Artificial Neural Network-based Control Research Articles

Artificial neural network-based control of powered knee exoskeletons for lifting tasks: design and experimental validation

Abstract This study introduces a hybrid model that utilizes a model-based optimization method to generate training data and an artificial neural network (ANN)-based learning method to offer real-time exoskeleton support in lifting activities. For the model-based optimization method, the torque of the knee exoskeleton and the optimal lifting motion are predicted utilizing a two-dimensional (2D) human–exoskeleton model. The control points for exoskeleton motor current profiles and human joint angle profiles from cubic B-spline interpolation represent the design variables. Minimizing the square of the normalized human joint torque is considered as the cost function. Subsequently, the lifting optimization problem is tackled using a sequential quadratic programming (SQP) algorithm in sparse nonlinear optimizer (SNOPT). For the learning-based approach, the learning-based control model is trained using the general regression neural network (GRNN). The anthropometric parameters of the human subjects and lifting boundary postures are used as input parameters, while the control points for exoskeleton torque are treated as output parameters. Once trained, the learning-based control model can provide exoskeleton assistive torque in real time for lifting tasks. Two test subjects’ joint angles and ground reaction forces (GRFs) comparisons are presented between the experimental and simulation results. Furthermore, the utilization of exoskeletons significantly reduces activations of the four knee extensor and flexor muscles compared to lifting without the exoskeletons for both subjects. Overall, the learning-based control method can generate assistive torque profiles in real time and faster than the model-based optimal control approach.

Adaptive Artificial Neural Network-Based Control Through Attracting-Manifold Design and Lipschitz Constant Projection

Abstract This article presents an adaptive artificial neural network-based control scheme that strategically integrates the attracting-manifold design and a smooth Lipschitz-constant projection operator. The essence of the scheme is elaborated through the design of the reference-command-tracking control law of an nth-order single-input uncertain nonlinear system that can be transformed into the Brunovsky form. The method offers two major advantages: First, by employing the attracting-manifold design, it is possible to achieve asymptotic recovery of the ideal (deterministic) closed-loop dynamics. In other words, the perturbation caused by parametric uncertainties will be driven to zero asymptotically as a result of such a design, fostering a superior control performance. Second, the established smooth projection operator ensures the Lipschitz constant of the adaptive artificial neural network is bounded from above, thereby providing a certain degree of robustness against adversarial perturbations. The proposed method is validated through a numerical simulation example and compared with a standard certainty-equivalent neural-adaptive control method to demonstrate its superior performance.

Ion Current Based Data Driven Control of HCCI - the Key to Improve Pressure Based Control Strategies?

Homogeneous charge compression ignition (HCCI) is a low-temperature combustion process that can both increase efficiency and reduce pollutant raw emissions. The process is highly dependent on low-temperature kinetics of the chemical reactions that lead to auto-ignition. Due to strong coupling of consecutive combustion cycles stable control of HCCI is a major challenge. Ion current sensors that detect ionization in the combustion chamber can provide information about chemical reactions taking place inside the cylinder. In contrast, model-based HCCI control strategies, which require accurate models that describe the current cylinder state, are often based only on the cylinder pressure. However, low-temperature chemical kinetics are not sufficiently taken into account by only using the cylinder pressure signal. Therefore, the main hypothesis of this paper is that HCCI strongly depends on the chemical state in the cylinder, which cannot be adequately described by the pressure sensor in the cylinder alone. Hence, a conventional spark plug is used to apply an ion current sensor, which is used alongside the pressure sensor. The aim is to use further information about the state of the chemical mixture for the control of the engine. The features derived from the ion current are integrated into an explicit artificial neural network-based controller. The developed control strategies are then compared against a purely pressure-based controller. Using the ion current features, the standard deviation of the combustion phasing CA50 is reduced by up to 42% compared to the pressure-based approach. The experimental results of this work show that the inclusion of the ion current in the controller provides a significant advantage for low temperature combustion control.

Optimization algorithm of an artificial neural network-based controller and simulation method for animated virtual idol characters

Optimization algorithm of an artificial neural network-based controller and simulation method for animated virtual idol characters

Novi pristup u upravljanju hidroelektranama zasnovan na neuronskim mrežama

A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. In this paper, the focus is put on the mechanical power responses improvement and the advantages of implementing new technologies contrary to the problems that can occur by using them such as plant destabilization by implementing minor changes, fitting parameters, learning, and training processes, number of hidden layers/neurons, number of epochs, etc.

BALLU2: A Safe and Affordable Buoyancy Assisted Biped.

This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platform’s distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robot’s limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU’s walking.

