Network-based Control System Research Articles

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

A neural network-based control system is offered, which ensures high quality blended braking of the green energy vehicles in both the intensive and the gradual deceleration scenarios, with energy recovery at the changing road pavement. In this study, a neural network controller provides the torque gradient control without a tire model resulting in returning maximal energy to the hybrid energy source during the braking process. To meet the conflicting requirements of different braking modes and road surfaces, an allocation algorithm determines how to distribute the driver’s torque request between the friction and electrical brakes. Simulation demonstrates effectiveness of the proposed braking system. Model states and inputs are used as a guidance to learn a coupled two-layer neural network capable to capture various dynamic behaviours that could not be included in the simplified physics-based model. An experimental part of the research proves the model and simulation validity.

Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

Neuroadaptive tracking control of uncertain nonlinear systems with spatiotemporal constraints

This study investigates the neuroadaptive tracking control problem for a class of strict-feedback nonlinear systems with spatiotemporal constraints. An adaptive neural network-based control system is developed to alleviate the effects of modeling uncertainties and external disturbances. In particular, the proposed method ensures that the system tracking error has a predefined performance boundary (spatial constraint). Moreover, using a novel time-scale transformation method, uncertain nonlinear systems can achieve a prescribed finite-time convergence to a time-varying scaling function in the pointing position (temporal constraint). Finally, the efficiency of the proposed method is verified with two simulation examples.

Sailboat navigation control system based on spiking neural networks

In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. We demonstrate improved processing and laser control performance.

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Modeling and evaluation of cyber-attacks on grid-interactive efficient buildings

Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.

Design of Refueling Control System with Multi-ControlTerminal Based on Network

This paper discusses a network-based refueling control system with multiple control terminals that achieves the control input and display of near-aircraft position, refueling station, and centralized console. The system can achieve the closed-loop control of the refueling input flow, automatic control of the fixed refueling amount, and collection of environmental parameters, such as pipeline pressure, according to the refueling aircraft model. Moreover, the system can dynamically display parameter information of the refueling process in real time on three terminals. To meet the requirements of system functions and the real-time performance of tasks, this study selected the LPC2378 controller with the ARM core. The controller uses the µC/OS-II embedded real-time operating system as the software operating platform. To achieve the special requirements of the explosion-proof system, the explosion-proof 485 bus was employed to communicate with the display control unit (DCU) of the intrinsically safe contact aircraft stand and refueling station. The communication adopts the dual-redundant Ethernet communication and full-duplex 485 communication. To avoid the influence of external equipment on the control circuit, the power isolation method was adopted to ensure the stability of the system. After comprehensive debugging of hardware and software, we deem that the system has good performance and can be applied in engineering.

Оптимизация тихоходного синхронного генератора модульного типа и принцип реализации системы управления напряжением генератора на основе нейронной сети

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

무인 경비 로봇을 위한 다중 무선 통신 기반 관제시스템

무인 경비 로봇을 위한 다중 무선 통신 기반 관제시스템

Network-based reference model tracking for vehicle turning with event-triggered transmission and interval communication delays

This paper deals with the network-based modelling and reference model tracking control of four-wheel-independent-drive electric vehicles for turning purpose. First, a reference model is designed by adjusting its input to characterise the expected neutral turning of the vehicle. By considering event-triggered transmission and interval communication delays, the network-based tracking control system attaining vehicle turning is modelled as an interval input delay system, where the input delay is bounded by the bounds of communication delays and the sampling period. Second, a new delay-dependent bounded real lemma with less conservatism is derived by proposing a matrix-based binary quadratic convex method, constructing a discontinuous augmented Lyapunov–Krasovskii functional tailored based on second-order Bessel–Legendre inequality, and exploiting the relation among the upper bounds of input delay and interval communication delays, and the sampling period. Third, the network-based tracking controller design result is further established by linear matrix inequalities. Finally, an example is provided to confirm the superiority of the analysis results and the effectiveness of control design.

Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment

This study is devoted to development of a neural network based control system of robots group. The control system performs estimation of an environment state, searching the optimal path planning method, path planning, and changing the trajectories on via the robots interaction. The deep learning neural networks implements the optimal path planning method, and path planning of the robots. The first neural network classifies the environment into two types. For the first type a method of the shortest path planning is used. For the second type a method of the most safety path planning is used. Estimation of the path planning algorithm is based on the multi-objective criteria. The criterion includes the time of movement to the target point, path length, and minimal distance from the robot to obstacles. A new hybrid learning algorithm of the neural network is proposed. The algorithm includes elements of both a supervised learning as well as an unsupervised learning. The second neural network plans the shortest path. The third neural network plans the most safety path. To train the second and third networks a supervised algorithm is developed. The second and third networks do not plan a whole path of the robot. The outputs of these neural networks are the direction of the robot’s movement in the step k. Thus the recalculation of the whole path of the robot is not performed every step in a dynamical environment. Likewise in this paper algorithm of the robots formation for unmapped obstructed environment is developed. The results of simulation and experiments are presented.

