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

Kameron Eves, John Valasek

Singularly perturbed systems are a class of mathematical systems that are not well approximated by their limits and can be used to model plants with multiple fast and slow states. Multiple-timescale systems are very common in engineering applications, but adaptive control can be sensitive to timescale effects. Recently a method called [K]control of Adaptive Multiple-timescale Systems (KAMS) has shown improved performance and increased robustness for singularly perturbed systems, but it has only been studied on systems using adaptive control for the slow states. This article extends KAMS to the general case when adaptive control is used to stabilize both the slow and fast states simultaneously. This causes complex interactions between the fast state reference model and the manifold to which the fast states converge. It is proven that under certain conditions the system still converges to the reference model despite these complex interactions. This method is demonstrated on a nonlinear, nonstandard, numerical example.

Milkias Berhanu Tuka, Seid Endris Ali

The increasing demand for electric power, coupled with rapid urbanization, necessitates a reliable and high-quality electricity supply to meet consumer expectations. However, existing passive distribution systems are inadequate to address the escalating power requirements, resulting in challenges such as increased power losses and suboptimal voltage profiles. In the base case scenario, the total active and reactive power losses were substantial, and many buses exhibited voltage magnitudes that fell outside acceptable limits. This study investigates the optimal placement and sizing of distributed generation (DG) resources to improve the performance of distribution feeders. A multi-objective optimization framework, utilizing a Genetic Algorithm (GA), was developed to minimize power losses and enhance voltage profiles. Load flow analysis was conducted using the Backward/Forward Sweep (BFS) method, allowing for precise evaluation of the distribution feeder under various DG configurations. Consequently, the study successfully enhanced the system through optimal DG allocation. Additionally, a comparative analysis was conducted to assess the performance of the proposed GA algorithm against other optimization techniques. The results indicate that, in nearly all cases, the GA method outperforms PSO by reducing system power losses and improving the voltage profile more effectively.

Junyi Wang, Ying Zheng, Jinliang Ding et al.

This article is concerned with the memory-based event-triggered leader-following dissipative fault-tolerant consensus (LFDFTC) problem for the nonlinear multi-agent systems (NMASs) with semi-Markov switching topologies subject to the generally uncertain semi-Markov (GUSM) jumping process. Unlike the existing event-triggered (ET) consensus results, the dynamic memory event-triggered mechanism (DMETM) and memory-based distributed fault-tolerant (FT) controllers are designed to reduce the ET times. By constructing a general mode-dependent Lyapunov-Krasovskii functional (LKF) and strictly $(\bf {\mathcal {R,Q,T}})-\boldsymbol {\gamma }$ dissipative analysis, the dissipative FT consensus conditions of NMASs are derived in this paper. Finally, three actual physical systems are utilized to verify the validity of the proposed method. Note to Practitioners—Owing to the complexity of engineering environment, the consensus control issue of NMASs has attracted widespread attention. Nowadays, the consensus control of NMASs is generally utilized in diverse fields, such as multi-vehicle coordination, smart grids, and unmanned aerial vehicle formation. However, for the electronic device in practical applications, the channel bandwidth is limited due to power and energy constraints, and it is difficult for the fixed communication topologies and traditional periodic sampled-data control method to cope with these unexpected situations. Therefore, the LFDFTC issue for the NMASs with GUSM switching topologies is investigated by adopting DMETM and memory-based distributed FT controllers in this paper. In addition, the proposed FT consensus control methods with prescribed dissipative performance are applied to multiple vehicles time-invariant formation, Chua’s circuits synchronization, and multiple manipulators consensus.

Zhijie Li, Xiaofei Wang, Hao Guo et al.

