Real System Output Research Articles

Pseudo-model-based iterative learning control for nonlinear multi-phase batch processes

In this article, a pseudo-model-based iterative learning control (ILC) is exploited for multi-phase batch processes which can be described as a nonlinear switched system with unknown functions and identical states in different phases. The nonlinear switched system is converted into a linear model whose system parameter matrix is approximated by minimizing the discrepancy from the real system output increment to the approximated system output increment. A data-driven ILC is constructed in an interactive form with system parameter matrix approximate algorithm. Meanwhile, the signs of the diagonal elements of system lower triangular parameter matrix are introduced into the construction of control input law. Theoretical analysis shows that the pseudo-model-based ILC (PM-ILC) concept can be extended to multi-phase batch processes with non-identical states in different phases. Furthermore, the approximation error of the system parameters matrix is bounded and the proposed PM-ILC is robust if the parameter is appropriately chosen. Simulation results illustrate the effectiveness and practicability of the proposed PM-ILC.

Initial flight test verification of software and hardware in the loop simulations of the flight stabilization system

PurposeThis paper aims to present assessment of models and simulation results used in the development process of flight stabilisation system that uses trim tabs for PZL-130 Orlik turboprop military trainer aircraft. Flight test of the system allowed to compare software and hardware simulation results with real flight recordings.Design/methodology/approachProposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation, software and hardware in the loop testing. The project reached flight testing stage which allowed to gather data to verify models and simulation results and asses their quality.FindingsResults of the comparison showed that the trim tab actuator model used in simulation can be improved by adding play. This reduced the difference between simulation and real flight system output – actuator angle. The influence of airloads on the flying actuator angle compared to hardware in the loop simulation in lab is less than ± 0.6°.Originality/valueProposed flight stabilisation system that uses trim tabs has several benefits over classic automatic flight system in terms of weight, energy consumption and structure simplicity and does not need aircraft primary control modification. It was developed using modern techniques of model-based design, automatic code generation and hardware in the loop simulations.

HYPEREION—A precision system for the detection of the absorption profile centred at 78 MHz in the radio background spectrum

Abstract The report of a detection of an absorption profile centred at 78 MHz in the continuum radio background spectrum by the EDGES experiment and its interpretation as the redshifted 21 cm signal of cosmological origin has become one of the most debated results of observational cosmology in recent times. The cosmological 21 cm has long been proposed to be a powerful probe for observing the early Universe and tracing its evolution over cosmic time. Even though the science case is well established, measurement challenges posed on the technical ground are not fully understood to the level of claiming a successful detection. EDGES’s detection has naturally motivated a number of experimental attempts worldwide to corroborate the findings. In this paper, we present a precision cross-correlation spectrometer HYPEREION purpose-designed for a precision radio background measurement between 50–120 MHz to detect the absorption profile reported by the EDGES experiment. HYPEREION implements a pre-correlation signal processing technique that self-calibrates any spurious additive contamination from within the system and delivers a differential measurement of the sky spectrum and a reference thermal load internal to the system. This ensures an unambiguous ‘zero-point’ of absolute calibration of the purported absorption profile. We present the system design, measurement equations of the ideal system, systematic effects in the real system, and finally, an assessment of the real system output for the detection of the absorption profile at 78 MHz in the continuum radio background spectrum.

Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment.

PurposeThis study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study.MethodsThe structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data.ResultsThe R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance.ConclusionIt can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.

Model-Based Diagnosis of Multi-Agent Systems: A Survey

As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well-known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system's output contradicts the model's expected output. The model is then used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. non-temporal. Temporal diagnosis is used to diagnose the MAS's temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.

A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems

Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM.

