Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

Modelling and intelligent control of double-link flexible robotic manipulator

The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively

Thermal Management in Laminated Die Systems Using Neural Networks

The thermal control of a die is crucial for the development of high efficiency injection moulds. For successful thermal management, this research provides an effective control strategy to find sensor locations, identify thermal dynamic models, and design controllers. By applying a clustering method and sensitivity analysis, sensor locations are identified. The neural network and finite element analysis techniques enable the modeling to deal with various cycle-times for the moulding process and uncertain dynamics of a die. A combination of off-line training through finite element analysis and training using on-line learning algorithms and experimental data is used for the system identification. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-adaptive PID methods with backpropagation (BP) and radial basis function (RBF) neural networks to tune control parameters. Direct adaptive inverse control and additive feedforward control by adding direct adaptive inverse control to self-adaptive PID controllers are also provided. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times. Additionally, the improved cooling effectiveness of the conformal cooling channel designed in this study is presented by comparing with a conventional straight channel

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its’ effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework

Intelligent control of agriculture production in greenhouses

The agricultural greenhouse system has undergone significant developments in recent years. Greenhouse microclimate is the phenomenon under study in this work. Its modelling and control processes are complex tasks to be performed mainly due to the strong nonlinearity of the phenomenon. In this thesis, a set of contributions in greenhouse microclimate modelling and control, including implementing computational intelligence algorithms, have been accomplished. The second chapter briefly describes the experimental greenhouses used in this thesis. Initially, due to the lack of an experimental greenhouse, a wooden-structured polyethene-covered greenhouse prototype was constructed and used as a small-scale nursery under arid climate conditions (moderate desert climate) in Meziraa, Biskra, Algeria. A low-cost microcontroller-based data acquisition system with a wireless connection was designed (hardware and software) and installed in the greenhouse with several low-cost sensors. It was used to gather instant information on the essential inside and outside climate variables. A dataset of five days was successfully acquired for modelling, estimation and experimental validation purposes. Secondly, a metal-structured polyethene-covered commercial-sized experimental greenhouse under Mediterranean climate conditions was exploited. It is located at “Las Palmerillas” Experimental Station, a property of the Cajamar Foundation in Almería, Spain. It is equipped with all the necessary professional sensors, actuators and data acquisition systems. A set of sufficient reliable datasets of fifteen days were obtained in different agri-seasons and used for different purposes such as microclimate modelling and control, online parameter estimation and real-time experimental validation. In the third chapter, two contributions were achieved. Firstly, a grey-box model for greenhouse temperature prediction under moderate desert climate conditions has been proposed. This contribution stands on reformulating a white-box model to make it independent of the availability of accurate values of the static parameters of its elements. The model has become less complicated by alleviating the coupling between its parameters, which makes it easier for the identification algorithm to find the optimal parameter values. A variant of the Particle swarm optimisation algorithm (PSO) called Random Inertia Weight PSO (RIWPSO) was used to identify the parameters of the proposed model by calibrating it against the experimental data. The constructed greenhouse prototype has been used to validate the proposed temperature model. The simulation results show that particle swarm optimisation has successfully achieved the desired optimality. The experimental validation process has confirmed the suitability of this model to be implemented to study and predict the greenhouse temperature, and it has emphasised the successful prediction with satisfactory accuracy. Secondly, an enhanced variant of the bio-inspired metaheuristic Bat Algorithm (BA) has been proposed and called the Random Scaling-based Bat Algorithm (RSBA). The proposition includes modifying the exploitation of the standard BA by randomly making the scaling parameter changes over the iterations. It has been dedicated to the same task of calibrating the proposed thermal grey-box model. It has been assessed as the same as PSO, primarily on the same simulated greenhouse temperature model with the assumed parameters. The simulation results have shown the superiority of the proposed RSBA compared to the standard BA in terms iv of convergence and performance accuracy. To experimentally investigate the proposed RSBA algorithm, the same experimental dataset from the greenhouse prototype has been used. The obtained prediction results are found to be in good agreement with the measured ones, which show the effectiveness of the proposed RSBA in identifying the real greenhouse thermal model. Finally, a comparative study was conducted between the RSBA and the RIWPSO. The BA has shown a faster convergence than PSO at the start of optimisation, but its convergence speed was reduced at the end. BA and PSO have shown superb performance in accurately finding the optimal solutions. However, PSO has shown a superior performance than BA in terms of time consumption regarding the problem of interest. Greenhouse microclimate modelling is a difficult task mainly due to the strong nonlinearity of the phenomenon and the uncertainty of the involved physical and non-physical parameters. The uncertainty stems from the fact that most of these parameters are unmeasurable or difficult to measure, and some are time-varying, signifying the necessity to estimate them. As the first contribution in the fourth chapter of the thesis, a methodology for online parameter estimation is proposed to estimate the time-varying parameters of a simplified greenhouse temperature model for real-time model adaptation purposes. An online estimator is developed based on an enhanced variant of the Bat Algorithm called the Random Scaling-based Bat Algorithm. It allows the continuous adaptation of the internal air temperature model and the internal solar radiation sub-model by estimating their parameters simultaneously by minimising a cost function, intending to achieve global optimality. Constraints on the search ranges are imposed to respect the physical sense. The adaptation of the models was tested with recorded datasets of different agri-seasons and on a real greenhouse in real time. The evolutions of the time-varying parameters were graphically presented and thoroughly discussed. The experimental results illustrate the successful model adaptation, presenting an average error of less than 0.28 °C for air temperature prediction and 20 W m−2 for solar radiation simulation. It proves the usefulness of the proposed methodology under changing environmental conditions. Natural ventilation flux is an important variable to measure or estimate for its significant effect on greenhouse microclimate modelling and control. It is commonly known that it can be mathematically estimated depending on the type and dimension of the greenhouse and its vents and, most importantly, on the vents opening percentage. However, most commercial greenhouses are not equipped with an automatic vent opening system which obligates the grower to perform manual control, in addition to the lack of vent position sensors, due to economic and management reasons. It leads to the absence of the control signal variable representing the vents opening percentage necessary for ventilation flux estimation. This issue has been encountered in this work after attempting to implement the developed adaptive microclimate model based on the online parameter estimator through an IoF2020 platform (internet of food and farm) in a set of commercial greenhouses with manually controlled vents located in Almeria province, Spain. To cope with this issue, the estimation of ventilation flux without using the vent opening percentage was investigated. As a second contribution in the fourth chapter, a virtual sensor for greenhouse ventilation flux estimation is proposed. It has been developed using a nonlinear autoregressive v neural network with exogenous inputs based on principal component analysis using the available measured data and the evolutions of the heat fluxes representing the greenhouse energy balance. Preliminary results show an encouraging performance of the virtual sensor in estimating the ventilation flux with a mean absolute error of 0.41 m3 s-1

