Adaptive Recursive Research Articles

Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities

The rapid expansion of the Internet and communication technologies is leading to significant changes in both society and the economy. This development is driving the evolution of smart cities, which utilize cutting-edge technologies and data analysis to optimize efficiency and reduce waste in their infrastructure and services. As the number of mobile devices and embedded computers grows, new technologies, such as fifth-generation (5G) cellular broadband networks and the Internet of Things (IoT), are emerging to extend wireless network connectivity. These cities are often referred to as unmanned aerial vehicles (UAVs), highlighting their innovative approach to utilizing technology. To address the challenges posed by continuously varying perturbations, such as unknown states, gyroscopic disturbance torque, and parametric uncertainties, an adaptive recursive sliding mode control (ARSMC) has been developed. The high computational cost and high-order nonlinear behavior of UAVs make them difficult to control. The controller design is divided into two steps. First, a confined stability analysis is performed using controllability and observability to estimate the system’s stability calculation. Second, a Lyapunov-based controller design analysis is systematically tackled using a recursive design procedure. The strategy design aims to enhance robustness through Lyapunov stability-based mathematical analysis in the presence of considered perturbations. The ARSMC introduces new variables that depend on state variables, controlling parameters, and stabilizing functions to minimize unwanted signals and compensate for nonlinearities in the system. The paper’s significant contribution is to improve the controlled output’s rise time and stability time while ensuring efficient robustness.

Recursive adaptation in action: allochronic isolation and divergence of host‐associated populations of the apple maggot fly, Rhagoletis pomonella, following its recent introduction to the western USA

AbstractAn outstanding issue in the study of insect host races concerns the idea of ‘recursive adaptive divergence’, whereby adaptation can occur repeatedly across space and/or time, and the most recent adaptive episode is defined by one or more previously similar cases. The host plant shift of the apple maggot fly, Rhagoletis pomonella (Walsh) (Diptera: Tephritidae, Carpomyini), from ancestral downy hawthorn [Crataegus mollis (Torr. & A. Gray) Scheele] to introduced, domesticated apple (Malus domestica Borkh.) in the eastern USA has long served as a model system for investigating ecologically driven host race formation in phytophagous insect specialists. Here, we report results from an annual geography survey of eclosion time demonstrating a similar ecological pattern among nascent host‐associated populations of the fly recently introduced ca. 40 years ago from its native range in the east into the Pacific Northwest (PNW) region of the USA. Specifically, using data collected from 25 locations across 5 years, we show that apple‐infesting fly populations in the PNW have rapidly and repeatedly shifted (and maintained differences in) their adult eclosion life‐history timing to infest two novel hawthorn hosts with different fruiting phenologies – a native species (Crataegus douglasii Lindl.) and an introduced species (Crataegus monogyna Jacq.) – generating partial allochronic reproductive isolation in the process. The shifts in the PNW parallel the classic case of host race formation in the eastern USA, but have occurred bi‐directionally to two hawthorn species with phenologies slightly earlier (black hawthorn) and significantly later (ornamental hawthorn) than apple. Our results imply that R. pomonella can both possess and retain extensive‐standing variation (i.e., ‘adaptive memory’) in diapause traits, even following introductions, to rapidly and temporally track novel phenological host opportunities when they arise. Thus, ‘specialized’ host races may not constitute evolutionary dead ends. Rather, adaptive phenotypic and genetic memory may carry over from one host shift to the next, recursively facilitating host race formation in phytophagous insects.

Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Decentralized cooperative localization (DCL) is a promising method to determine accurate multirobot poses (i.e., positions and orientations) for robot teams operating in an environment without absolute navigation information. Existing DCL methods often use fixed measurement noise covariance matrices for multirobot pose estimation; however, their performance degrades when the measurement noise covariance matrices are time-varying. To address this problem, in this article, a novel adaptive recursive DCL method is proposed for multi-robot systems with time-varying measurement accuracy. Each robot estimates its pose and measurement noise covariance matrices simultaneously in a decentralized manner based on the constructed hierarchical Gaussian models using the variational Bayesian approach. Simulation and experimental results show that the proposed method has improved cooperative localization accuracy and estimation consistency but slightly heavier computational load than the existing recursive DCL method.