IMPLEMENTATION OF LEBESGUE SAMPLING METHOD AND DIGITAL SENSORS FOR CONTROLLING THE LEVEL VARIABLE IN A CONTINUOUS SYSTEM

This study aims to show that the continuous control from a level system can be efficiently measured and controlled using capacitive digital binary sensors, which in this case, replace the measurement signal from an analog differential pressure transmitter in a level control system. The binary sensors low cost and the digital output they process allow the reproduction of a correct signal and the estimation of a variable for controlling the water level inside the process tank through a proportional pneumatic level control valve, which receives the control signal from the Lebesgue sampling estimation algorithm applied herein for processing digital measurements. In this particular case, the Lebesgue algorithm is applied to reproduce the estimation of values obtained from the continuous signal in the real level process for the measurement and control. Also, are compared both, simulated and real outputs obtained using the Lebesgue algorithm and digital sensors, which were applied to a state observer controller that relates digital signals for controlling the real level system output. The application of the Lebesgue algorithm in the real level process concludes that the analog level signal can be efficiently reproduced using this method. In addition, the controller enables the system to smoothly conduct digital output processing using digital sensors to control the system output correctly, validating that not only analog sensors should be applied for controlling the output of proportional actuators, because it is shown that digital binary signals can be used for controlling and emulating continuous signals, which were processed and applied to the pneumatic valve. Keywords: Lebesgue sampling, estimation, binary sensor, observer controller, finite state machine, continuous system, control, LTI systems, identification, state variable, estimated output, proportional actuator

Robust adaptive iterative learning control for nonrepetitive systems with iteration-varying parameters and initial state

This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizing the discrepancy of the real system outputs from the estimated system outputs, a gradient-type adaptation law is designed to estimate the system lower triangular parameter matrix and the system initial state. Especially, the current parametric estimation is updated by virtue of the input-output data and the previous estimation. Secondly, an AILC mechanism is constructed based on the estimated system lower triangular parameter matrix, where the control input algorithm and the adaptation law are scheduled in an interactive mode. Thirdly, the boundedness of the estimation error between the real system matrix and the estimation one is derived by means of vector norm theory. Based on the boundedness of the estimation error, the robust condition of the proposed AILC is given. Finally, the proposed AILC is investigated for a class of nonlinear affine systems and the corresponding results are captured. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

CONTINUOUS-TIME FRACTIONAL ORDER LINEAR SYSTEMS IDENTIFICATION USING CHEBYSHEV WAVELET

In this paper, the identification of continuous-time fractional order linear systems (FOLS) is investigated. In order to identify the differentiation or- ders as well as parameters and reduce the computation complexity, a novel identification method based on Chebyshev wavelet is proposed. Firstly, the Chebyshev wavelet operational matrices for fractional integration operator is derived. Then, the FOLS is converted to an algebraic equation by using the the Chebyshev wavelet operational matrices. Finally, the parameters and differentiation orders are estimated by minimizing the error between the output of real system and that of identified systems. Experimental results show the effectiveness of the proposed method.

Identification of fractional Hammerstein system with application to a heating process

In this paper, fractional Hammerstein system identification is considered, where the linear block is of fractional order. The original discrete Hammerstein system is first converted to a fractional polynomial nonlinear state-space model (PNLSS), which allows a better parameterization of the model. An output-error identification approach is developed based on the robust Levenberg–Marquardt algorithm, whose nevralgic point is the calculation of parametric sensitivity functions. These last are developed as a multivariable fractional PNLSS model which effectively reduces the computational effort. Various simulations are used to test the method’s efficiency and its statistical performance is analyzed using Monte Carlo simulation. Finally, the method is evaluated through a heating experimental benchmark. The obtained results show good agreement with the real system outputs.

