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

Serdar Incekara, Seongil Kwon, Gangil Kwon et al.

Replicating the flexible and efficient locomotion of biological snakes remains a significant challenge in robotics. Conventional snake robots, often built from serially linked rigid joints, require complex control strategies to simultaneously manage body undulation and propulsive ground forces. Based on the previous theoretical study, this article presents the first physical realization of a new principle that simplifies locomotion control by decoupling these two tasks. The core idea is that by using a flexible continuum body, different gaits can be generated by superimposing simple globally‐applied tensions (for vertical bending and axial twisting) onto a basic planar undulation. These global tensions, combined with the robot's compliance and weight, passively shape the required ground contact patterns, eliminating the need for active force control at individual points. The design and implementation of a tendon‐driven continuum snake robot that embodies this principle is presented. The robot uses globally routed tendons, actuated by centrally‐located motors, to create uniform bending and twisting. Through experiments, it is demonstrated that the robot can produce three distinct gaits—forward, backward, and sidewinding—on flat ground simply by changing the global actuation mode, and demonstrate its capability for intuitive steering.

Mingming Ji, Zhixiong Zhang

For networked control systems with logarithmic quantization, the state estimation is important. Different from the traditional state estimation methods where the uncertainties are assumed to have known probability distributions, the set-membership estimation method used in this paper only supposes that the uncertainties are bounded. This assumption is more accurate and less conservative in practical application. This paper considers the ellipsoidal set-membership estimation of discrete-time linear systems with logarithmic quantized input. The set-membership estimator, which belongs to the ellipsoid, is proposed. The estimation error is analysed, and the sufficient conditions to guarantee the boundedness of the estimation error are given. Using the sector bound property of the logarithmic quantizer, the asymptotic stability of the system and the set-membership performance constraint are analysed, respectively. According to a numerical example, it is shown that the theoretical results obtained in this paper are effective and the set-membership estimation can estimate the system state effectively. The research presents an ellipsoidal set-membership estimation for networked control systems subject to logarithmic quantized control input. It has high computational efficiency and thus has great significance.

LI Shasha, XIA Manjiang, ZHANG Jianjun

To address challenges posed by model uncertainties due to joint friction and imprecise measurements faced by underwater manipulators during diverse operating missions, this paper proposes a model reference adaptive impedance control method aimed at enhancing robustness and operational stability. First, a desired impedance model based on the errors between the standard force and the operating force was developed. Adaptive laws and a bounded-gain-forgetting adaptive update strategy were formulated based on the operational spatial position and the desired position output from the impedance model, enabling the manipulator's end-effector to precisely track standard force signals. Then, a Lyapunov function was established, proving the global stability and force-position tracking performance of the system. Finally, a two-degree-of-freedom manipulator simulation experiment was conducted on the Matlab/Simulink platform. The results demonstrate that the proposed controller exhibits excellent adaptive performance and robustness, ensuring superior force-position tracking capabilities.

Ludovico Musenich, Lorenzo Strozzi, Massimiliano Avalle et al.

Helmets are critical for minimizing the risk of traumatic brain injuries in road accidents and sports. Traditional designs feature a rigid outer shell and a deformable inner liner of foam for energy absorption. Recent advancements have introduced architected materials as alternatives to conventional foams, offering improved safety and multifunctionality. Herein, a diatom‐inspired architected material optimized for energy absorption in helmet liners is proposed and designed for a new concept of multifunctional helmets. The material is modeled using CAD tools, its performance is evaluated through finite element analysis and quasistatic compression tests on 3D‐printed elastomeric samples, and parametric optimization is applied. The results demonstrate energy absorption comparable to conventional materials, laying the groundwork for future studies on fluid‐dynamic behavior and multifunctional helmet designs.

Allan Andre do Nascimento, Han Wang, Antonis Papachristodoulou et al.

