Nonlinear Output Research Articles

Adaptive fuzzy command filtering control for nonlinear MIMO systems with full state constraints and unknown control direction

In this paper, the command-filter-based adaptive control strategy is proposed for a class of unknown nonlinear multiple input multiple output (MIMO) systems with full state constraints and unknown control direction, whose unknown nonlinear function is approximated by the fuzzy logic system (FLS). First, the fuzzy logic system (FLS) act as the universal approximator of the unknown nonlinear function and the command-filtered backstepping control method is utilized to handle the difficulty induced by the explosion of differentiation, as well as the compensating signals are designed to make up for the command filter error. Besides, the appropriate barrier Lyapunov function and the Nussbaum-type functions are employed to deal with the constraints violation and the unknown direction control gains, respectively. It is proved that the practical tracking performance of the system can be achieved with the designed protocols and all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation example is performed to verify the effectiveness of the proposed control strategy.

An FPGA Based energy correction method for one-to-one coupled PET detector: model and evaluation

A PET scanner based on silicon photomultipliers (SiPMs) has been widely used as an advanced nuclear medicine imaging technique that yields quantitative images of regional in vivo biology and biochemistry. The compact size of the SiPM allows direct one-to-one coupling between the scintillation crystal and the photosensor, yielding better timing and energy resolutions than the light sharing methods that have to be used in photomultiplier tube (PMT) PET systems. To decrease the volume of readout electronics, a front-end multiplexer with position decoder is a common choice for the one-to-one system without a highly integrated application specific integrated circuit (ASIC). However, in this case we cannot measure each crystal's deposited energy inspired by an annihilation photon, so the inter-crystal scatter (ICS) events will lead to the crystal mispositioning and then deteriorate the detector intrinsic resolution. Besides, considering the events rejection within the energy window resulting from the gain dispersion and non-linear outputs of the SiPMs, an energy correction mechanism is needed. Yet, lack of the information of each crystal's energy will introduce large energy correction error for the ICS events. For this issue, an online energy correction mechanism implemented on a Kintext-7 Field Programmable Gate Array (FPGA) device is presented in this paper. Experiments in the laboratory were performed using an 8 × 8 segmented LYSO crystals coupled with an 8 × 8 SiPM (J-series, from ON Semiconductor) array which is under 22Na point source excitation. Test results indicate that both the energy of the non-ICS and ICS events can be precisely corrected and the energy resolution is better than 12 %. We also applied this method to an actual clinical PET scanner under a 68Ge line source to verify its multi-channel reliability.

Fault-Tolerant Control of an Offshore Ocean Thermal Energy Conversion System with Output Constraints

A fault-tolerant control method is investigated for an offshore ocean thermal energy conversion (OTEC) system with constraints in this work. In the system modelling, the distributed parameter system is used to build the OTEC system, which is expressed by a set of partial differential equations (PDEs) and ordinary differential equations (ODEs). Moreover, a boundary fault-tolerant control scheme is proposed to solve actuator failures. Meanwhile, according to the control scheme, system vibration suppressing and system stability can be realized. Two proposed coupled controllers are designed to solve actuator faults while considering the output nonlinearity of the OTEC system. In addition, the stability of the OTEC system based on barrier Lyapunov function is proven by simulation results, which verifies the effectiveness of the proposed coupled fault-tolerant-boundary control method. Finally, the proposed method can effectively improve the performance and safety of ocean thermal energy conversion in the OTEC system.

Design, Analysis, and Hardware Verification of a Linearized Thermistor-Based Temperature Measurement System

This article brings forward a thermistor-based system for linear estimation of temperature. The proposed system uses a relaxation oscillator-based digitizer and a versatile linearization approach as the core elements in its architecture. This linearizing digitizer for thermistors, aided with ratiometric operations, provides many salient features, including: 1) simplicity of system architecture; 2) direct-digital linear transfer characteristics; 3) compensation against connecting lead resistances; 4) low calibration requirements; and 5) wide range of operation, with better performance with respect to the prior art. The design philosophy of the proposed system is mathematically derived and presented in this article, and the aforementioned features are theoretically established. Furthermore, detailed error analysis, as well as experimental evaluation of the hardware system prototype, is carried out. The developed digitizer is shown to exhibit excellent performance (nonlinearity < 0.09%, relative error < 0.4%, and lead resistance influence < 0.1%) during emulation studies, over a range of 0 °C – 120 °C. Multiple exper- iments with synthesized thermistor characteristics and actual sensor interfacing tests reveal that the maximum output nonlinearity of the developed system is around 0.45%. This article also discusses precision-wise performance and allied use cases of the system, followed by a summary of its comparison study with similar literature.