Adaptive Iterative Learning Control of Switched Nonlinear Discrete-Time Systems With Unmodeled Dynamics

This paper is concerned with the issue of adaptive iterative learning control (AILC) for single-input, single-output (SISO), switched nonlinear discrete-time systems with unmodeled dynamics and stochastic measurement noise. Under the random switching rule, the nonlinear subsystem is estimated by a linear model which is constructed by minimizing the discrepancy between the real system output and the estimated system output with gradient-type technique. Meanwhile, the adaptive switched control law is designed by the updated subsystem matrix estimation. By taking advantages of norm theory, the boundedness of the estimation error is derived. Further, by the manner of the statistical technique, the results are conducted that the expectation of the tracking error is monotonically convergent to zero and the covariance matrix of the tracking error is bounded. Finally, simulation results are given to validate the proposed scheme is effective.

Global finite‐time output stabilization of nonlinear systems with unknown measurement sensitivity

SummaryThis paper focuses on the output stabilization problem for a class of strict‐feedback systems with unknown nonlinear dynamics and unknown measurement sensitivity. The imprecise sensor measurements render the existing robust control approaches such as neuro or fuzzy control infeasible. A solution is provided in this paper by a feat of the concept of tuning functions and barrier Lyapunov functions (BLFs). Utilizing the modification capability of tuning functions, global stability is preserved. Exploiting the potential robustness of BLFs, the effect of unmodeled dynamics is counteracted under sensitivity errors. The combination of tuning functions with BLFs further leads to a distinct property that the real system output converges to an adjustable region in finite time. Compared with the existing results, the need for prior knowledge of system nonlinearities and measurement sensitivity is removed in this paper. Finally, simulation results illustrate the established theoretical findings.

Dynamic modelling of the swash plate of a hydraulic axial piston pump for condition monitoring applications

In the last years Prognostic and Health Management (PHM) has become one of the challenging topic in the engineering field. In particular, model-based approach for diagnostic relies on the development of a mathematical model of the system representing its flawless status. Once the model has been developed and carefully calibrated on experimental data referred to flawless pump condition the comparison between the model output and the real system output leads to the residual analysis, which gives a diagnosis of the component health. This paper presents the mathematical model of a hydraulic axial piston pump developed in order to replicate the dynamic behavior of the swash plate for PHM applications. The model has been developed on the basis of simplified hypotheses, a friction model between swash plate and bearings has been introduced. A detailed experimental activity was carried out to calibrate and validate the model with step tests and sweep tests. The comparison between numerical and experimental results shows a satisfying agreement and highlights the model capability to reproduce the swash plate dynamics. Future works will include tests with the pump in faulty conditions to evaluate the pump health state through the residual analysis of the swash plate position.

Heuristics-Enhanced Model Fusion Considering Incomplete Data Using Kriging Models

Simulation models are widely used to describe processes that would otherwise be arduous to analyze. However, many of these models merely provide an estimated response of the real systems, as their input parameters are exposed to uncertainty, or partially excluded from the model due to the complexity, or lack of understanding of the problem's physics. Accordingly, the prediction accuracy can be improved by integrating physical observations into low fidelity models, a process known as model calibration or model fusion. Typical model fusion techniques are essentially concerned with how to allocate information-rich data points to improve the model accuracy. However, methods on subtracting more information from already available data points have been starving attention. Subsequently, in this paper we acknowledge the dependence between the prior estimation of input parameters and the actual input parameters. Accordingly, the proposed framework subtracts the information contained in this relation to update the estimated input parameters and utilizes it in a model updating scheme to accurately approximate the real system outputs that are affected by all real input parameters (RIPs) of the problem. The proposed approach can effectively use limited experimental samples while maintaining prediction accuracy. It basically tweaks model parameters to update the computer simulation model so that it can match a specific set of experimental results. The significance and applicability of the proposed method is illustrated through comparison with a conventional model calibration scheme using two engineering examples.

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.

