Research

16 pages, 2614 KiB

Open AccessArticle

A Nonlinear Subspace Predictive Control Approach Based on Locally Weighted Projection Regression

by Xinwei Wu and Xuebo Yang

Electronics 2024, 13(9), 1670; https://doi.org/10.3390/electronics13091670 - 26 Apr 2024

Viewed by 223

Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on [...] Read more.

Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on locally weighted projection regression (NSPC-LWPR). By projecting the input space into localized regions, constructing precise local models, and aggregating them through weighted summation, this approach handles the nonlinearity effectively. Additionally, it dynamically adjusts the control strategy based on online process data and model parameters, while eliminating the need for offline process data storage, greatly enhancing the adaptability and efficiency of the approach. The parameter determination criteria and theoretical analysis encompassing feasibility and stability assessments provide a robust foundation for the proposed approach. To illustrate its efficacy and feasibility, the proposed approach is applied to a continuous stirred tank heater (CSTH) benchmark system. Comparative results highlight its superiority over SPC and adaptive subspace predictive control (ASPC) methods, evident in enhanced tracking precision and predictive accuracy. Overall, the proposed NSPC-LWPR approach presents a promising solution for nonlinear control challenges in industrial process systems. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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18 pages, 8945 KiB

Open AccessFeature PaperArticle

Adaptive Dynamic Boundary Sliding Mode Control for Robotic Manipulators under Varying Disturbances

by Zhendong Song, Danyang Bao, Wenbin Wang and Wei Zhao

Electronics 2024, 13(5), 900; https://doi.org/10.3390/electronics13050900 - 27 Feb 2024

Viewed by 480

Abstract

This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and [...] Read more.

This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and fast-terminal-type convergence laws to address errors and parameter uncertainties inherent in the nonlinear system models. The adaptive law is designed to cover all boundary conditions based on the model’s state. It can dynamically determine upper and lower bounds without requiring prior knowledge. Consequently, the ADBSMC control method amalgamates the benefits of adaptive law and fast terminal sliding mode, leading to significant enhancements in control performance compared with traditional sliding mode control (SMC), exhibiting robustness against uncertain disturbances. To mitigate external disturbances, a system-adapted disturbance observer is devised, facilitating real-time monitoring and compensation for system disturbances. The stability of ADBSMC is demonstrated through the Lyapunov method. Simulation and experimental results validate the effectiveness and superiority of the ADBSMC control scheme, showcasing its potential for practical applications. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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21 pages, 5731 KiB

Open AccessArticle

Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System

by Fubing Li, Linhao Yang, Ao Ye, Zongmin Zhao and Bingxia Shen

Electronics 2024, 13(4), 697; https://doi.org/10.3390/electronics13040697 - 08 Feb 2024

Viewed by 523

Abstract

The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control [...] Read more.

The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control in the TVT. To solve this problem, a multi-channel Smith proportional–integral–derivative (PID) controller based on a grouping neural network (Grouping-NN) is proposed. Firstly, the mathematical derivation for a typical multi-channel temperature control model of the TVT is carried out. Then, the multi-channel interaction system is identified using a Grouping-NN to predict the output temperature of each channel by grouping the hidden layer neurons according to the number of channels. Finally, two Grouping-NNs are utilized to update the Smith predictor, and the time-delay error is fed back to the PID controller, which is used to optimize the control effect of the multi-channel interaction system under high time delay. The proposal is compared with the traditional PID controller and Smith predictor-based PID controller through simulation. The simulation results show that the proposed method has better suppression of overshooting. In addition, the algorithm is verified by controlling the temperature of six channels in a practical thermal vacuum test. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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18 pages, 4222 KiB

Open AccessArticle

The Prescribed-Time Sliding Mode Control for Underactuated Bridge Crane

by Yin’an Feng, Hao Zhang and Chan Gu

Electronics 2024, 13(1), 219; https://doi.org/10.3390/electronics13010219 - 03 Jan 2024

Viewed by 655

Abstract

In this article, a prescribed-time sliding mode controller is proposed for the design of the positioning and anti-swing time of the underactuated bridge crane under different initial conditions. Compared with the existing crane positioning and anti-swing controller, the controller can directly specify the [...] Read more.

In this article, a prescribed-time sliding mode controller is proposed for the design of the positioning and anti-swing time of the underactuated bridge crane under different initial conditions. Compared with the existing crane positioning and anti-swing controller, the controller can directly specify the positioning and anti-swing time of the bridge crane system through the controller parameters. Firstly, in order to solve the underdrive problem of the bridge crane system, the crane system model is transformed by constructing composite variables; secondly, a new prescribed-time convergence rate and a new prescribed-time sliding mode surface are designed to ensure that the state of the bridge crane system can converge within the prescribed time; finally, the Lyapunov stability analysis and simulation results show that the designed controller can enable the crane to position and anti-swing within the prescribed time. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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15 pages, 3447 KiB

Open AccessArticle

Real-Time Telemetry-Based Recognition and Prediction of Satellite State Using TS-GCN Network

by Shuo Liu, Shi Qiu, Huayi Li and Ming Liu

Electronics 2023, 12(23), 4824; https://doi.org/10.3390/electronics12234824 - 29 Nov 2023

Cited by 2 | Viewed by 782

Abstract

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers [...] Read more.