ARTIFICIAL NEURAL NETWORK (ANN) AND ITS APPLICATIONS IN THE FIELD OF PHARMACY WITH A SPECIAL FOCUS ON PHARMACEUTICAL PRODUCT DEVELOPMENT: A RECENT REVIEW

Artificial Neural Network (ANN) is a new field and a machine learning method based on artificial intelligence (AI) which has recently been utilized to optimize pharmaceutical products. ANN usually makes highly complicated models into a simple and convenient numerical solution. The potential of ANN technique is its capacity to understand and measure complicated non-linear input output interactions and their ability to interpret ambiguous or slightly constricted structures. Description about the ANN includes the historical background, its different phases while product development and also its various types that can be employed in field of optimized pharmaceutical product development. Within the context of Quality by Design (QbD), ANN techniques can be employed for the creation of design space within the limits of all applied variable factors. The aim of this review paper includes the description about the ANN and all possible applications in the field of pharmaceutical product development.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

A long short-term memory based Quasi-Virtual Analyzer for dynamic real-time soft sensing of a Simulated Moving Bed unit

The evaluation of unmeasurable quantities is an issue faced in several fields. One example is in the production of pharmaceuticals, in which, typically, isomer properties related to different effects are found: one enantiomer may kill while the other may cure. Thus, strict quality control is necessary in this field, which requires accurate, reliable and frequent measurements of the system. However, the measurement of the main quality properties associated with the production of some pharmaceuticals has a low frequency. Consequently, in addition to safety-related issues, financial losses are also related to these low frequency of measurements, as the product can easily run towards an out of spec production. In this scenario, the present work proposes a novel Deep Artificial Intelligence structure, which has an intrinsic (Nonlinear Output Error) NOE structure, associated with a (Nonlinear AutoRegressive with Exogenous input) NARX predictor, to be used as an online soft sensor in order to provide information about the main properties of a Simulated Moving Bed chromatographic unit, commonly used in the production of pharmaceuticals, in order to mitigate the low frequency of measurement associated with this unit. The proposed structure is here called Improved Quasi-Virtual Analyzer. The model can adapt itself as the process evolves, having the possibility of online learning through measurements obtained in the laboratory periodically. The proposed structure was tested in a software-in-the-loop scenario and compared with a more traditional alternative. These tests showed a robust capacity of the Improved Quasi-Virtual Analyzer to provide reliable predictions in real-time, as well as to outperform the traditional artificial network structure.

Multi-target regression via non-linear output structure learning

The problem of simultaneously predicting multiple real-valued outputs using a shared set of input variables is known as multi-target regression and has attracted considerable interest in the past couple of years. The dominant approach in the literature for multi-target regression is to capture the dependencies between the outputs through a linear model and express it as an output mixing matrix. This modelling formalism, however, is too simplistic in real-world problems where the output variables are related to one another in a more complex and non-linear fashion. To address this problem, in this study, we propose a structural modelling approach where the correlations between output variables are modelled using a non-linear approach. In particular, we pose the multi-target regression problem as one of vector-valued composition function learning in the reproducing kernel Hilbert space and propose a non-linear structure learning approach to capture the relationship between the outputs via an output kernel. By virtue of using a non-linear output kernel function, the proposed approach can better discover non-linear dependencies among targets for improved prediction performance. An extensive evaluation conducted on different databases reveals the benefits of the proposed multi-target regression technique against the baseline and the state-of-the-art methods.

Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification

Most real-time systems are nonlinear in nature, and their optimization is very difficult due to inherit stiffness and complex system representation. The computational intelligent algorithms of evolutionary computing paradigm (ECP) effectively solve various complex, nonlinear optimization problems. The differential evolution algorithm (DEA) is one of the most important approaches in ECP, which outperforms other standard approaches in terms of accuracy and convergence performance. In this study, a novel application of a recently proposed variant of DEA, the so-called, maximum-likelihood-based, adaptive, differential evolution algorithm (ADEA), is investigated for the identification of nonlinear Hammerstein output error (HOE) systems that are widely used to model different nonlinear processes of engineering and applied sciences. The performance of the ADEA is evaluated by taking polynomial- and sigmoidal-type nonlinearities in two case studies of HOE systems. Moreover, the robustness of the proposed scheme is examined for different noise levels. Reliability and consistent accuracy are assessed through multiple independent trials of the scheme. The convergence, accuracy, robustness and reliability of the ADEA are carefully examined for HOE identification in comparison with the standard counterpart of the DEA. The ADEA achieves the fitness values of 1.43 × 10−8 and 3.46 × 10−9 for a population size of 80 and 100, respectively, in the HOE system identification problem of case study 1 for a 0.01 nose level, while the respective fitness values in the case of DEA are 1.43 × 10−6 and 3.46 × 10−7. The ADEA is more statistically consistent but less complex when compared to the DEA due to the extra operations involved in introducing the adaptiveness during the mutation and crossover. The current study may consider the approach of effective nonlinear system identification as a step further in developing ECP-based computational intelligence.

Robust output regulation for unknown nonlinear strict‐feedback systems with input delay

AbstractThis article investigates the robust output regulation problem for unknown nonlinear strict‐feedback systems with input delay. Nonlinear fuzzy internal model and adaptive fuzzy backstepping control are integrated to obtain a feedforward‐feedback composite controller. As a distinctive feature, Pade approximation technique is introduced to deal with the input delay of nonlinear output regulation problem for the first time. The process of control strategy can be divided into two steps. In the first step, a novel nonlinear fuzzy internal model is designed to get the feedforward compensator. In the second step, an adaptive fuzzy backstepping control scheme is proposed to get the feedback controller, which can guarantee semiglobally uniformly ultimately bounded of the closed‐loop system. Simulation results of an interconnected power system show the validity of the theoretical results.

ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

An improved method for PV output prediction using artificial neural network with overlap training range

Efficient and accurate photovoltaic (PV) modeling is significant for ensuring the stable operation of the power grid. The nonlinear output characteristics of PV modules are usually described by current–voltage (I–V) curves or power–voltage (P–V) curves. Traditional models normally include a diode model and an analytical model. The process of model parameter's extraction is complex and inconvenient because the equations of the diode model and analytical model are non-linear. In traditional extraction methods using a neural network, all irradiance training data are input into the neural network, which suffer from long training time and insufficient accuracy. In this paper, two neural networks with different training ranges are proposed to replace the whole neural network for predicting I–V curves, P–V curves, and maximum power of the analytical model. Each neural network consists of three layers. In detail, the input is the value of solar irradiance and module temperature, and the output is the shape parameters of the analytical model. The effectiveness of the proposed method is verified by massive experimental data of different PV modules. The output performance estimation of the proposed method is better than that of other methods.

Nonlinear robust periodic output regulation of minimum phase systems

In linear system theory, it is a well-known fact that a regulator given by the cascade of an oscillatory dynamics, driven by some regulated variables, and of a stabiliser stabilising the cascade of the plant and of the oscillators has the ability of blocking on the steady state of the regulated variables any harmonics matched with the ones of the oscillators. This is the well-celebrated internal model principle. In this paper, we are interested to follow the same design route for a controlled plant that is a nonlinear and periodic system with period T: we add a bunch of linear oscillators, embedding \(n_o\) harmonics that are multiple of \(2 \pi /T\), driven by a “regulated variable” of the nonlinear system, we look for a stabiliser for the nonlinear cascade of the plant and the oscillators, and we study the asymptotic properties of the resulting closed-loop regulated variable. In this framework, the contributions of the paper are multiple: for specific class of minimum-phase systems we present a systematic way of designing a stabiliser, which is uniform with respect to \(n_o\), by using a mix of high-gain and forwarding techniques; we prove that the resulting closed-loop system has a periodic steady state with period T with a domain of attraction not shrinking with \(n_o\); similarly to the linear case, we also show that the spectrum of the steady-state closed-loop regulated variable does not contain the n harmonics embedded in the bunch of oscillators and that the \(L_2\) norm of the regulated variable is a monotonically decreasing function of \(n_o\). The results are robust, namely the asymptotic properties on the regulated variable hold also in the presence of any uncertainties in the controlled plant not destroying closed-loop stability.

