The smooth variable structure filter: A comprehensive review

The smooth variable structure filter (SVSF) is a type of sliding mode filter formulated in a predictor-corrector format and has seen significant development over the last 15 years. In this paper, we provide a comprehensive review of the SVSF and its variants. The developments, applications and improvements of the SVSF in terms of robustness and optimality are investigated. In addition, the combination of the SVSF with different filtering strategies is considered in an effort to improve estimation accuracy while maintaining robustness to model uncertainty. State estimation techniques such as the unscented and cubature Kalman filters (UKF & CKF), SVSF, the combination of SVSF with UKF (UK-SVSF), and the combination of CKF with SVSF (CK-SVSF) are applied on a 4-DOF industrial robotic arm. The SVSF state estimation performance is examined under different operating conditions. The results of these filters have been compared based a number of statistics such as the root mean squared error (RMSE) and mean absolute error (MAE), among others. It is shown that the UK-SVSF and CK-SVSF strategies acquire the best performance in the presence of uncertainties

A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability

This article proposes a novel adaptive Kalman filter (AKF) to estimate the unknown probability of measurement loss using the interacting multiple-model (IMM) filtering framework, yielding the IMM-AKF algorithm. In the proposed IMM-AKF algorithm, the state, Bernoulli random variable, and measurement loss probability are jointly inferred based on the variational Bayesian (VB) approach. In particular, a new likelihood definition is derived for the mode probability update process of the IMM-AKF algorithm. Experiments demonstrate the superiority of the proposed IMM-AKF algorithm over existing filtering algorithms by adaptively estimating the unknown time-varying measurement loss probability

LED Reliability Assessment Using a Novel Monte Carlo-Based Algorithm

Application of Monte Carlo (MC) simulations in the statistical analysis of LED lumen maintenance is presented in this paper. Lumen maintenance data is acquired using experimental tests accomplished in the electro-optics laboratory of the Mazinoor lighting industry, which is an accredited laboratory by Iranian National Standards organization. The sampling rate and the duration of the experiments are consistent with LM-80-15 standard introduced by the Illumination Engineering Society of North America. In some cases, due to the existence of nonlinear dynamics in real trends of light flux, particularly in the first 1,000 hours, features are not completely captured using traditional reliability assessment techniques such as TM-21. In this study, a two-phase model is applied to cover features in lumen maintenance data. Furthermore, to estimate the parameters of the dedicated model in mild and severe operating conditions, a nonlinear Kalman filter-based method known as the iterated extended Kalman filter (IEKF) is used. A set of MC simulations are run to construct the probability density functions (PDFs) for the estimated parameters. Each simulation uses different values of the parameters chosen from the corresponding distribution. Finally, lifetime PDFs are constructed to extract reliability indices. All of the simulations are conducted in MATLAB and the results are compared with the conventional and well-known TM-21 approach

Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm

The significant global trend towards solar energy has led to the development of studies on the fabrication of high-performance solar cells. Accurate modeling and parameter identification of solar cells is of paramount importance. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). Each model has a number of unknown parameters and several methods have been presented in the literature to find their optimal values. In this paper, an efficient optimization algorithm, namely Whippy Harris Hawks Optimization (WHHO), is proposed to estimate the model parameters of solar systems. The proposed WHHO is an enhanced version of the HHO algorithm and has the advantages of high convergence speed, global search capability, and high robustness over the original method. To evaluate the efficiency of the proposed WHHO algorithm, it is utilized to identify the parameters of various models of solar cells, and photovoltaic (PV) module. The results are compared with those obtained from a number of other recently presented optimization methods, which shows the superiority of the proposed algorithm. Furthermore, the effectiveness of WHHO algorithm in the practical application has been assessed for the parameter estimation of three commonly-used commercial modules under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm

A hybrid intelligent busbar protection strategy using hyperbolic S‐transforms and extreme learning machines

In power systems, busbars connect important components such as generators, transmission lines, and loads. A typical fault occurrence on the busbar may result in the isolation of faulty sections from other normally operating parts of the system resulting from differential protection operation. Although the main busbars' protection scheme is differential protection, its operation is significantly affected by magnetic saturation of the current transformer (CT), particularly during external fault occurrence or energizing power transformers. Saturation of the CT may generate a spurious differential current and is the main reason for the differential scheme malfunctioning. Previous research presented different methods to modify and improve busbars' differential protection scheme. However, there has been lack of a comprehensive study to assess the efficiency of the busbar protection scheme regarding all involved, and influencing aspects including various fault types, energizing power transformer, (high) fault resistance, fault angle (changing from 0° to 360°), and the angle of the sources. Thus, in this study, a hybrid intelligent busbar protection scheme is proposed and the effects of all these factors are investigated. The proposed strategy utilizes the hyperbolic S-transform as a signal processing technique to extract an efficient feature that is able to discriminate internal faults from other abnormal modes, that is, external faults and inrush current under CT saturation. To obtain this goal, a learning-based classification method known as extreme learning machines is used to classify the system conditions based on the selected features. The proposed protection scheme was found to have low sensitivity to CT saturation and noise and was able to accurately detect internal faults from half a cycle to one cycle of the power system depending on the fault resistance