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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17 pages, 374 KiB

Open AccessArticle

Distributed Adaptive Consensus Output Tracking Problem of Nonlinear Multi-Agent Systems with Unknown High-Frequency Gain Signs under Directed Graphs

by Jingyu Chen and Zhengtao Ding

Electronics 2023, 12(8), 1830; https://doi.org/10.3390/electronics12081830 - 12 Apr 2023

Cited by 1 | Viewed by 868

Abstract

This paper deals with the consensus output tracking problem for multi-agent systems with unknown high-frequency gain signs, in which the subsystems are connected over directed graphs. The subsystems may have different dynamics, as long as the relative degrees are the same. A new [...] Read more.

This paper deals with the consensus output tracking problem for multi-agent systems with unknown high-frequency gain signs, in which the subsystems are connected over directed graphs. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is first presented to tackle adaptive consensus control of network-connected systems without the knowledge of the high-frequency gains. Adaptive laws and internal models are then proposed to handle the uncertainties and unknown parameters. An integral Lyapunov function based on sufficient conditions is finally introduced to tackle the asymmetry of the Laplacian matrix of directed graphs, into which we incorporate the new Nussbaum gain and the adaptive internal model to design the controller. It is apparent that the control scheme and the adaptive laws are fully distributed, which means that only the relative information of the neighbourhood subsystems’ outputs is used, and the simulation results validate the effectiveness of the control design, whereby they guarantee the asymptotic convergence of errors to zero as well as the boundedness of the state variables. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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14 pages, 1509 KiB

Open AccessArticle

A Novel Heterogeneous Parallel System Architecture Based EtherCAT Hard Real-Time Master in High Performance Control System

by Hongzhe Shi, Weiyang Lin, Chenlu Liu and Jinyong Yu

Electronics 2022, 11(19), 3124; https://doi.org/10.3390/electronics11193124 - 29 Sep 2022

Cited by 3 | Viewed by 1772

Abstract

EtherCAT is one of the preferred real-time Ethernet technologies. However, EtherCAT is not applicable in high-end control fields due to real-time constraints. Clock synchronization and cycle time are the most representative limitations. In this paper, a novel Heterogeneous Parallel System Architecture (HPSA [...] Read more.

EtherCAT is one of the preferred real-time Ethernet technologies. However, EtherCAT is not applicable in high-end control fields due to real-time constraints. Clock synchronization and cycle time are the most representative limitations. In this paper, a novel Heterogeneous Parallel System Architecture (HPSA) with features of parallel computation and hard real-time is presented. An HPSA-based EtherCAT hard real-time master is developed to significantly improve clock synchronization and shorten cycle time. Traditional EtherCAT masters feature serial processing and run on a PC. This HPSA-based master consists of two parts: EtherCAT master stack (EMS) and EtherCAT operating system (EOS). EMS implements the parallel operation of EtherCAT to realize the shorter cycle time, and EOS brings a hard real-time environment to the HPSA-based master to improve clock synchronization. Furthermore, this HPSA-based master operates on a heterogeneous System-on-a-chip (SoC). EMS and EOS form a heterogeneous architecture inside this SoC to achieve low-latency process scheduling. Experimental results show that in our HPSA-based EtherCAT hard real-time master, the cycle time reaches the sub-50

μ s

range, and the synchronization error reduces to several nanoseconds. Thus, this HPSA-based master has great application value in high-performance control systems. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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18 pages, 5315 KiB

Open AccessFeature PaperArticle

Sliding Mode Observer-Based Stuck Fault and Partial Loss-of-Effectiveness (PLOE) Fault Detection of Hypersonic Flight Vehicle

by Changhua Hu, Meijie Liu, Hongzeng Li and Xiaoxiang Hu

Electronics 2022, 11(19), 3059; https://doi.org/10.3390/electronics11193059 - 26 Sep 2022

Cited by 4 | Viewed by 1248

Abstract

In order to improve the safety and reliability of the hypersonic flight vehicle, a sliding mode observer-based fault detection scheme is applied in this paper to handle the actuator fault detection issue, including stuck fault detection and PLOE fault detection. A dynamic linear [...] Read more.

In order to improve the safety and reliability of the hypersonic flight vehicle, a sliding mode observer-based fault detection scheme is applied in this paper to handle the actuator fault detection issue, including stuck fault detection and PLOE fault detection. A dynamic linear model with uncertainty is first derived from the original nonlinear hypersonic flight vehicle model by using Taylor’s linearization approach at the equilibrium point. Secondly, the actuator fault model, reflecting stuck faults and PLOE faults, is constructed. Then, a sliding mode-based fault detection observer, considering system decomposition, is developed based on the linearized hypersonic flight vehicle model. At last, the designed sliding mode observer is applied to the original nonlinear hypersonic flight vehicle for single-input, single-style actuator fault detection. The simulation results show that stuck faults and big proportion PLOE faults can be timely and accurately detected at the fault time, and the stuck actuator fault from input 3 can cause a deadly impact to the hypersonic flight vehicle, which deserves much more attention than the actuator faults from the other three inputs. Meanwhile, the detection of a small proportion of PLOE faults encounters some difficulties and needs special attention and further investigation. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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14 pages, 6286 KiB

Open AccessArticle

Hysteresis Modeling of Piezoelectric Actuators Based on a T-S Fuzzy Model

by Liu Yang, Qingtao Wang, Yongqiang Xiao and Zhan Li

Electronics 2022, 11(17), 2786; https://doi.org/10.3390/electronics11172786 - 04 Sep 2022

Cited by 1 | Viewed by 1305

Abstract

Piezoelectric actuators (PEAs) have been widely used in aerospace, electronic communication and other high-accuracy manufacturing fields because of their high precision, low power consumption, fast response, and high resolution. However, piezoelectric actuators have very complicated hysteresis nonlinearity, which greatly affects their positioning and [...] Read more.

Piezoelectric actuators (PEAs) have been widely used in aerospace, electronic communication and other high-accuracy manufacturing fields because of their high precision, low power consumption, fast response, and high resolution. However, piezoelectric actuators have very complicated hysteresis nonlinearity, which greatly affects their positioning and control accuracy. Particularly in the field of active vibration control, the control accuracy of piezoelectric actuators is easily affected by noise points. To address the problem, this paper proposes a hyperplane probability c-regression model (HPCRM) algorithm to establish its T-S fuzzy model of hysteresis nonlinearity. Firstly, an improved fuzzy c regression clustering algorithm is proposed to identify the antecedent parameters of T-S fuzzy model. This algorithm not only divides the fuzzy space better but also effectively avoids the influence of noise points generated by the external environment during data acquisition. Secondly, a new type of hyperplane membership function is introduced to solve the problem that the traditional Gaussian membership function does not match the hyperplane clustering algorithm. Finally, the accuracy of the modeling method is confirmed by several comparative experiments. Experimental results show that the proposed method is more precise than the traditional fuzzy c-regression models (FCRM) and probability c-regression models (PCRM) under the sine signals of 5 Hz–100 Hz. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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23 pages, 3772 KiB

Open AccessArticle

Neural Network-Based Dual-Cylinder Synchronous Control of a Multi-Link Erection Mechanism

by Weilin Zhu, Yaowen Ge, Wenxiang Deng, Lan Li, Xiangxin Liu, Jialin Zhang and Jianyong Yao

Electronics 2022, 11(16), 2542; https://doi.org/10.3390/electronics11162542 - 14 Aug 2022

Viewed by 1209

Abstract

A dual-cylinder erection mechanism, in which two telescopic cylinders physically connect to a load, is a nonlinear system with model uncertainties and coupled dynamics. In this paper, a novel synchronous control algorithm with thrust-allocation law is proposed for eliminating the excessive internal forces [...] Read more.

A dual-cylinder erection mechanism, in which two telescopic cylinders physically connect to a load, is a nonlinear system with model uncertainties and coupled dynamics. In this paper, a novel synchronous control algorithm with thrust-allocation law is proposed for eliminating the excessive internal forces caused by the unbalanced rotation and lateral moments during the erection process. With regulated internal forces, the “pull and drag” issue is attenuated and better synchronization performance is attained. For improved tracking accuracy, the inter-stage collision dynamics of the telescopic cylinder are considered for model compensation to enhance stage-changing and in-position performance. A radial basis function (RBF) neural network is utilized to estimate the model uncertainties and external disturbances, which alleviates reliance upon the accuracy of a system model for controller implementation. As a result, theoretical analysis revealed that the semi-global asymptotic stability and synchronized motion performance with decreased internal forces can be achieved via the presented synchronous controller with thrust-allocation strategy. Contrasting simulations were implemented on a multi-link erection mechanism and the results confirmed the superiority and effectiveness of the proposed synchronous control algorithm. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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18 pages, 2251 KiB

Open AccessFeature PaperArticle

Real-Time ISR-YOLOv4 Based Small Object Detection for Safe Shop Floor in Smart Factories

by Byungjin Ku, Kangsan Kim and Jongpil Jeong

Electronics 2022, 11(15), 2348; https://doi.org/10.3390/electronics11152348 - 27 Jul 2022

Cited by 15 | Viewed by 3284

Abstract

Wearing a hard hat can effectively improve the safety of workers on a construction site. However, workers often take off their helmets because they have a weak sense of safety and are uncomfortable, and this action poses a large danger. Workers not wearing [...] Read more.

Wearing a hard hat can effectively improve the safety of workers on a construction site. However, workers often take off their helmets because they have a weak sense of safety and are uncomfortable, and this action poses a large danger. Workers not wearing hard hats are more likely to be injured in accidents such as human falls and vertical falls. Therefore, the detection of wearing a helmet is an important step in the safety management of a construction site, and it is urgent to detect helmets quickly and accurately. However, the existing manual monitor is labor intensive, and it is difficult to popularize the method of mounting the sensor on the helmet. Thus, in this paper, we propose an AI method to detect the wearing of a helmet with satisfactory accuracy with a high detection rate. Our method selects based on YOLOv4 and adds an image super resolution (ISR) module at the end of the input. Afterward, the image resolution is increased, and the noise in the image is removed. Then, dense blocks are used to replace residual blocks in the backbone network using the CSPDarknet53 framework to reduce unnecessary computation and reduce the number of network structure parameters. The neck then uses a combination of SPPnet and PANnet to take full advantage of the small target’s capabilities in the image. We add foreground and background balance loss functions to the YOLOv4 loss function part to solve the image background and foreground imbalance problem. Experiments performed using self-constructed datasets show that the proposed method has more efficacy than the currently available small target detection methods. Finally, our model achieves an average precision of 93.3%, a 7.8% increase over the original algorithm, and it takes only 3.0 ms to detect an image at 416 × 416. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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19 pages, 4391 KiB

Open AccessArticle

Active Disturbance Rejection Adaptive Control for Hydraulic Lifting Systems with Valve Dead-Zone

by Fengbo Yang, Hongping Zhou and Wenxiang Deng

Electronics 2022, 11(11), 1788; https://doi.org/10.3390/electronics11111788 - 05 Jun 2022

Cited by 4 | Viewed by 1493

Abstract

In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including [...] Read more.

In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including both mechanical dynamics and hydraulic actuator dynamics with a valve dead-zone of the hydraulic lifting system, were modeled. Then, by adopting the system model and a backstepping technique, a composite parameter adaptation law and extended state disturbance observer were successfully combined, which were employed to dispose of the parametric uncertainties and unmodeled disturbances, respectively. This much decreased the learning burden of the extended state disturbance observer, and the high-gain feedback issue could be shunned. An ultimately bounded tracking performance can be assured with the developed control method based on the Lyapunov theory. A simulation example of a hydraulic lifting system was carried out to demonstrate the validity of the proposed controller. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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18 pages, 2809 KiB

Open AccessArticle

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

by Changan Li, Sirui Wu, Zhan Li, Yuxiao Zhang, Lijie Zhang and Luis Gomes

Electronics 2022, 11(9), 1390; https://doi.org/10.3390/electronics11091390 - 27 Apr 2022

Cited by 4 | Viewed by 2044

Abstract

Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process [...] Read more.

Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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14 pages, 2244 KiB

Open AccessArticle

Modeling and Extended State Observer-Based Backstepping Control of Underwater Electro Hydrostatic Actuator with Pressure Compensator and External Load

by Yong Nie, Jiajia Liu, Zhenhua Lao and Zheng Chen

Electronics 2022, 11(8), 1286; https://doi.org/10.3390/electronics11081286 - 18 Apr 2022

Cited by 6 | Viewed by 2159

Abstract

Electro hydrostatic actuator (EHA) has been successfully developed for flight control applications to replace the cumbersome centralized hydraulic system. It also has excellent potential for ocean applications due to its advantages on miniaturization and energy-savings. One of the special technologies for EHA’s underwater [...] Read more.

Electro hydrostatic actuator (EHA) has been successfully developed for flight control applications to replace the cumbersome centralized hydraulic system. It also has excellent potential for ocean applications due to its advantages on miniaturization and energy-savings. One of the special technologies for EHA’s underwater application is pressure compensation, which is used to equalize the return pressure of the hydraulic system and the seawater pressure. This paper investigates the modeling and control design of underwater EHA to improve performance, especially considering the effect of additional pressure compensator and uncertain external load. The nonlinear hydraulic model is extended by the dynamic characteristics of the pressure compensator. Two low-order extended state observers were constructed to cope with the external load fore and the effect of the pressure compensator, respectively. The backstepping methods were designed to guarantee the robust stability of the entire high-order nonlinear hydraulic system. Finally, the theoretical proving and simulation on Matlab/Simulink are conducted to demonstrate the high tracking performance of the proposed control strategy. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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