint infeasibility.

LUO Yuan, YI Jie, BAI Jinlei et al.

High-speed automatic train operation (ATO) systems inherently exhibit strong nonlinearity and uncertainty. In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. Based on a multi-particle train model, an initial analysis explores the impacts of additional resistance changes on train operation. Subsequently, a multi-object performance indicator function containing control input constraints is constructed, combined with key indicators during the operation of high-speed trains, such as speed tracking accuracy, stopping accuracy, and riding comfort. Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. Taking into account various factors including influences of external environments and passenger movement during train operation, the resistance changes greatly, making it difficult to establish an accurate mathematical model, a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled auto-regressive integrated moving average model (CARIMA) of the train control system under different operational conditions. This approach aims to improve the robustness of the control system. Simulation results show that, compared with traditional GPC in the absence of the feed forward functionality and PID controller, the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a ±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

Dengfeng Li, Jian Li, Pengcheng Wu et al.

Soft actuators have been an important research focus in robotics due to their advantages in nondestructive contact and excellent motion adaptability, which enable flexible manipulation, extreme environments exploration, and in vivo surgical treatment. Various soft robots actuated electrically, magnetically, optically, and fluidically are built toward different application scenarios. Among them, electrical actuation method is an advanced choice to actuate and control soft actuators that are expected to be compatible and integrated with existing industrial robotic systems and wearable intelligent devices. Current robotic systems have higher requirements on the actuators’ function, which can be explored through material and structural designs. Based on a variety of deformable materials, the structural design enables the soft actuators to span from low dimension to high dimension with further improvement in function. Therefore, in this review, 2D and 3D electrical‐based soft actuators from the perspective of dimension design are introduced, which involve functional materials, structure design, and fabrication technology. Some novel 3D fabrication methods, such as the 3D compressive buckling process, are summarized to build 3D soft robots. This review aims at offering important guidelines for the development of soft actuators and the construction of integrated robotic systems in the future.

V. Cerone, S. M. Fosson, S. Pirrera et al.

The continuous-time analysis of existing iterative algorithms for optimization has a long history. This work proposes a novel continuous-time control-theoretic framework for equality-constrained optimization. The key idea is to design a feedback control system where the Lagrange multipliers are the control input, and the output represents the constraints. The system converges to a stationary point of the constrained optimization problem through suitable regulation. Regarding the Lagrange multipliers, we consider two control laws: proportional-integral control and feedback linearization. These choices give rise to a family of different methods. We rigorously develop the related algorithms, theoretically analyze their convergence and present several numerical experiments to support their effectiveness concerning the state-of-the-art approaches.

Changrui Liu, Shengling Shi, Bart De Schutter

Handling model mismatch is a common challenge in model predictive control (MPC). While robust MPC is effective, its conservatism often makes it less desirable. Certainty-equivalence MPC (CE-MPC), which uses a nominal model, offers an appealing alternative due to its design simplicity and low computational costs. This paper investigates CE-MPC for uncertain nonlinear systems with multiplicative parametric uncertainty and input constraints that are inactive at the steady state. The primary contributions are two-fold. First, a novel perturbation analysis of the MPC value function is provided, without assuming the Lipschitz continuity of the stage cost, better tailoring the widely used quadratic cost and having broader applicability in value function approximation, learning-based MPC, and performance-driven MPC design. Second, the stability and performance analysis of CE-MPC are provided, quantifying the suboptimality of CE-MPC compared to the infinite-horizon optimal controller with perfect model knowledge. The results provide insights in how the prediction horizon and model mismatch jointly affect stability and the worst-case performance. Furthermore, the general results are specialized to linear quadratic control, and a competitive ratio bound is derived, serving as the first competitive-ratio bound for MPC of uncertain linear systems with input constraints and multiplicative uncertainty.

Jake Welde, Vijay Kumar

In this work, we address the design of tracking controllers that drive a mechanical system's state asymptotically towards a reference trajectory. Motivated by aerospace and robotics applications, we consider fully-actuated systems evolving on the broad class of homogeneous spaces (encompassing all vector spaces, Lie groups, and spheres of any finite dimension). In this setting, the transitive action of a Lie group on the configuration manifold enables an intrinsic description of the tracking error as an element of the state space, even in the absence of a group structure on the configuration manifold itself (e.g., for $\mathbb{S}^2$). Such an error state facilitates the design of a generalized control policy depending smoothly on state and time, which drives the geometric tracking error to a designated origin from almost every initial condition, thereby guaranteeing almost global convergence to the reference trajectory. Moreover, the proposed controller simplifies elegantly when specialized to a Lie group or the n-sphere. In summary, we propose a unified, intrinsic controller guaranteeing almost global asymptotic trajectory tracking for fully-actuated mechanical systems evolving on a broad class of manifolds. We apply the method to an axisymmetric satellite and an omnidirectional aerial robot.

Samuel Mallick, Azita Dabiri, Bart De Schutter

This paper presents a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimization problems, requiring significant computation power or time. We propose a distributed MPC scheme that requires solving only convex optimization problems. The key contribution is a novel method, based on the alternating direction method of multipliers, for solving the non-convex optimal control problem that arises due to the PWA dynamics. We present a distributed MPC scheme, leveraging this method, that explicitly accounts for the coupling between subsystems by reaching agreement on the values of coupled states. Stability and recursive feasibility are shown under additional assumptions on the underlying system. Two numerical examples are provided, in which the proposed controller is shown to significantly improve the CPU time and closed-loop performance over existing state-of-the-art approaches.

Chia-Hsien Shih, Noel Naughton, Udit Halder et al.

Inspired by the unique neurophysiology of the octopus, a hierarchical framework is proposed that simplifies the coordination of multiple soft arms by decomposing control into high‐level decision‐making, low‐level motor activation, and local reflexive behaviors via sensory feedback. When evaluated in the illustrative problem of a model octopus foraging for food, this hierarchical decomposition results in significant improvements relative to end‐to‐end methods. Performance is achieved through a mixed‐modes approach, whereby qualitatively different tasks are addressed via complementary control schemes. Herein, model‐free reinforcement learning is employed for high‐level decision‐making, while model‐based energy shaping takes care of arm‐level motor execution. To render the pairing computationally tenable, a novel neural network energy shaping (NN‐ES) controller is developed, achieving accurate motions with time‐to‐solutions 200 times faster than previous attempts. The hierarchical framework is then successfully deployed in increasingly challenging foraging scenarios, including an arena littered with obstacles in 3D space, demonstrating the viability of the approach.

Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach.

Tamás Czimmermann, Marcello Chiurazzi, M. Milazzo et al.

Quality control in industry involves trained operators to manipulate and inspect metallic surfaces in order to identify, and eventually correct, manufacturing defects. These tasks are manually performed, and a poor performance (e.g., missing defects) leads to an increase of the costs and prolongation of the manufacturing time cycle. In this work, we propose a multi-agent robotic platform to autonomously perform Industry 4.0 quality control processes of metallic surfaces. The platform consists of three anthropomorphic robots with custom-made end-effectors designed to manipulate, inspect, and eventually correct a metallic frame of a motorcycle. The description of a novel multi-agent platform is followed by the presentation of the developed inspection procedure, in which a linear laser scanner is used to reconstruct the three-dimensional metallic surface of a motorcycle with a resolution of ~0.1 mm. In order to validate the platform, we perform a set of experiments to assess the performance of the robotic platform in a real Industry 4.0 scenario. Results confirmed that such a system guarantees a sub-millimetric precision to identify defects on complex-shaped metallic surfaces and effectively correct them. The proposed robotic platform can be adopted for overcoming the drawbacks of a traditional procedure that relies on visual-tactile manual defects correction (e.g., low-repeatability, high-subjectivity) and is scalable to different industrial applications. The proposed approach aims to elevate the role of operators to expert supervisors of the process, limiting the interactions with potentially-dangerous tools/procedures and thus improving the working conditions in an industrial 4.0 scenario. Note to Practitioners—This work was motivated by a crucial need in industry, i.e. to automatize the manufacturing quality control, translating the commonly-used visual-based manual approach performed by operators to an objective robotic one that relies on defect detection by using a linear laser scanner. A novel multi-agent robotic platform, developed by the authors, showed its effectiveness in automatizing complex tasks, in which huge workspace and different tools are required. The aim of the paper was to develop a fully automatized application that covers the entire quality control process, while focusing on one specific phase, i.e. automatic inspection. The integrated software and the infrastructural communication protocols of the entire robotic platform were designed to be flexible in order to realize a new reference for industrial applications, where a multi-agent approach is demanded. The experimental validation focused on a specific use case (a motorcycle frame selected due to its complex structure, i.e. multiple curvatures with variable radii, and large volumes), but the proposed platform and the implemented methodology have to be intended as a general purpose approach, adaptable to any industrial process and mechanical component. The authors, starting by a laboratory development with extensive tests, applied and demonstrated the feasibility of the proposed approach in a real Industry 4.0 scenario.

Po-Han Chiang, Melissa Wong, S. Dey

Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Lassoued Zeineb, Abderrahim Kamel

Zongjie Shen, Chun Zhao, Lixing Kang et al.