Research developments in adaptive intelligent vibration control of smart civil structures

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Artificial Neural network-based Controller for PEMFC based Distributed generation

Artificial Neural network-based Controller for PEMFC based Distributed generation

An Intelligent Approach to Active and Reactive Power Control in a Grid-Connected Solar Photovoltaic System

The increasing demand of electrical energy and environmental concerns are invigorating the use of renewable energy resources for power generation. Renewable energy resources can provide an attractive solution for present and future energy requirements. In this scenario, solar photovoltaic systems are becoming prominent and sustainable solutions with numerous advantages. However, the utilization of solar photovoltaic systems in distribution generation makes it mandatory to deploy efficient and organized control measures for integrating solar photovoltaic plants with the grid. In this paper, the control of grid-tied solar photovoltaic systems using a Kalman filter-based generalized neural network is presented with a variable step size perturb and observe-based maximum power point tracking controller to extract the maximum power from a solar photovoltaic plant. The presented system provides power-quality enhancement and supports a three-phase AC grid. The proposed approach extracts the load currents’ primary components for efficient harmonics elimination, synchronizes the system with the grid and provides a fast response during rapidly changing conditions. The results of the proposed control technique are also compared with the artificial neural network-based control technique for validation purposes. The proposed algorithm is found more suitable for using a smaller number of unknown weights and training patterns with reduced computational time.

Artificial Neural Network-Based Control of Switched Reluctance Motor for Torque Ripple Reduction

Switched reluctance motor is acquiring major attention because of its simple design, economic development, and reduced dependability. These attributes make switched reluctance motors superior to other variable speed machines. The major challenge associated with the development of a switched reluctance motor is its high torque ripple. Torque ripple produces noise and vibration, resulting in degradation of its performance. Various techniques are developed to cope with torque ripples. Practically, there exists not a single mature technique for the minimization of torque ripples in switched reluctance motors. In this research, a switched reluctance motor is modelled and analysed. Its speed and current control are implemented through artificial neural networks. Artificial neural network is found to be a promising technique as compared with other techniques because of its accuracy, reduced complexity, stability, and generalization. The Levenberg–Marquardt algorithm is utilized in artificial neural networks due to its fast and stable convergence for training and testing. It is found from research that artificial neural network-based improved control shows better performance of the switched reluctance motor. Realization of this technique is further validated from its mean square error analysis. Operating parameters of the switched reluctance motor are improved significantly. Simulation environment is created in Matlab/Simulink.

Artificial neural network-based current control of field oriented controlled induction motor drive

A hysteresis current controller (HCC) is commonly used in high-performance AC motor drives to control the current directly. Recently, the predictive current controller (PCC) is also used as an alternative to the classical current controller for speed and torque regulation of induction motor (IM) drives. However, PCC has drawbacks of large flux and torque ripples, large total harmonic distortions (THDs) in current and voltage and dependency on parameters. This paper proposes current control with artificial neural network (ANN) for a field-oriented controlled induction motor (FOCIM) drive. The ANN has input current error between the reference and the measured stator currents. The output function of neuron is a hyperbolic tan (or tan-sigmoid) function to apply error Levenberg–Marquardt (L–M) back propagation as learning rule because of its fast convergence. The proposed method is based on a new approach in which hysteresis band is replaced by ANN comparator to improve the performance of the FOCIM drive. It minimizes torque ripples, flux ripples, voltage and current THDs over the existing HCC and PCC methods. The superiority of the proposed method compared to existing methods is established by simulation and experimental results.

Accurate power-sharing, voltage regulation, and SOC regulation for LVDC microgrid with hybrid energy storage system using artificial neural network

ABSTRACT In this paper, an artificial neural network-based control strategy is proposed for low voltage DC microgrid (LVDC microgrid) with a hybrid energy storage system (HESS) to improve power-sharing between battery and supercapacitor (SC) to suit the demand-generation imbalance, maintain state-of-charge (SOC) within boundaries and thereby to regulate the dc bus voltage. The conventional controller cannot track the SCs current rapidly with the high-frequency component that will place dynamic stress on the battery, further resulting in shorter battery life. The significant advantage is that in the proposed control strategy, redirections of unwaged battery currents to SCs for fast compensations enhance battery life span. The proposed control strategy effectiveness was investigated by simulations, including a comparison of overshoot/undershoot and settling time in dc bus voltage with a conventional control strategy. The results have been experimentally verified by hardware-in-loop (HIL) on a field-programmable gate array (FPGA)-based real-time simulator.

Power enhancement in a grid connected solar PV System by using PLL-neural based converter

This work deals with neural network control algorithm-based grid connected to solar photo voltaic (PV) system consisting of DC-AC converter. The reference solar-grid current for three-leg VSC are estimated using neural network control algorithm. The neural network control algorithm based solar PV system is modeled in MATLAB R2018a along with SIMULINK.. This study presents an artificial neural network-based controller for regulating the level of active and reactive power output. First, the three phase currents from the VSI are measured and compared with the three reference currents. The neural network is trained to have minimum output error. It was concluded that the power output from the system was found to be190 KVA in case of system having no intelligent controller and 700 KVA in case of system with AAN based control. The voltage of output is maintained to be 20 kV in the grid system for analysis purpose. Thus the proposed control is expected to be implemented in the renewable energy resources for better output.