In this work we propose a Model Predictive Control (MPC) formulation that splits constraints in two different types. Motivated by safety considerations, the first type of constraint enforces a control-invariant set, while the second type could represent a less restrictive constraint on the system state. This distinction enables closed-loop sub- optimality results for nonlinear MPC with heterogeneous state constraints (distinct constraints across open loop predicted states), and no terminal elements. Removing the non-invariant constraint recovers the partially constrained case. Beyond its theoretical interest, heterogeneous constrained MPC shows how constraint choices shape the system's closed loop. In the partially constrained case, adjusting the constraint horizon (how many predicted- state constraints are enforced) trades estimation accuracy for computational cost. Our analysis yields first, a sub- optimality upper-bound accounting for distinct constraint sets, their horizons and decay rates, that is tighter for short horizons than prior work. Second, to our knowledge, we give the first lower bound (beyond open-loop cost) on closed-loop sub-optimality. Together these bounds provide a powerful analysis framework, allowing designers to evaluate the effect of horizons in MPC sub-optimality. We demonstrate our results via simulations on nonlinear and linear safety-critical systems.

Saber Omidi, Marek Petrik, Se Young Yoon et al.

Safety in stochastic control systems, which are subject to random noise with a known probability distribution, aims to compute policies that satisfy predefined operational constraints with high confidence throughout the uncertain evolution of the state variables. The unpredictable evolution of state variables poses a significant challenge for meeting predefined constraints using various control methods. To address this, we present a new algorithm that computes safe policies to determine the safety level across a finite state set. This algorithm reduces the safety objective to the standard average reward Markov Decision Process (MDP) objective. This reduction enables us to use standard techniques, such as linear programs, to compute and analyze safe policies. We validate the proposed method numerically on the Double Integrator and the Inverted Pendulum systems. Results indicate that the average-reward MDPs solution is more comprehensive, converges faster, and offers higher quality compared to the minimum discounted-reward solution.

Yi Shao, Huiru Zhu, Xuefeng Deng et al.

To study the influence of chaotic systems on wheat image encryption, a method to determine the optimal chaotic system encryption based on a wheat image is proposed. Ten different chaotic system schemes were combined to encrypt the wheat images using 13 common chaotic maps. The best chaotic system scheme was obtained by considering the anti-attack capability of these encryption schemes, which analyzed eight commonly used image encryption performance evaluation indexes. The experimental results show that the new four-dimensional chaotic system has the best encryption effect and is suitable for wheat image encryption. The proposed scheme for wheat image encryption based on chaotic systems provides a reference for other crop image encryption methods.

Guangshuai Peng, Lizhen Wang, Shan Tian et al.

Pressure ulcers, which can result from prolonged sitting, pose a significant challenge for wheelchair users. Soft robotics has considerable potential in preventing pressure ulcers. However, current soft robotics, constructed from flexible materials, face limitations including insufficient proprioception and controllability. Herein, a vacuum‐powered proprioceptive soft–rigid hybrid actuator (PSHA) module and a modular pressure redistribution cushion (MPRC) developed using this module are introduced. This PSHA module is capable of detecting both position and force. Each module within the MPRC is equipped with onboard control, proprioceptive sensation, and inter‐module communication. The MPRC incorporates a closed‐loop control system, enabling it to actively redistribute pressure, thereby preventing prolonged compression of local soft tissue during periods of inactivity. The proposed PSHA module, as evidenced in its application in pressure redistribution cushions, offers a promising approach for designs intent on reducing the risk of pressure ulcers. This study significantly contributes to the advancement of assistive technology, with the potential to enhance the quality of life for individuals with immobility or limited mobility.

Pavel Osinenko, Stefan Streif

This paper investigates the possibility of constructive extraction of measurable selector from set-valued maps which may commonly arise in viability theory, optimal control, discontinuous systems etc. For instance, existence of solutions to certain differential inclusions, often requires iterative extraction of measurable selectors. Next, optimal controls are in general non-unique which naturally leads to an optimal set-valued function. Finally, a viable control law can be seen, in general, as a selector. It is known that selector theorems are non-constructive and so selectors cannot always be extracted. In this work, we analyze under which particular conditions selectors are constructively extractable. An algorithm is derived from the theorem and applied in a computational study with a three-wheel robot model.