This paper presents an output feedback prescribed performance control method for a class of high-order nonlinear uncertain multiagent systems. General prescribed performance consensus control for multiagent systems requires that the initial consensus error is constraining within a boundary value. Unlike existing works, the conservative condition of prescribed performance consensus control for multiagent systems is relaxed as the consensus error is independent of the initial state. In this condition, we employ a control scheme with improved prescribed performance to constraint the transient behavior of the system. By designing reduce order dynamic gain K-filter, the state variables of the systems are reconstructed. Based on the baskstepping method and dynamic surface control technology, we introduce an innovative prescribed performance event-triggered approach aimed at ensuring prescribed performance levels for both transient and steady-state aspects of consensus control and reducing the communication bandwidth resource. Furthermore, we rigorously demonstrated through the Lyapunov function that all agents can achieve consensus with the leader driven by the controller. The simulation results have demonstrated the effective tracking performance of the developed control approach. Finally, the effectiveness and reliability of the proposed control strategy are verified through the successful execution of multi-QUAVs formation encirclement control.Note to Practitioners—This paper considers output feedback prescribed performance consensus control problem of multiagent systems, which can be applied to some practical systems, e.g., low-altitude formation flight of autonomous aerial vehicle with specified transient performance, multi underwater robot for oil field line-cruising, etc. Furthermore, in harsh working environments, the states of agent devices are difficult to measure or cannot be measured. Hence the control objective in these applications can be transformed into output feedback prescribed performance consensus control problem of multiagent systems. Besides, in military applications, high-speed aircraft need to fly at a certain height above the ground in order to avoid radar detection, which poses strong limitations on the position of the aircraft. This challenging issue can be well addressed through prescribed performance control. Compared with the existing results, this paper proposed a novel prescribed performance control strategy for multiagent systems with unrestricted initial state. It should be noted that in practical applications, communication bandwidth resources are extremely limited, so we propose event triggered control to effectively alleviate the communication pressure of multiagent system cooperative control. The nonlinear high-order system model studied in this article can be converted into a Lagrangian system, multi-complexity manipulator system and an autonomous aerial vehicle system, which has strong engineering practical significance.

Jin-Zi Yang, Jinxi Zhang, Tianyou Chai

This paper is concentrated on the problem of decentralized output-feedback control of interconnected strict-feedback systems with actuator failures. It is focused on the cases where the virtual control coefficients of the plant are unknown; the global boundedness, matching conditions or global Lipschitz conditions of the interconnections are not assumed; the control algorithm is as simple as possible. They render the existing decentralized output-feedback fault-tolerant control designs infeasible. To address the problem, a low-complexity decentralized robust prescribed performance control approach based on a linear state transformation and an input-driven filter is put forward in this paper. It achieves the system outputs to track the corresponding references with the preassigned speed and accuracy. It is also inherently robust against the unknown system dynamics, the actuator failures, and the disturbances, thus without parameter estimation, function approximation, derivative computation, command filtering, fault detection, fault isolation or fault estimation. Finally, a comparative simulation on two inverted pendulums linked by a spring is conducted to demonstrate the developed control design. Note to Practitioners—Many complex systems, such as power systems, aerospace systems, and chemical systems, can be modeled as interconnected systems. Moreover, due to the increasing scale and complexity of engineering systems, actuator failures are becoming more likely to occur during system operation. On the other hand, both the transient and steady-state tracking performance of the systems are required to be preassigned in practical scenarios, e.g., missile interception. Existing approaches to compensate for the actuator failures guarantee only the boundedness of the tracking error under nonparametric uncertainties in the system model. This paper presents a decentralized robust prescribed performance control approach. It is inherently robust to the system nonlinearities, the actuator failures, and the disturbances. It exhibits lower costs in computation, higher efficiency in design, and is more user-friendly in implementation. It achieves trajectory tracking with preassigned rate and accuracy, despite the actuator failures. Extension of the approach to multi-agent systems with actuator failures is an interesting topic for future investigations.

Jun Cheng, Tianfeng Tang, Huaicheng Yan et al.