Implementation of an adaptive fuzzy compensator for coupled tank liquid level control system

In this paper, an adaptive fuzzy control (AFC) system is proposed to realize level position control of two coupled water tanks, often encountered in practical process control. The fuzzy control system includes an adaptive model identifier and controller. The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. Because the controller has a recursive form it treats model uncertainties and external disturbances in an implicit way. Thus there is no need to specify uncertainty and disturbances for this controller design in advance. A well-tuned conventional proportional integral (PI) controller is also applied to the two coupled tank system for comparison with the AFC system. Experimentation of the coupled tank system is realized in two different configurations, namely configuration #1 and configuration #2 respectively. In configuration #1, the water level in the top tank is controlled by a pump. In configuration #2, the water level in the bottom tank is controlled by the water flow coming out of the top tank. Experimental results prove that the AFC shows better trajectory tracking performance than PI controller in that the plant transient responses to the desired output changes have shorter settling time and smaller magnitude overshot/undershoot. Robustness of the AFC with respect to water level variation and capability to eliminate external disturbances are also achieved. Experimental results show that AFC is a strong and a practical choice for liquid level control.

Adaptive Output Trajectory Tracking Control for a Class of Affine Nonlinear Discrete-Time Systems

In this paper, we develop an adaptive output trajectory tracking control (AOTTC) method for a class of affine nonlinear discrete-time systems. The controller designed by this AOTTC method can make real-system output track the given expected trajectory asymptotically. Our method has some advantages: 1) it requires relatively few assumptions about the system model; 2) it can simplify the control problem by dynamically linearizing the system model and designing and adjusting the feedback gain matrix to adaptively stabilize the system online; and 3) it can estimate the system parameters by using an optimization scheme without using vast amounts of sampled data. This designing and adjusting procedure of the feedback gain matrix has a clear physical meaning and is easily applied. We also give out the convergence condition of the AOTTC method. Then, a voltage-controllable permanent magnet linear motor system is employed for computer simulation, whose results demonstrate the feasibility of our control method.

Adaptive estimator design for unstable output error systems: A test problem and traditional system identification based analysis

A key open question in adaptive estimator design is how to assure that the parameters of the proposed algorithms are converging to their almost correct solutions; hence, the learning algorithm is unbiased. Moreover, determining the speed of parameter convergence is important as it provides insight about the performance of the learning algorithms. The main contributions of the article are fourfold: the first one is that the article, initially, introduces an adaptive estimator to learn the discounted Q-function and approximate optimal control policy without requiring linear, discrete time, unstable output error system dynamics, but using only the noisy system measurements. The simulation results show that the adaptive estimator minimizes the stochastic cost function and temporal difference error and also learns the approximate Q-function together with the control policy. The second one is consideration of a different approach by taking a simple test problem to investigate issues associated with the Q-function’s representation and parametric convergence. In particular, the terminal convergence problem is analyzed with a known optimal control policy where the aim is to accurately learn only the Q-function. It is parameterized by terms which are functions of the unknown plant’s parameters and the Q-function’s discount factor, and their convergence properties are analyzed and compared with the adaptive estimator. The third one is to show that even though the adaptive estimator with a large Q-function discount factor yields larger control feedback gains, so that faster state converges upright, the learning problem is badly conditioned; hence, the parameter convergence is sluggish, as the Q-function discount factor approaches the inverse of the dominant pole of the unstable system. Finally, the fourth one is comparison of the state output learned by the adaptive estimator with the ones obtained from traditional system identification algorithms. Simulation result for a higher order unstable output error system shows that the adaptive estimator closely follows the real system output whereas the system identification algorithms do not.

Adaptive Optimal Digital PID Controller

It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

An intelligent self-learning method for dimensional error pre-compensation in CNC grinding

The problem of error compensation in manufacturing industry becomes important since the product quality is the manufactures’ competition. This paper presents an intelligent self-learning method for dimensional error pre-compensation in CNC grinding. Measurements of the system output obtained from the previous runs are used to compute the correction for the current run without in-process sensing and measurement, which is attractive for applications lacking in situ measurements. A modified NC program is fed to the machine tool to push the system output towards target. The simulation results and the real system input and output responses show the feasibility and effectiveness of this intelligent self-learning method.