Artificial neural network-based control strategies for PMSG-based grid connected wind energy conversion system

This paper presents a novel control strategy employing the artificial neural network-based direct torque control (DTC) strategy and direct power control (DPC) strategy for permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). The Aeolos 3 kW wind system ratings are considered in this paper for real time system validation. The proposed control strategy combines artificial neural network (ANN)-based DTC as machine side control (MSC) strategy and ANN-based DPC as grid side control (GSC) strategy in order to track the maximum power and to regulate the active power, respectively. To generate the switching pulse for the converter in generator side and grid side, a neural network-based technique is employed. Furthermore, the DC link voltage is optimised using the GSC topology. The proposed controller provides a stable voltage and power which is an effective solution for grid integration and constant power flow from the generator system to the grid system. The proposed coordinate control strategy is validated using MATLAB/Simulink software to analysis the working condition. The experimental test bench is implemented using dSPACE to study the real time analysis of the proposed control strategy.

Artificial neural network-based control strategies for PMSG-based grid connected wind energy conversion system

This paper presents a novel control strategy employing the artificial neural network-based direct torque control (DTC) strategy and direct power control (DPC) strategy for permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). The Aeolos 3 kW wind system ratings are considered in this paper for real time system validation. The proposed control strategy combines artificial neural network (ANN)-based DTC as machine side control (MSC) strategy and ANN-based DPC as grid side control (GSC) strategy in order to track the maximum power and to regulate the active power, respectively. To generate the switching pulse for the converter in generator side and grid side, a neural network-based technique is employed. Furthermore, the DC link voltage is optimised using the GSC topology. The proposed controller provides a stable voltage and power which is an effective solution for grid integration and constant power flow from the generator system to the grid system. The proposed coordinate control strategy is validated using MATLAB/Simulink software to analysis the working condition. The experimental test bench is implemented using dSPACE to study the real time analysis of the proposed control strategy.

Adaptive Heart Rate Regulation Using Implantable Pacemaker with Artificial Neural Network-Based Backstepping Controller

Implantable cardiac pacemaker is a standard medical device totreat heart rhythm disorders. In this paper, a new adaptivebackstepping controller is developed to enhance the performanceof dual-sensor pacemakers for regulating the heart rate, based onradial basis function neural networks. The robust design ofadaptive backstepping controller using Lyapunov functions allowsguaranteeing the stability and performance of the rate-adaptivepacing system for accurately accomplishing the heart rateregulation at different preset values. This developed controlsystem has been retrospectively tested on six datasets of twopatients with a pacemaker during three body activities of the rest,walking, and exercising. The resulting root mean square error(RMSE) and maximum error are less than 0.36 and 0.50 %,respectively. In addition, comparative results of this study showedthat the performance of developed backstepping controller issuperior to other pacemaker controllers in the previous studies.

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.

Smoothing wind power fluctuations by artificial neural network-based pitch angle controller

Wind energy has been receiving more acceptance as a reproducible, resourceful and clean energy source since last decade. Wind power is not constant and may fluctuate below the rated wind power when the wind speed is lower than the rated speed. This fact affects the stability of the power system, to which the wind generators are connected. This is becoming more significant with the increasing penetration of wind energy systems. Pitch angle control has been one of the most common methods for smoothing output power fluctuations during below rated wind incidents. An artificial neural network (ANN)-based pitch angle controller is proposed in this paper for smoothing wind power fluctuations during below rated wind incidents beside traditional power regulations during above rated wind incidents. Two smoothing methods have been presented: the determination of the command output power based on the exponential moving average with a proper selection of correction factor by neural network and the dynamic selection of target output power according to the wind incident. Simulation results show the effectiveness of the proposed ANN-based pitch angle controller in smoothing output power fluctuations with significantly small drop of output power.

An artificial neural network-based controller for the control of induced paralysis using vecuronium bromide

An artificial neural network-based controller for the control of induced paralysis using vecuronium bromide

An artificial neural network-based controller for the control of induced paralysis using vecuronium bromide

This study presents an artificial neural network-based controller for regulating the level of induced paralysis during surgery using vecuronium bromide. The controller uses the myogram of a rapid muscle contractions (called twitch) to generate the appropriate infusion rate. The controller is self-adjusting and can accommodate inter- and intrapatient drug response variations. It also withstands changes in the pure time delay and nonlinear pharmacokinetic parameters of the response. Another feature of the controller is that it does not depend on a priori knowledge of the patient response model. Computer simulations using pharmacokinetic and pharmacodynamic models showed negligible steady-state error and maximum percent undershoot averaged to 6.24%. The average infusion rate for 90% paralysis was 1.22 (microg x kg(-1) x min[-1]).