This study focuses on the learning-based asynchronous sliding mode control for switching systems, operating under a general switching rule and partially unknown probability information. A novel switching rule is constructed, governed by a joint probability distribution dependent on the current mode and its duration time, thereby overcoming the limitations of traditional Markov/semi-Markov models in terms of the difficulty in obtaining transition probabilities and computational complexity. Departing from traditional geometric distribution assumption, the proposed method follows more general duration distribution. Acknowledging the challenge of obtaining complete probability information in practical scenarios, partially unknown probability information is considered. In addition, a learning-based asynchronous sliding mode control law is developed, aimed at guiding state signals onto preset sliding regions and effectively reducing chattering induced by mode switchings. Finally, the efficacy and superiority of the developed theories are verified through both numerical and practical examples. Note to Practitioners—With the rapid development of networked control technologies, designing switching rules for switching systems and suppressing chattering in sliding mode control remain key challenge. In practical engineering systems, such as power systems, robotic control, and networked systems, the duration of different modes is often influenced by external disturbances, equipment characteristics, and load variations, making it difficult to model simply as a geometric distribution. Meanwhile, traditional Markov/semi-Markov models rely on extensive statistical data, and their transition probabilities are challenging to obtain accurately in practice. To address these issues, a joint probability distribution function based on the current mode and its duration, is adopted to accurately reflect the duration characteristics of the system in different states while reducing computational burden. The duration distribution, which depends on the current mode, is characterized by a general distribution, making the model more applicable to complex engineering systems. Furthermore, in industrial applications, sliding mode control often suffers from chattering issues caused by system modes, asynchronous double switching, discontinuities in the sign function, and other factors. As considered in this paper, a novel learning-based asynchronous sliding mode control method, incorporating emission probability and iterative learning, is proposed to alleviate chattering in sliding mode control.

Jianjun Chen, Liyuan Zheng, Huilai Zou et al.

Precise segmentation of individual teeth from digital three-dimensional (3D) tooth models is critical in computer-assisted orthodontic surgery. This study explores the application of Point Multi-Layer Perceptron (PointMLP) in processing 3D tooth models and introduces an innovative integration of the Graph Attentional Convolution (GAC) Layer with a graph attention mechanism. By incorporating the GAC Layer into PointMLP, the model can focus on key local regions in the 3D tooth model and dynamically adjust the attention applied to these areas. This enhanced attention mechanism allows the model to better capture subtle surface structures, facilitating the accurate extraction of valuable local features. Compared to other traditional segmentation algorithms, the proposed method shows improvements of 1.1, 2.04, 1.06, 2.2, and 1.8 percentage points in Overall Accuracy (OA), Sensitivity (SEN), Positive Predictive Value (PPV), and Intersection Over Union (IoU), respectively. At the same number of training epochs, our method outperforms both GAC and PointMLP in segmentation performance.

Amr S. Mohamed, Emily Nguyen, Deepa Kundur

Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies.

Robin Strässer, Manuel Schaller, Julian Berberich et al.

We derive novel deterministic bounds on the approximation error of data-based bilinear surrogate models for unknown nonlinear systems. The surrogate models are constructed using kernel-based extended dynamic mode decomposition to approximate the Koopman operator in a reproducing kernel Hilbert space. Unlike previous methods that require restrictive assumptions on the invariance of the dictionary, our approach leverages kernel-based dictionaries that allow us to control the projection error via pointwise error bounds, overcoming a significant limitation of existing theoretical guarantees. The derived state- and input-dependent error bounds allow for direct integration into Koopman-based robust controller designs with closed-loop guarantees for the unknown nonlinear system. Numerical examples illustrate the effectiveness of the proposed framework.

G. Ghazi, Julien Voyer

This paper presents a hands-on pedagogical approach using a DJI Tello drone as an interactive teaching platform in the field of automatic control engineering. The DJI Tello is a small commercial quadcopter drone and includes a software development kit (SDK) that allows developers to control the Tello using various programming languages, including Python. The drone is also equipped with a large number of sensors that can be used in real-time to collect data and analyze how changes in control inputs such as thrust, pitch, roll, and yaw affect its flight path and stability. These features make the Tello a good teaching tool for demonstrating control concepts in an attractive and practical way. Two examples of pedagogical applications are presented in this paper. The first example aims to illustrate in practice how system identification can be used to create a mathematical model of the DJI Tello drone using transfer functions. The second example aims to illustrate how to design a Proportional-Integral (PI) controller and validate it after its implementation on the DJI Tello drone. Through these teaching demonstrations, it was possible to enhance cognitive learning while providing students with a better understanding of the fundamental concepts of modeling and control. It was also observed that even though the students had no background in aeronautics, using an atypical system such as a drone aroused their curiosity, encouraging them to participate, thus making the in-class demonstrations more dynamic.

FENG Jianghua, TAN Bo, DOU Zechun et al.

