Stochastic gradient-based particle filtering method for ARX models with nonlinear communication output submodel

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

Time-varying human-operator (HO) adaptation in basic manual-control tasks is barely understood, as most HO identification methods do not explicitly take into account time variations. An identification procedure based on both batch and recursive autoregressive exogenous (ARX) models is used in this paper for captur- ing HO adaptation to time-varying changes in controlled-element dynamics in compensatory tracking tasks. Conditions with constant controlled-element dynamics, matching recent experimental work, and gradual and sudden transitions in the controlled-element dynamics were compared by means of Monte Carlo simulations with different remnant noise intensities. Overall, with batch ARX model identification results for conditions with constant dynamics, it is shown that differences between remnant noise and HO dynamics, both of which are directly coupled in the ARX model structure, will bias the identified ARX model parameters. Nevertheless, recursive ARX model identification results were still found to provide a reasonably estimate of time-varying HO model parameters for the tested time-varying scenarios. A direct comparison of using a constant scalar forgetting factor and a constant forgetting matrix, containing separate forgetting factors for each ARX model parameter, showed that a forgetting matrix did not result in a significant improvement for the considered HO identification problem. A forgetting factor of = 0.99609, corresponding to a memory horizon of 256 samples at 100 Hz, is found to be optimal for all tested conditions. An evaluation of real experimental manual-control data shows that, with straightforward online implementation, the method has potential as a tool for investigating, modeling, and measuring operators’ adaptive control characteristics.

State of charge (SOC) is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX) model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC) cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.

The DSPM (Donnan Steric Pore Model) is integrated with osmotic pressure to develop nanofiltration modeling. The new model establishes the relation between the pressure across the membrane to the concentrations of permeate and retentate at a particular solute concentration in the feed. The model is successfully tested against the experimental data. We also developed the nanofiltration modeling with ARX (Auto Regressive Exogeneous), a type of black box model. The data for modeling is obtained by simulation of the first principle model. With the help of identification toolbox, an ARX model is developed considering pressure across the membrane as input variable (manipulated variable) and percentage rejection as output variable (control variable). The model is validated against a data subset used for estimation of model. However, the models are sustained only at low feed concentrations. The model output data are compared to the actual output data and calculate the mean square errors (MSE). The obtained residuals are well satisfactory in the range of 0.99. Hence, the derived modeling sustains for nanofiltration and its control operations.

In this work, a miniature-sized, radio controlled quadrotor is modeled and a black-box model is found using real-time flight data. The quadrotor which is used in this work is equipped with a special telemetry circuit to collect real-time data. Euler angles versus motor speeds data is used to identify the nonlinear rotational subsystem of the quadrotor as a quasi-linear ARX (auto-regressive exogenous) model. The ARX model performances are tested and found quite satisfactory.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

Photovoltaic (PV) cells are integral in harnessing solar energy, yet their performance is hindered by excessive heat generation, impacting efficiency and sustainability. Addressing the challenge of efficiency loss in photovoltaic (PV) cells due to overheating, this study focuses on optimizing active water cooling control for PV modules. The aim is to develop a dynamic, sustainable model and integrate a PID controller tuned by Sine Cosine Algorithm (SCA), targeting optimal operating temperatures. This study introduces a dynamic model and a closed-loop control system to manage PV cell temperature, investigating the correlation between water flow and temperature regulation. Experimental data is gathered using a pseudo-random binary sequence (PRBS) as an excitation signal, forming the foundation of an Auto Regressive eXogenous (ARX) model. The closed-loop system incorporates a PID controller and tuned using the Sine Cosine Algorithm (SCA) to optimize performance. The resulting model is rigorously validated through experimental investigation, demonstrating its precision in capturing the system’s dynamics. Moreover, the implementation of a controller-based cooling system substantiates the model’s practical efficacy. The research demonstrates significant improvements when implementing a controller-based water-cooling system for photovoltaic (PV) modules. Compared to the baseline scenario without cooling, the system achieves a 34.5% reduction in average PV temperature (from 59.2°C to 38.9°C) and a 9.46% increase in average power output (from 196.7W to 215.3W). Moreover, this system utilizes only 248.8 liters of water, marking a substantial 64% decrease in water consumption compared to traditional free-flow cooling methods, which use 790.9 liters. The research demonstrates that the controller-based cooling approach is a sustainable option, delivering power output comparable to the free-flow method, yet significantly lowering water consumption. This research signifies a turning point for sustainability, offering an efficient and water-conscious approach for enhancing PV system performance, a crucial step toward a greener and more environmentally responsible energy future.

One approach for State-of-health estimation onboard electric vehicles is to train a data-driven virtual battery on operational data and use this model, rather than the actual battery, for performance tests. A temperature-dependent continuous-time output-error (OE) model is proposed as virtual battery and identified and validated on real operational data from electric buses. The proposed model is compared to discrete-time and parameter-invariant models and shows better performance on all data sets. In addition, the OE model structure is shown to be superior to a conventional Auto Regressive eXogenous (ARX) model for the purpose of modeling the battery voltage response. Finally, challenges regarding vehicle log data are identified and improvements to the model are suggested in order to capture observed un-modeled phenomena.