C. Scherer, C. Ebenbauer, Tobias Holicki

We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design accelerated gradient descent algorithms with optimal guaranteed convergence rates by solving small-sized convex semi-definite programs. It is shown that this extends to the design of extremum controllers, with the goal to regulate the output of a general linear closed-loop system to the minimum of an objective function. Numerical experiments illustrate that we can not only recover gradient decent and the triple momentum variant of Nesterov's accelerated first order algorithm, but also automatically syn-thesize optimal algorithms even if the gradient information is passed through non-trivial dynamics, such as time-delays.

Pedram Rabiee, Jesse B. Hoagg

This paper presents a new approach for guaranteed safety subject to input constraints (e.g., actuator limits) using a composition of multiple control barrier functions (CBFs). First, we present a method for constructing a single CBF from multiple CBFs, which can have different relative degrees. This construction relies on a soft minimum function and yields a CBF whose $0$-superlevel set is a subset of the union of the $0$-superlevel sets of all the CBFs used in the construction. Next, we extend the approach to systems with input constraints. Specifically, we introduce control dynamics that allow us to express the input constraints as CBFs in the closed-loop state (i.e., the state of the system and the controller). The CBFs constructed from input constraints do not have the same relative degree as the safety constraints. Thus, the composite soft-minimum CBF construction is used to combine the input-constraint CBFs with the safety-constraint CBFs. Finally, we present a feasible real-time-optimization control that guarantees that the state remains in the $0$-superlevel set of the composite soft-minimum CBF. We demonstrate these approaches on a nonholonomic ground robot example.

Mohammadreza Kamaldar, Dennis S. Bernstein

By optimizing the predicted performance over a receding horizon, model predictive control (MPC) provides the ability to enforce state and control constraints. The present paper considers an extension of MPC for nonlinear systems that can be written in pseudo-linear form with state- and control-dependent coefficients. The main innovation is to apply quadratic programming iteratively over the horizon, where the predicted state trajectory is updated based on the updated control sequence. Output-feedback control is facilitated by using the block-observable canonical form for linear, time-varying dynamics. This control technique is illustrated on various numerical examples, including the Kapitza pendulum with slider-crank actuation, the nonholonomic integrator, the electromagnetically controlled oscillator, and the triple integrator with control-magnitude saturation.

Romeo Ortega, Emmanuel Nuño

Jianping Lin, Nikhil V. Divekar, Gray C. Thomas et al.

Task-specific, trajectory-based control methods commonly used in exoskeletons may be appropriate for individuals with paraplegia, but they overly constrain the volitional motion of individuals with remnant voluntary ability (representing a far larger population). Human-exoskeleton systems can be represented in the form of the Euler-Lagrange equations or, equivalently, the port-controlled Hamiltonian equations to design control laws that provide <italic>task-invariant</italic> assistance across a continuum of activities/environments by altering energetic properties of the human body. We previously introduced a port-controlled Hamiltonian framework that parameterizes the control law through basis functions related to gravitational and gyroscopic terms, which are optimized to fit normalized able-bodied joint torques across multiple walking gaits on different ground inclines. However, this approach did not have the flexibility to reproduce joint torques for a broader set of activities, including stair climbing and stand-to-sit, due to strict assumptions related to input-output passivity, which ensures the human remains in control of energy growth in the closed-loop dynamics. To provide biomimetic assistance across all primary activities of daily life, this paper generalizes this energy shaping framework by incorporating vertical ground reaction forces and global planar orientation into the basis set, while preserving passivity between the human joint torques and human joint velocities. We present an experimental implementation on a powered knee-ankle exoskeleton used by three able-bodied human subjects during walking on various inclines, ramp ascent/descent, and stand-to-sit, demonstrating the versatility of this control approach and its effect on muscular effort.