Smart agriculture: Development of a skid-steer autonomous robot with advanced model predictive controllers

The agricultural domain has been experiencing extensive automation interest over the past decade. The established process for measuring physiological and morphological traits (phenotypes) of crops is labour-intensive and error-prone. In this paper, a mobile robotic platform, namely The Autonomous Robot for Orchard Surveying (AROS), was developed to automate the process of collecting spatial and visual data autonomously. Furthermore, six different control frameworks are presented to evaluate the feasibility of using a kinematic model in agricultural environments. The kinematic model does not consider wheel slippage or any forces associated with dynamic motion. Thus, the following six controllers are evaluated: Proportional-Derivative (PD) controller, Sliding Mode Controller (SMC), Control-Lyapunov Function (CLF), Nonlinear Model Predictive Controller (NMPC), Tube-Based Nonlinear Model Predictive Controller (TBNMPC), and Model Predictive Sliding Mode Control (MPSMC). This paper provides insight into the degree of disturbance rejection that the mentioned control architectures can achieve in outdoor environments. Experimental results validate that all control architectures are capable of rejecting the present disturbances associated with unmodelled dynamics and wheel slip on soft ground conditions. Additionally, the optimal-based controllers managed to perform better than the non-optimal controllers. Performance improvements of the TBNMPC of up to 209.72% are realized when compared to non-optimal methods. Results also show that the non-optimal controllers had low performance due to the underactuated constraint present in the kinematic model

Self-harming behaviors among forensic psychiatric patients who committed violent offences: an exploratory study on the role of circumstances during the index offence and victim characteristics

Background: Self-harming behaviors are common among forensic patients with violent index offenses. While various factors, including feelings of shame and guilt, may influence self-harming behaviors, little is known about how the circumstances surrounding the index offense and the victims’ characteristics affect self-harming tendencies among forensic patients. In this study, we examined the association of the circumstances surrounding the index offence and victim characteristics with self-harming behaviors among forensic patients who have committed violent offences. Methods: The present study consisted of 845 forensic psychiatric patients under the Ontario Review Board who had violent offences (Mean age = 42.13 ± 13.29; 85.68% male) in the reporting year 2014/15. The study examined the association between self-harming incidents with the circumstances during the index offense and victims’ characteristics while controlling for clinical and demographic factors based on multiple hierarchical negative binominal regression. Results: The prevalence of self-harm was 4.14%, and more than half (61.29%) of the patients with self-harming behaviors had multiple incidents. The total number of self-harming incidences recorded in the reporting year was 113. The results showed that of the overall 24.05% explained by the models, the victim’s characteristics contributed approximately 5% points, and circumstances during the index offence contributed an additional 2% points in explaining self-harming behaviors among forensic psychiatric patients during the reporting year. In the final model, the risk of self-harm increased with having a victim who was a healthcare/support staff or a co-patient/cohabitant. Conclusion: Self-harm among forensic patients who committed violent offences is associated with various factors, including previous history of self-harm and the victim’s characteristics, especially when the victim was a healthcare/support worker or co-patient. These findings suggest that self-harm might be a maladaptive way of coping with negative emotions, such as feelings of guilt and shame triggered by harming others. Mitigating measures for self-harm among patients with violent offences need to be robust and individualized, taking into consideration vulnerability issues and the best available evidence

Use of the Morphoedaphic Index to Predict Nutrient Status and Algal Biomass in Some Canadian Lakes

The assumptions included the relationship between mean depth of lakes and various hydrologic characteristics (flushing rate and stratification regime), water transparency characteristics (water color and turbidity), and the stoichiometric relationship among ions (expressed as a proportion between TDS and the concentration of primary nutrients, total phosphorus (TP) and total nitrogen (TN). Although these basic assumptions could be supported empirically, the predictive power of the MEI became progressively weakened with increasing trophic level. MEI accounted for up to 85% of the variation in TP and TN, less than 50% of the variation in [Chl a], and none of the variation in the biomass of herbivorous zooplankton. The functions relating TDS to both TP and TN were fundamentally different: as lakes increased in salinity, the TN:TP ratio decreased dramatically so that TP almost exceeded TN concentrations in extremely saline lakes. This necessitated the development of separate MEI-nutrient relationships for saline (TDS > 1000 mg/L) and nonsaline lakes. -from Autho

Motion Control of a Differential Drive Mobile Robot Considering Voltage and Current Limits

The mobile robot trajectory tracking problem, whereby a controller is responsible for ensuring a robot follows a predetermined trajectory is investigated in this work. Several different algorithms are implemented as the controller for a differential drive wheeled robot in this study, and their performances are examined across different operating conditions using several performance measures. Specifically, we implement proportional, integral, and derivative controls, as well as sliding-mode control and model predictive control, and observe their control performance in ideal, tuned operating conditions, as well as in the face of varying levels of sensor noise, actuator saturation due to voltage and current constraints, or wheel slippage in one or both wheels. Background on the kinematic model of the differential drive wheeled robot as well as the implementation and tuning of the controllers are included in this work. Furthermore, we present a discussion of the advantages and limitations of each controller in the face of varying circumstances for the task of controlling a differential drive wheeled robot