With the continuous advancement of China's "dual carbon" goals and the ongoing optimization of the energy mix, the electrification of transportation equipment, as a low-carbon and environmentally-friendly approach, has become an important development trend in the transportation industry. This paper presents the exploration in high-power-density electrification technologies for transportation equipment, focusing on those in the chain-type key technology routes encompassing devices, components, equipment, systems and architectures. Taking products supplied by CRRC Zhuzhou Institute Co., Ltd. as an case study, detailed investigations were made into five key technologies for high-power-density design: high-frequency converters, customized devices, silicon-based equipment, structural integration, and diversified networking, and three high-power-density generic technologies: thermal management, electromagnetic compatibility, and reliability, highlighting their key roles in improving the performance, efficiency, and reliability of transportation equipment. The study summarizes the current research status concerning the development of transportation equipment towards higher power, lighter weight, and smaller size. For future development in high-power-density electrification technologies, this paper suggests a focus on continuous innovation and development in four areas: the innovation chain, intelligent systems, new power semiconductor device technologies, and safety. The research outcomes provide strong support for the green transformation and sustainable development of the transportation industry.

ZHONG Hanwen, XIAO Lei, CHEN Wenguang et al.

Long wheelbase design of vehicle can effectively increase the standing area without increasing the body length, thus increasing the passenger capacity. Today, with the development of urbanization, the long wheelbase vehicle design has become a trend, but this poses new challenges to the low-speed trafficability and high-speed stability of vehicles. This paper takes the long wheelbase commercial vehicle as the research object. Based on the vehicle dynamics and suspension design theory, the author first designed key parameters of long wheelbase vehicle and built an 18 degrees of freedom (DOF) dynamics simulation model for the vehicle; then designed the four-wheel steering (4WS) control algorithm according to the design parameters of the vehicle, for achieving the control target that the side slip angle tends to zero; and finally researched the influence of front-wheel steering (FWS) control and four-wheel steering control on the dynamic performance of the vehicle under the steady-state circumferential conditions with different turning radiuses and steering wheel angle pulse conditions with different speeds. The simulation results show that, under the steady-state circumferential turning condition with a low-speed turning radius of R15, the four-wheel steering design reduces the passing space from 4.6 m to 3.9 m, effectively improving the tracking ability of the front and rear axles of the vehicle and enhancing the trafficability and safety of the vehicle, and under the pulse condition with a maximum speed of 100 km/h, reduces the peak lateral acceleration from 4 m/s2 to 1.5 m/s2 and the peak yaw rate from 11°/s to 3°/s. Therefore, under high-speed steering wheel angle pulse conditions, the four-wheel steering design can effectively reduce the dynamic indicators of the vehicle, such as side slip angle, lateral acceleration and yaw rate, and improve the safety, stability and comfort of the vehicle at high speed.

CHEN Ao, LI Chen, YAN Jiayun et al.

Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data formats, analytical complexities, and high labour costs, hindering the achievement of data standardization. Large language models (LLM), featuring powerful performance in natural language processing comprehension and handling complex tasks, have made great academic and industrial progress in recent years. This study initially investigated the application performance of LLMs in information extraction from locomotive overhaul data, with the following three reveals, as the universal information extraction (UIE) LLM is suitable for information extraction in the field of locomotive overhaul; expanding the size of locomotive data helps improve the UIE performance in information extraction from locomotive overhaul data; balancing the types of fault labels does not notably help improve this performance. Subsequent explorations concentrated on difficulties in data annotation. The script writing method was utilized for automated annotation of data, and ChatGLM was leveraged to standardize locomotive overhaul data, yielding Bleu-4, Rouge-1, Rouge-2, and Rouge-L metrics of 86.87%, 89.60%, 87.54%, and 94.26%, respectively, in alignment with the requirements of engineering applications. Further developments introduced an auxiliary data standardization pre-processing tool to streamline the standardization process by encapsulating the LLM.

Max H. Cohen, Ryan K. Cosner, Aaron D. Ames

Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a CBF can be constructed. This paper draws connections between partial feedback linearization and CBF synthesis. We illustrate that when a control affine system is input-output linearizable with respect to a smooth output function, then, under mild regularity conditions, one may extend any safety constraint defined on the output to a CBF for the full-order dynamics. These more general results are specialized to robotic systems where the conditions required to synthesize CBFs simplify. The CBFs constructed from our approach are applied and verified in simulation and hardware experiments on a quadrotor.