Development of Site Classification System and Seismic Site Coefficients for Korea

ABSTRACT The contemporary two-parameter-based site classification system of Korea, which is depth to bedrock and averaged shear wave velocity of soil deposits overlying the bedrock, has been reported to be ineffective in representing the unique amplification characteristics of shallow bedrock profiles. A new site classification scheme conditioned on site natural period and corresponding intensity-dependent seismic coefficients for Korean site conditions is proposed based on extensive one-dimensional non-linear site response analysis outputs. It is highlighted that the proposed site classification system and site coefficients coupled with a new elastic response spectrum considerably reduce the residuals with the computed site response analysis outputs.

Particle swarm algorithm and OLE for process control based PID design for dual capacity water tank

Typical dual capacity tank level control systems suffer from long control regulation times, large nonlinear output deviations and hysteresis. Facing with this situation, an optimization of multi-parameter control of PID controller using particle swarm algorithm (PSO) is proposed to reduce the overshoot phenomenon. By analyzing the mathematical model of the dual capacity water tank, the PSO intelligent algorithm is combined with the PID controller to obtain the optimal control parameters and performance indicators in MATLAB, through the OPC communication protocol, the water level of the tank is transmitted to the Kingview monitoring system to display the change curve in real time. The results showed that the PSO algorithm and OPC protocol can combine the complex algorithm function of MATLAB with the excellent plotting of Kingview, play a key role in the dual capacity tank level systems.

An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

Solar power plant generation forecasting using NARX neural network model: A case study

New technologies have been developed and adopted to generate energy from renewable sources to satisfy the increasing demand without causing environmental damage. However, estimating the power output of inherently intermittent, weather-driven, and non-dispatchable renewable energy sources is a major scientific and societal concern. In this study, a neural network model to enable short-to-middle term forecasts of a photovoltaic (PV) power system is provided. Using historical weather and power generation data, a non-linear autoregressive network with exogenous input (NARX) model is built to forecast the non-linear photovoltaic system output. The performance of the model is then analyzed by different statistical evaluation parameters. It is shown that the PV system power output estimation method can be successfully employed.

Desain Simulasi Maksimum Power Point Tracking Metode P&amp;O Pada Panel Surya Di Azzahra Hidroponik Juata Tarakan

Solar panels are electricity generators that utilize solar energy. One of them is the Azzahra Hydroponic solar panel with a capacity of 200 Wp which has a non-linear output. According to Amri, (2015) this is due to the changing conditions of the sun. To solve the non-linear problem of solar panels, there are 2 types of controllers, namely PWM and MPPT. This study uses the Perturb Observe (PO) method because this method is simpler, has efficiency and a high level of accuracy compared to other methods (Kesler, 2016). Under conditions of intensity S= 1000 and temperature T = 40°, Pmax is 201.5 W, Po is 201.1 W and the time required for MPPT to track is 0.003 seconds, at S=500 T=25, Pmax= 97 ,7 W and Po = 96.4 W and the time required is 0.006 seconds. And in the condition S = 100 T = 10 obtained Pmax = 9.4 W, Po = 9.3 W and the time required for MPPT to track is 0.04 seconds. Based on the simulation results, it can be concluded that the maximum power point tracking (MPPT) simulation design using the perturb and observe (PO) method was successful and MPPT was able to track the maximum power on Azzahra Hydroponic solar panels under conditions of varying intensity and temperature.

Identification of Wiener systems based on the variable forgetting factor multierror stochastic gradient and the key term separation

SummaryThe identification of Wiener systems is very difficult because of the output nonlinearity and the parameter product term. To identify the Wiener system, a novel stochastic gradient algorithm based on the multierror and the key term separation is proposed. Firstly, the Wiener system is parameterized as a pseudo‐linear model to avoid the products of the parameters. Secondly, a parzen window is used to estimate the probability density function of the error. Thirdly, a stochastic information gradient algorithm with the multierror is adopted to estimate the parameters. The multierror takes the place of the scalar error by the stacked error, which accelerates the algorithm greatly. Fourthly, a variable forgetting factor considering errors is integrated to further speed the algorithm up. Finally, the proposed algorithm is validated by a numerical example and an industrial case. The estimation results indicate that the proposed algorithm can obtain accurate estimates with fast convergence speed.

A Takagi-Sugeno Fuzzy Model for Greenhouse Climate

This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm.