Neural Network Based Control System Research Articles

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.

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.

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.

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.

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.

Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

The major objective of this study is to design an effective control algorithm for dealing with multiple-input–multiple-output uncertain nonlinear systems. Novelty advantages of the proposed method include: (1) The network has the maximum initial rules; it helps to increase the responsiveness of the system; (2) the network has two dynamic thresholds: One dynamic threshold is utilized to consider whether to retain or to delete the existing rules and the other is used for generating a new rule; (3) the fuzzy neural network-based system can automatically construct the network structure and adjust the parameters of the system; (4) the network uses multiple combination techniques, such as sliding mode control, adaptive control, recurrent unit, wavelet function, fuzzy logic, neural network, and technique for order of preference by similarity to ideal solution multi-criteria decision analysis method. Based on the advantages of the above techniques, a self-organizing recurrent wavelet fuzzy neural network control system is designed comprising a main controller and a robust compensator. The gradient descent method is used to online tune the parameters for the main controller, and a Lyapunov stability theorem is applied to guarantee the system’s stability. Finally, the proposed control system is applied to a nonlinear chaotic system, an inverted double-pendulum system, and an unmanned aerial vehicle motion control to verify the effectiveness of the proposed control scheme. The simulation results show that the proposed control scheme can achieve favorable control performance.

Development and application of a neural network based coating weight control system for a hot-dip galvanizing line

The hot-dip galvanizing line (HDGL) is a typical order-driven discrete-event process in steelmaking. It has some complicated dynamic characteristics such as a large time-varying delay, strong nonlinearity, and unmeasured disturbance, all of which lead to the difficulty of an online coating weight controller design. We propose a novel neural network based control system to solve these problems. The proposed method has been successfully applied to a real production line at VaLin LY Steel Co., Loudi, China. The industrial application results show the effectiveness and efficiency of the proposed method, including significant reductions in the variance of the coating weight and the transition time.

Neural Network Based Vibration Control of Seismically Excited Civil Structures

This study proposes a neural network based vibration control system designed to attenuate structural vibrations induced by an earthquake. Classical feedback control algorithms are susceptible to parameter changes. For structures with uncertain parameters they can even cause instability problems. The proposed neural network based control system can identify the structural properties of the system and avoids the above mentioned problems. In the present study it is assumed that a full state of the structure is known, which means the at each floor horizontal displacements and rotations about the vertical axis are measured. Additionally, it is assumed the acceleration signal coming from the earthquake is also available. The proposed neural control strategy is compared with the classical linear quadratic regulator (LQR) not only in terms of displacement responses, but also required control forces. Moreover, the influence of different weighting matrices on performance of the proposed control strategy has been presented.The effectiveness of the neuro-controller has been demonstrated on two numerical examples: a simple single degree of freedom (DOF) structure and a multi-DOF structure representing a twelve story building. Both structures under consideration have been excited with El Centro acceleration signal. The results of numerical simulations on the SDOF system indicate that using neuro-controller it would be possible to obtain smaller amplitudes as compared with the LQ regulator, but it would require higher control effort.

Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel, and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are online tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms that the proposed network is capable of producing better control performances with high computational efficiency.

Control of unmanned agricultural vehicles using neural network-based control system

Recent advances in the fields of robotics, control systems, and wireless communication technologies have accelerated the studies related with unmanned agricultural vehicles. The unmanned agricultural vehicles have been developed to perform various functions in agricultural fields such as seeding, spraying, hoeing, weed and pest control, and harvest. Given the reduction in the number of people working in agriculture and industry, it is obvious that intelligent unmanned agricultural vehicles will be an indispensable part of agriculture in the future. In this paper, PID controller and model-based neural network PID control system has been applied for controlling an unmanned agricultural vehicle. To test the performance of control structures developed, three different input signals were used. When the results of both control structures were examined in the way of transient-state response (such as rise time, settling time, peak time, peak value, settling time) and steady-state response, the control system produced had favorable results.

Stereoscopic First Person View System for Drone Navigation

Ground control of unmanned aerial vehicles (UAV) is key to the advancement of this technology for commercial purposes. Not only because handover situations will require humans to take control over the drones when autonomous systems fail. But also, because training deep neural network based control systems require extensive real world data, and capturing meaningful and diverse data is a bottleneck. This axiom is even more prominent for the case of unmanned flying robots where there is no simple solution to capture optimal navigation footage. In such scenarios, improving the ground control and developing better autonomous systems are two sides of the same coin. To improve the ground control experience, and thus the quality of the footage, we propose to upgrade on-boad teleoperation systems to a fully immersive setup that provides operators with a stereoscopic First Person View through a Virtual Reality (VR) Head Mounted Display (HMD). We tested users (n=7) by asking them to fly our drone on the field. Test flights showed that operators flying our system can take off, fly and land successfully while wearing VR headsets. Additionally, we ran two experiments with pre-recorded videos of the flights and walks to a wider set of participants (n=69 and n=20) to compare the proposed technology to the experience provided by current drone FPV solutions that only include monoscopic vision. Our immersive stereoscopic setup enables higher accuracy depth perception, which has clear implications for achieving better teleoperation and unmanned navigation. Our studies show comprehensive data on the impact of motion and simulator sickness in case of stereoscopic setup. We present the device specifications as well as the measures that improve teleoperation experience and reduce induced simulator sickness. Our approach provides higher perception fidelity during flights, which leads to a more precise better teleoperation and ultimately translates into better flight data for training deep UAV control policies.

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources-photovoltaics modules and a wind turbine generator-have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system.

Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process.

Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.

Design of neural network-based control systems for active steering system

Nowadays, safety of road vehicles is an important issue due to the increasing road vehicle accidents. Passive safety system of the passenger vehicle is to minimize the damage to the driver and passenger of a road vehicle during an accident. Whereas an active steering system is to improve the response of the vehicle to the driver inputs even in adverse situations and thus avoid accidents. This paper presents a neural network-based robust control system design for the active steering system. Primarily, double-pinion steering system used modeling of the active steering system. Then four control structures are used to control prescribed random trajectories of the active steering system. These control structures are as classical PID Controller, Model-Based Neural Network Controller, Neural Network Predictive Controller and Robust Neural Network Predictive Control System. The results of the simulation showed that the proposed neural network-based robust control system had superior performance in adapting to large random disturbances.