Devansh R. Agrawal, Hardik Parwana, Ryan K. Cosner et al.

We present a multi-rate control architecture that leverages fundamental properties of differential flatness to synthesize controllers for safety-critical nonlinear dynamical systems. We propose a two-layer architecture, where the high-level generates reference trajectories using a linear Model Predictive Controller, and the low-level tracks this reference using a feedback controller. The novelty lies in how we couple these layers, to achieve formal guarantees on recursive feasibility of the MPC problem, and safety of the nonlinear system. Furthermore, using differential flatness, we provide a constructive means to synthesize the multi-rate controller, thereby removing the need to search for suitable Lyapunov or barrier functions, or to approximately linearize/discretize nonlinear dynamics. We show the synthesized controller is a convex optimization problem, making it amenable to real-time implementations. The method is demonstrated experimentally on a ground rover and a quadruped robotic system.

Johannes Köhler, Melanie N. Zeilinger

We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shrinking-horizon framework that utilizes general probabilistic reachable sets and minimizes the expected cost. Then, we provide a tractable receding-horizon formulation that uses a nominal state to minimize a deterministic quadratic cost and satisfy tightened constraints. Our theoretical analysis demonstrates recursive feasibility, satisfaction of chance constraints, and bounds on the expected cost for the resulting closed-loop system. We provide a constructive design for probabilistic reachable sets of nonlinear continuously differentiable systems using stochastic contraction metrics and an assumed bound on the covariance matrices. Numerical simulations highlight the computational efficiency and theoretical guarantees of the proposed method. Overall, this paper provides a framework for computationally tractable stochastic predictive control with closed-loop guarantees for nonlinear systems with unbounded noise.

Kaijun Luo, Xun Qiao

—Automatic spraying system is mainly used for automatic spraying operation in the field of industrial manufacturing, painting and surface coating. This system realizes the automatic application of paint, paint or other surface coating materials through the use of mechanical devices and control systems. Its main structure can be divided into: painting machinery, paint supply system, control system. These components work together to achieve an efficient, consistent and safe painting process. Different types of automatic spraying systems may have different structures and components, depending on their specific applications and requirements. With the improvement of environmental protection and quality requirements, the automatic spraying system is also put forward higher and higher requirements. In view of the low efficiency of production and the great harm of manual spraying to human body, a general automatic spraying system is designed in this paper. the device has five degrees of freedom, flexible operation and two degrees of freedom control of the wrist. The advantage of directly improving the quality and effect of spray painting can significantly improve production efficiency and reduce labor costs.

Yerong Sun, Kechuan Yi

So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.

R. Sacile, M. Sallak, E. Zero

In Industry 4.0 context predictive maintenance is a hot topic with several challenges that can be investigated by a system of systems engineering approach where the emergent behaviour can be related to possible system failures. The Internet of Things (IoT) can be also used to monitor and control the different system components. This work proposes a simple approach to detect, monitor, and control emergent behavior by IoT sensors. Starting from the raw data which were daily extracted from sensors, an analysis of the collected signals is performed to help decision-making concerning its maintenance. In the case study related to a door system in a bus, the proposed method can recognize the different damage states analysing the input parameters: temperature, pressure, and humidity of door systems. Some recent reliability performance indicators are used to evaluate the damage classification.

MA Jing, CAO Du, MA Lili

Aero-engine´s characteristics vary with flight conditions and operating states. In complex operating environments, both model uncertainty and controller parameter variation exist simultaneously, which greatly affect the control performance in the whole flight envelope. Therefore, a robust elastic adaptive control method based on parameter perturbation model is proposed in this paper. The structural model of aero-engine parameter perturbation is established. Then, aiming at the uncertainty of the controlled object model and the perturbation of controller gain, the robust resilient adaptive control law is designed when the gain perturbation is bounded but the upper bound is unknown by using Lyapunov stability theory and linear matrix inequality constraints, and the controller design problem is transformed into a feasible solution problem of linear matrix inequalities. The controller design only depends on the existence of the solution matrix of linear matrix inequalities, and the stability of the algorithm is proved. On this basis, the control simulation of different operating states of the engine in the flight envelope is carried out. The simulation results show that the adjustment time is less than 1.8 s and the overshoot is less than 5%, which indicates good stability and control performance of the designed controller.