Aiming to solve the problem of unknown parameters estimation in nonlinear and/or non-Gaussian dynamic system, Extension Particle Filtering(EPF) algorithm was proposed. EPF algorithm modeled unknown parameters by Gaussian random walk process, regarded unknown parameters in dynamic system as a part of state variations, and then estimated the state variations in extension nonlinear dynamic system by particle filtering algorithm. In order to improve the estimate precision of unknown parameters by utilizing observable information effectively, a new important density was purposed to instead of Bootstrap filter, further more, it avoided transformation about covariance. In order to solve the problem that covariance augmented infinitely with time in Gaussian random walk model, and Kernel smooth factor restrained covariance excessively so as to the values of estimate parameters could not access the values of true parameters sufficiently, the Gradually Reduce(GR) factor was purposed to instead of Kernel factor. At the end of this paper, the effectiveness and availability of purposed algorithm was validated by an exemplum.

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.

We propose a novel approach for modeling the end-to-end time-delay dynamics of the Internet using system identification, and apply it to control real-time telerobotic operations via Internet with specific Quality of Service (QoS) offerings. To do this, we have employed system identification techniques and the Auto-Regressive eXogeneous (ARX) model to obtain the values of relevant parameters and study their dynamics. The results show a periodic behavior of the Internet delay. Experimental measurements show the accuracy and usefulness of our theoretical derivations.

Internet-based teleoperation employs robots and internet a two breakthrough technologies to manipulate robots from distance for different applications. Variable and unknown time delay dynamics of internet is the main obstacle for realtime teleoperation via internet. In this paper the internet delay dynamics and its characteristics have been studied based on the measurement in different nodes. Then a black-box model for end-to-end internet delay dynamics has been developed using system identification and Auto-Regressive eXogeneous (ARX) model. Our experimental studies show a regular periodic behaviour in long-term intervals of internet delay variation and also confirm the accuracy and reliability of our theoretical and modelling derivations. This paper also introduces a novel multivariable control method for real-time telerobotic operations via Internet. Random communications delay of the Internet can cause instability in realtime closed-loop telerobotic systems. When a single identification model is used, it will have to adapt itself to the operating condition before an appropriate control mechanism can be applied. Slow adaptation may result in a large transient error. As an alternative, we propose to use a Multiple Model framework. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. We propose the use of Multi-Model Adaptive Control Theory and Multivariable Wave prediction method to capture the concurrency and complexity of Internet-based teleoperation. The results confirm the efficiency of the proposed technique in dealing with constant and variable delay dynamics of internet.

This paper presents the improvement of an ultrasonic pulse generator for a pipeline inspection gauge (PIG), which uses 64 transducers for inspecting distances up to 100 km with an axial resolution fixed at 3 mm and variable speeds between 0 and 2 m/s. An ultrasonic pulse generator is composed of a high-voltage (HV) MOSFETs, driver logic and an HV power supply. We used a DC-HV DC converter device as the HV power supply because it reduces the size of the ultrasound system considerably. However, pipeline geometry and inspection effects such as hammer and shock cause a variable pulse repetition frequency (PRF), producing voltage drops, poor quality of the HV pulse generated, failures in the dimensioning of defects and damage to devices by over-voltage. Our improvement is to implement a control scheme to maintain the high quality of the HV regardless of the variable PRF. To achieve this, we characterized three transfer functions of the DC-HV DC converter, varying the connected load to 10%, 45% and 80%. For the characterization, we used the least squares technique, considering an autoregressive exogenous (ARX) model. Later, we compared three control schemes: (1) proportional-integral-derivative (PID) tuned by simultaneous optimization of several responses (SOSR), (2) PID tuned by a neural network (NN) and (3) PI tuned by the analytical design method (ADM). The metrics used to compare the control schemes were the recovery time, the maximum over-voltage and the excess energy when the shock and hammer effects happen to occur. Finally, to verify the improvement of the HV pulser, we compared the ultrasonic pulses generated for various frequencies and amplitudes using the pulse generator with and without the control scheme.

This paper deals with the self-tuning control problem of linear systems described by autoregressive exogenous (ARX) mathematical models in the presence of unmodelled dynamics. An explicit scheme of control is described, which we use a recursive algorithm on the basis of the robustnessσ-modification approach to estimate the parameters of the system, to solve the problem of regulation tracking of the system. This approach was designed with the assumptions that the norm of the vector of the parameters is well-known. A new quadratic criterion is proposed to develop a modified recursive least squares (M-RLS) algorithm withσ-modification. The stability condition of the proposed estimation scheme is proved using the concepts of the small gain theorem. The effectiveness and reliability of the proposed M-RLS algorithm are shown by an illustrative simulation example. The effectiveness of the described explicit self-tuning control scheme is demonstrated by simulation results of the cruise control system for a vehicle.