Performance of synchronized master-slave closed-loop control of AC electric drives using real time motion over ethernet (RTMoE)

Multi-motor driving servomechanisms are widely used in modern industry for high-performance applications involving synchronized control of position, speed, acceleration and deceleration. Several synchronous control schemes have been developed to achieve good speed or position tracking regardless of various uncertainties and load torque perturbations. However, these solutions have some disadvantages, namely the costs associated with the necessary interfaces to transmit hardware synchronization signals between multiple drives and difficulty to integrate other tools and communication networks. Recently, Industrial Ethernet network-based control systems are becoming increasingly popular for industrial applications and factory automation. To overcome some of these disadvantages this paper proposes an approach that uses a real-time Ethernet applied to a dual-motor master-slave synchronization system, without introducing significant degradation of speed or position in the application performance. With this solution the position reference from the master encoder is transmitted by the real-time Ethernet instead of the classic hardware interface. This allows the integration of different tools and the explore of other functionalities of electric drives. The performance of the proposed solution was tested using industrial equipment through several practical experiments. It was also compared with an existing solution based on hardware interfaces, to identify advantages, limitations and cost savings of using real-time Ethernet networks solutions in synchronized AC electric drives.

An Adaptive Fuzzy-PI Clock Servo Based on IEEE 1588 for Improving Time Synchronization Over Ethernet Networks

A network-based control system includes many connected devices, and time synchronization plays an important role in the unified operation of those systems. A clock servo needs a guarantee time offset and a clock rate to minimize the time difference between devices. This paper proposes an adaptive clock servo to improve time synchronization. This proposed algorithm uses a fuzzy-based proportional-integral clock servo (fuzzy-PI) based on IEEE 1588 to reduce the frequency of clock compensation. This method adapts the bandwidth and enhances noise reduction, improving both the time offset and rate difference between the slave and master. The node time can synchronize with the master time after just one cycle of synchronization. Experiments validated the effectiveness of the algorithm and demonstrated that the slave can track the master with the mean and standard deviation of the time offset are 0.432ns and 4.402ns. A cycle time of one second is used to ensure a low-bandwidth network. With these results, the number of nodes over a real-time network can be increased.

Stability Analysis of Load Frequency Control System with Constant Communication Delays

This paper deals with the stability analysis of network-controlled load frequency control system (LFC) in the presence of time-delays. Network-based control system requires transmitting measured data from the plant site to the controller and control signals from the controller to the plant site. Use of an open communication channel for transmitting data involves a delay in the control loop. Such a delay would affect the LFC system stability. Lyapunov-Krasovskii functional (LKF) analysis and linear matrix inequalities (LMIs) techniques are used to investigate the stability of LFC system with proportional-integral (PI) controller. A less conservative delay-dependent stability criterion is derived using a discrete delay N-decomposition approach to determine the delay margin for the LFC system. This investigation is done to find a relationship between the delay margin of the LFC system and gain of the PI controller. The effectiveness of the obtained results is verified using simulation studies.

Finite-time stabilisation of discrete networked cascade control systems under transmission delay and packet dropout via static output feedback control

ABSTRACTDelay and packet dropout are two inherent sources of the performance degradation or instability in the networked control systems. This paper addresses the stochastic finite time stability analysis of a class of discrete-time network-based cascade control systems against varying delay and random packet dropout modelled by the Bernoulli distributed sequence. Based on the Finsler lemma, sufficient conditions are derived to stabilise such systems by designing static output feedback controller with the help of linear matrix inequalities (LMIs). This LMI-based conditions are provided to ensure the stochastic finite-time stability with a given disturbance attenuation level. Efficiency of the proposed controller is verified by numerical examples.

Robust output regulation and model following of uncertain networked systems

This paper proposes a robust output feedback-based controller for a class of uncertain linear network-based control systems. This method ensures the stability, robustness and an output regulation against the modelling uncertainties and communication network negative effects. In this way, a switching sliding surface is proposed and designed using a switched quadratic Lyapunov functional, by solving a set of linear matrix inequalities. A method is then proposed to reach the quasi-sliding mode and stay thereafter. Simulation studies on four benchmark problems show the effectiveness of the proposed method with respect to the model uncertainties and also band-limited communication network deficiencies.

Sensor-based Traffic Control Network with Neural Network Based Control System

Sensor-based Traffic Control Network with Neural Network Based Control System

The neural network-based control system of direct current motor driver

<span lang="EN-US">This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.</span>

A recurrent emotional CMAC neural network controller for vision-based mobile robots

Vision-based mobile robots often suffer from the difficulties of high nonlinear dynamics and precise positioning requirements, which leads to the development demand of more powerful nonlinear approximation in controlling and monitoring of mobile robots. This paper proposes a recurrent emotional cerebellar model articulation controller (RECMAC) neural network in meeting such demand. In particular, the proposed network integrates a recurrent loop and an emotional learning mechanism into a cerebellar model articulation controller (CMAC), which is implemented as the main component of the controller module of a vision-based mobile robot. Briefly, the controller module consists of a sliding surface, the RECMAC, and a compensator controller. The incorporation of the recurrent structure in a slide model neural network controller ensures the retaining of the previous states of the robot to improve its dynamic mapping ability. The convergence of the proposed system is guaranteed by applying the Lyapunov stability analysis theory. The proposed system was validated and evaluated by both simulation and a practical moving-target tracking task. The experimentation demonstrated that the proposed system outperforms other popular neural network-based control systems, and thus it is superior in approximating highly nonlinear dynamics in controlling vision-based mobile robots.