Control of Continuous Casting Process Based on Two-Dimensional Flow Field Measurements

Two-dimensional flow field measurement allows us to obtain detailed information about the processes inside the continuous casting mould. This is very important because the flow phenomena in the mould are complex, and they significantly affect the steel quality. For this reason, control based on two-dimensional flow monitoring has a great potential to achieve substantial improvement over the conventional continuous casting control. Two-dimensional flow field measurement provides large amounts of measurement data distributed within the whole cross-section of the mould. An experimental setup of the continuous casting process called Mini-LIMMCAST located in Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany, is used for this thesis. This thesis examines two alternatives of flow measurement sensors: Ultrasound Doppler Velocimetry (UDV) and Contactless Inductive Flow Tomography (CIFT). Both sensor variants can obtain information on the velocity profile in the mould. Two approaches were considered to create the process model needed for model-based control: a spatially discretized version of a model based on partial differential equations and computational fluid dynamics and a model obtained using system identification methods. In the end, system identification proved to be more fruitful for the aim of creating the model-based controller. Specific features of the flow were parametrized to obtain the needed controlled variables and outputs of identified models. These features are mainly related to the exiting jet angle and the meniscus velocity. The manipulated variables considered are electromagnetic brake current and stopper rod position. Model predictive control in several versions was used as the main control approach, and the results of simulation experiments demonstrate that the model predictive controller can control the flow and achieve the optimum flow structures in the mould using UDV. CIFT measurements can provide similar velocity profiles. However, further technical developments in the CIFT sensor signal processing, such as compensating for the effects of the strong and time-varying magnetic field of the electromagnetic brake on CIFT measurements, are necessary if this sensor is to be used for closed-loop control.Two-dimensional flow field measurement allows us to obtain detailed information about the processes inside the continuous casting mould. This is very important because the flow phenomena in the mould are complex, and they significantly affect the steel quality. For this reason, control based on two-dimensional flow monitoring has a great potential to achieve substantial improvement over the conventional continuous casting control. Two-dimensional flow field measurement provides large amounts of measurement data distributed within the whole cross-section of the mould. An experimental setup of the continuous casting process called Mini-LIMMCAST located in Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany, is used for this thesis. This thesis examines two alternatives of flow measurement sensors: Ultrasound Doppler Velocimetry (UDV) and Contactless Inductive Flow Tomography (CIFT). Both sensor variants can obtain information on the velocity profile in the mould. Two approaches were considered to create the process model needed for model-based control: a spatially discretized version of a model based on partial differential equations and computational fluid dynamics and a model obtained using system identification methods. In the end, system identification proved to be more fruitful for the aim of creating the model-based controller. Specific features of the flow were parametrized to obtain the needed controlled variables and outputs of identified models. These features are mainly related to the exiting jet angle and the meniscus velocity. The manipulated variables considered are electromagnetic brake current and stopper rod position. Model predictive control in several versions was used as the main control approach, and the results of simulation experiments demonstrate that the model predictive controller can control the flow and achieve the optimum flow structures in the mould using UDV. CIFT measurements can provide similar velocity profiles. However, further technical developments in the CIFT sensor signal processing, such as compensating for the effects of the strong and time-varying magnetic field of the electromagnetic brake on CIFT measurements, are necessary if this sensor is to be used for closed-loop control.

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap

Development of a novel wave-force prediction model based on deep machine learning algorithms

The future knowledge of the waves and force is indispensable for the model identification and the real-time control of ocean engineering devices. In order to effectively control the motion of the offshore structures in a real-time manner, it is required to have an accurate and efficient prediction of the waves. Machine learning has been widely applied in ocean engineering field as it offers compromise between prediction accuracy and computational cost. The present study focuses on wave-force prediction of offshore structures based on deep machine learning algorithms. A novel wave-force prediction model is proposed, which makes full use of the efficient processing characteristics of Long Short-Term Memory Recurrent Neural Network (LSTM RNN) and Nonlinear Autoregressive Exogenous Feedback Neural Network (NARX FNN) for time series data processing. The relationship between the wave height and the wave height is non-causal and nonlinear which need future wave height knowledge for current wave excitation force. Therefore, The LSTM RNN is firstly utilized for multi-step prediction of the time series of wave elevation. The NARX FNN is used to address the model system identification between the wave heights and the wave force. Then, the LSTM RNN is further applied to predict the future force of offshore structures for the real-time control of the structure motions. After that, the proposed deep machine learning algorithm is utilized for wave-force prediction based on the experimental data obtained in Kelvin Hydrodynamic Laboratory and the optimal horizon can be specified for the test model by comparing the performance of different prediction horizons. The results indicate that LSTM-NARX model can successfully predict the time series of the waves and force