Emerging optical synapses with in‐memory computing sensor (IMCS) performance are considered to be one of the most effective candidates to circumvent the bottleneck of the current Von Neumann structure while developing neuromorphic systems with higher effectiveness and lower energy consumption. Biomimetic properties of optical IMCS synapses in function and form indicate the higher requirements for utilized functional materials, such as stronger optical sensitivity and lower energy dissipation. Because of properties with high optical‐sensitivity efficiency and excellent electrical conductivity, low‐dimensional nanomaterials have received tremendous interest in modulating optical‐induced synaptic plasticity and emulating optical‐triggered neuromorphic activity of optical IMCS synapses. Herein, a comprehensive summary of optical IMCS synapses based on low‐dimensional nanomaterials is introduced systematically for the first time, including 0D, 1D, and 2D materials. In addition, the content of biomimetic synaptic characteristics, materials classification, operation mechanism, and neuromorphic applications of optical IMCS synapses based on low‐dimensional nanomaterials are also summarized in this work. At last, the challenges and outlook related to artificial optical IMCS synapses with low‐dimensional nanomaterials are provided.

Runyan Lv, Yunze Cai, Xiangxiang Dong

Abstract To solve the problem of missing measurements in highly manoeuvring target tracking, an expected‐mode‐augmentation‐based unscented Kalman filter with missing measurements compensation (EMA‐MMCUKF) is designed based on the variable‐structure multiple model method. In the proposed EMA‐MMCUKF, the random missing measurements are described by the Bernoulli distribution, and the one‐step prediction is used as the compensation. Based on the proposed EMA‐MMCUKF, a Bayesian estimation‐based expected‐mode‐augmentation unscented Kalman filter with missing measurements compensation (BE‐EMA‐MMCUKF) is proposed for adaptive estimation of the sensor measurement reception rate, where the unknown measurement reception rate can be estimated by fully utilising prior information. Simulation results demonstrate that the proposed EMA‐MMCUKF can effectively track the manoeuvring target at different measurement reception rates. Moreover, when the sensor prior information differs significantly from the true measurement reception rate, the proposed BE‐EMA‐MMCUKF can effectively estimate the unknown sensor measurement reception rate and improve the accuracy of manoeuvring target tracking compared with non‐estimation of the sensor measurement reception rate.

Max H. Cohen, Calin Belta

This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee stability and safety of uncertain nonlinear systems with high relative degree. We first introduce the notion of a High Order Robust Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control policies guaranteeing satisfaction of high relative degree safety constraints in the face of parametric model uncertainty. The developed approach guarantees safety by initially accounting for all possible parameter realizations but adaptively reduces uncertainty in the parameter estimates leveraging data recorded online. We then introduce the notion of an Exponentially Stabilizing Adaptive Control Lyapunov Function (ES-aCLF) that leverages the same data as the HO-RaCBF controller to guarantee exponential convergence of the system trajectory. The developed HO-RaCBF and ES-aCLF are unified in a quadratic programming framework, whose efficacy is showcased via two numerical examples that, to our knowledge, cannot be addressed by existing adaptive control barrier function techniques.

Christoph Mayr-Dorn, Michael Vierhauser, Stefan Bichler et al.

Regulations, standards, and guidelines for safety-critical systems stipulate stringent traceability but do not prescribe the corresponding, detailed software engineering process. Given the industrial practice of using only semi-formal notations to describe engineering processes, processes are rarely "executable" and developers have to spend significant manual effort in ensuring that they follow the steps mandated by quality assurance. The size and complexity of systems and regulations makes manual, timely feedback from Quality Assurance (QA) engineers infeasible. In this paper we propose a novel framework for tracking processes in the background, automatically checking QA constraints depending on process progress, and informing the developer of unfulfilled QA constraints. We evaluate our approach by applying it to two different case studies; one open source community system and a safety-critical system in the air-traffic control domain. Results from the analysis show that trace links are often corrected or completed after the fact and thus timely and automated constraint checking support has significant potential on reducing rework.

Xiaocong Chen, Lina Yao, Xianzhi Wang et al.

Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. In most reinforcement learning applications, reward functions provide the critical guideline for optimization. However, current reinforcement learning-based methods rely on manually-defined reward functions, which cannot adapt to dynamic, noisy environments. Moreover, they generally use task-specific reward functions that sacrifice generalization ability. We propose a generative inverse reinforcement learning for user behavioral preference modeling to address the above issues. Instead of using predefined reward functions, our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN. Our model provides a general approach to characterizing and explaining underlying behavioral tendencies. Our experiments show our method outperforms state-of-the-art methods in several scenarios, namely traffic signal control, online recommender systems, and scanpath prediction.