Chaogai Xue, Wen Wen, Shumin Li

In order to explore how cross-border e-commerce and logistics companies make decisions during synergy in a dynamic environment, this paper first establishes an evolutionary game model to study the influencing factors of enterprises’ strategies in the process of synergy in a dynamic environment. Then the study uses sensitivity analysis and numerical simulation to analyse the impact of different enterprises’ strategies on synergy. Finally, the synergistic strategies of enterprises in different dynamic environments are discussed. We found that: (1) capital and labour input of cross-border e-commerce platforms will affect the synergy. In a highly dynamic environment, the influences are greater. (2) There is a positive correlation between the technical level of the platform and synergistic degree, while the high technical level of logistics enterprises will inhibit synergy. However, in an unstable environment, the positive effect still exists, and the change in the logistics enterprise technology level has little effect on synergy. (3) The technical level of the cross-border e-commerce platform has the greatest impact on the benefits of both parties, and then is the lower cost of labour and capital input, while the technical level of logistics companies has little impact.

YANG Jiang, YANG Yibin, OU Shengfen et al.

At present, urban rail signal maintenance system can only alarm a single fault source, and can not quickly locate the cause of fault and guide operation and maintenance personnel to deal with the fault. However, urban rail signal system has a large variety of faults, complex diagnosis and analysis logic, customized development of fault diagnosis procedures for different scenarios can not, quickly respond to the needs of operation and maintenance, and the cost is high. To solve this problem, this paper develops an information-based and platform-based urban rail signal fault diagnosis system based on knowledge model to realize signal system fault knowledge modeling, fault diagnosis semantic correlation and fault diagnosis process modeling. In order to realize the complete reasoning of signal system fault diagnosis, OWL DL(ontology web language description logic) is used to model knowledge, extract and describe the analysis logic of signal system fault diagnosis, and establish the knowledge model. The operation state of system equipment is used to match the fault cause, and the mapping from signal system equipment state space to fault cause space is used to realize the fault self-diagnosis of signal system equipment, so as to provide decision support for the production, operation and maintenance management of signal system equipment. Application results show that it can reduce the operation safety accident rate by 15% and improve the fault handling efficiency by 20%.

S. Palani

Shawn Priore, Christopher Petersen, Meeko Oishi

We propose an open loop control scheme for linear time invariant systems perturbed by multivariate $t$ disturbances through the use of quantile reformulations. The multivariate $t$ disturbance is motivated by heavy tailed phenomena that arise in multi-vehicle planning planning problems through unmodeled perturbation forces, linearization effects, or faulty actuators. Our approach relies on convex quantile reformulations of the polytopic target sets and norm based collision avoidance constraints to enable fast computation. We embed quantile approximations of the Student's $t$ distribution and the beta prime distribution in a difference-of-convex function framework to compute provably safe but likely suboptimal controllers. We demonstrate our method with three satellite rendezvous examples and provide a comparison with particle control.

Kexue Zhang, Bahman Gharesifard, Elena Braverman

This article studies the event-triggered control problem of general nonlinear systems with time delay. A novel event-triggering scheme is presented with two tunable design parameters, based on a Lyapunov functional result for the input-to-state stability of time-delay systems. The proposed event-triggered control algorithm guarantees the resulting closed-loop systems to be globally asymptotically stable, uniformly bounded, and/or globally attractive for different choices of these parameters. Sufficient conditions on the parameters are derived to exclude Zeno behavior. Two illustrative examples are studied to demonstrate our theoretical results.

Hiroaki Kuzuhara, H. Takimoto, Yasuhiro Sato et al.

Detection of insect pests in the agricultural field, which is useful in achieving smart agriculture, has attracted considerable attention. In particular, automatically monitoring the number of crop insect pests has evolved into key means of managing and optimizing agricultural resources. However, despite the fact that conventional convolutional neural network (CNN)-based approaches have yielded sufficient results for general object detection, few methods have been developed to detect and recognize small objects such as insect pests. In addition, no large dataset exists for pest detection even though CNN-based methods require a large dataset to optimize many parameters. In this study, we propose two-stage detection and identification methods for small insect pests based on CNN. We also present a region proposal network for insect pest detection using YOLOv3 and propose a re-identification method using the Xception model. To train these models, we propose a data augmentation method using image processing.