Pricing American Options under High-Dimensional Models with Recursive Adaptive Sparse Expectations*

Abstract We introduce a novel numerical framework for pricing American options in high dimensions. Our scheme manages to alleviate the problem of dimension scaling through the use of adaptive sparse grids. We approximate the value function with a low number of points and recursively apply fast approximations of the expectation operator from an exercise period to the previous period. Given that available option databases gather several thousands of prices, there is a clear need for fast approaches in empirical work. Our method processes an entire cross section of options in a single execution and offers an immediate solution to the estimation of hedging coefficients through finite differences. It thereby brings valuable advantages over Monte Carlo simulations, which are usually considered to be the tool of choice in high dimensions, and satisfies the need for fast computation in empirical work with current databases containing thousands of prices. We benchmark our algorithm under the canonical model of Black and Scholes and the stochastic volatility model of Heston, the latter in the presence of discrete dividends. We illustrate the massive improvement of complexity scaling over dense grids with a basket option study including up to eight underlying assets. We show how the high degree of parallelism of our scheme makes it suitable for deployment on massively parallel computing units to scale to higher dimensions or further speed up the solution process.

An Adaptive Recursive Wavelet Based Algorithm for Real-Time Measurement of Power System Variables During Off-Nominal Frequency Conditions

An adaptive wavelet-based method of phasor and frequency estimations and its application for online monitoring, control, and protective equipment are presented in this paper. The method is recursive with a low computational burden, suited for real-time implementations, and does not need any preprocessing stages. It computes the frequency of the analyzing signal accurately during nominal and off-nominal frequency conditions. To reduce computational errors of the phase angle and amplitude estimations that occur during off-nominal frequency conditions, the proposed method adapts itself according to the frequency drift. The proposed recursive adaptive method along with three other approaches based on recursive wavelet transform, discrete Fourier transform (DFT), and recursive DFT is implemented and compared using MATLAB, considering dynamic and static conditions. The methods are also employed for real-time computation of the phasor and frequency of the recorded data of the South West Interconnected System (SWIS) in Western Australia, and their efficiencies are assessed and compared. In addition, a real 33.3 MVA, 132/23.3 kV transformer located in SWIS is modeled in the electromagnetic transients program (EMTP) software, and the recorded signals have been employed for performance evaluation of the above-mentioned methods. The method is also implemented on a computer-based system in the laboratory and its performance is appraised accordingly. Extensive MATLAB simulations, laboratory implementation, field data, and EMTP results show that the proposed adaptive method has better static and dynamic performances in comparison to the other approaches.

Recursive Adaptive Sparse Exponential Functional Link Neural Network for Nonlinear AEC in Impulsive Noise Environment.

Recently, an adaptive exponential trigonometric functional link neural network (AETFLN) architecture has been introduced to enhance the nonlinear processing capability of the trigonometric functional link neural network (TFLN). However, it suffers from slow convergence speed, heavy computational burden, and poor robustness to noise in nonlinear acoustic echo cancellation, especially in the double-talk scenario. To reduce its computational complexity and improve its robustness against impulsive noise, this paper develops a recursive adaptive sparse exponential TFLN (RASETFLN). Based on sparse representations of functional links, the robust proportionate adaptive algorithm is deduced from the robust cost function over the RASETFLN in impulsive noise environments. Theoretical analysis shows that the proposed RASETFLN is stable under certain conditions. Finally, computer simulations illustrate that the proposed RASETFLN achieves much improved performance over the AETFLN in several nonlinear scenarios in terms of convergence rate, steady-state error, and robustness against noise.

Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking

The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo implementation of the PHD filter, known as the particle PHD filter, can give state estimates with nonlinear and non-Gaussian models. Recently, Mahler et al. have introduced a PHD smoother to gain more accurate estimates for both target states and number. However, as highlighted by Psiaki in the context of a backward-smoothing extended Kalman filter, with a nonlinear state evolution model the approximation error in the backward filtering requires careful consideration. Psiaki suggests that to minimize the aggregated least-squares error over a batch of data. We instead use the term retrodiction PHD filter to describe the backward filtering algorithm in recognition of the approximation error proposed in the original PHD smoother, and we propose an adaptive recursion step to improve the approximation accuracy. This step combines forward and backward processing through the measurement set and thereby mitigates the problems with the original PHD smoother when the target number changes significantly and the targets appear and disappear randomly. Simulation results show the improved performance of the proposed algorithm and its capability in handling a variable number of targets.

Pricing American Options Under High-Dimensional Models with Recursive Adaptive Sparse Expectations

We introduce a novel numerical framework for pricing American options in high dimensions. Such settings naturally arise for derivatives with multiple underlying assets, like basket options. They are equally important for single-asset options because high-dimensional models are best capable of capturing observed price dynamics. Yet, higher-dimensional settings come at the cost of a loss of tractability due to the accompanying exponential growth of computational complexity. Our scheme manages to alleviate the problem of dimension scaling through the use of adaptive sparse grids. We approximate the value function with a low number of points and recursively apply fast approximations of the expectation operator from an exercise period to the previous one. The algorithm copes with discretely spaced, possibly nonuniform, time grids. This makes it particularly fast for options with a limited number of exercise periods, like Bermudan options, and options for which the optimal exercise schedule is known ex ante. As compared to Monte Carlo simulations, our scheme processes an entire cross section of options in a single execution. It thereby offers an immediate solution to the estimation of hedging coefficients through finite differences and is ideal when multiple related options need to be analyzed. The algorithm is also capable of dealing with discrete dividends with no performance deterioration, thus improving on the documented inefficiency of exercise policies under continuous dividend yield approximations. We benchmark our algorithm under both the canonical model of Black and Scholes and the stochastic volatility model of Heston in the presence of discrete dividends. We illustrate the massive improvement of complexity scaling over dense grids with a basket option study including up to eight underlying assets. We show how the high degree of parallelism of our scheme makes it suitable for deployment on massively parallel computing units to scale to higher dimensions or further speed up the solution process.

Compensate Voltage Sags in Series Transformers of Dynamic Voltage Restorers for Preventing Saturation

This paper proposes a procedure for preventing saturation in series transformers from Dynamic Voltage Restorer (DVR) systems. Power quality has been an issue that is becoming increasingly pivotal in modern industrial and commercial applications. Voltage disturbances especially the voltage sag and swell are the most common power quality problems due to increased use of a large numbers of sophisticated and sensitive electronic equipment in industrial systems. The method consists in correcting the voltage which is injected through the transformers into the power system to compensate voltage sags. To overcome this problem, custom power devices are used. One of the devices is the Dynamic Voltage Restorer (DVR), which is the most efficient and effective modern custom power device used in power distribution networks. It is a series connected power electronic based device that can quickly mitigate the voltage sags in the system and restore the load voltage to the pre-fault value. Moreover, the technique allows a certain level of sag compensation even when the estimated flux is expected to exceed the saturation limit. The voltage sag level and phase are computed through an adaptive Recursive Least Squares (RLS). In RLS Method we have to use 4-leg voltage source inverter and it can be slow process to compensate the voltage sag occur in the power system. In extension method, the Adaptive Network based Fuzzy Inference System (ANFIS) is used to reduces the voltage sag in the power system network. In these method, the 3-leg voltage source inverter as comparing to the RLS algorithm. The ANFIS system is faster and accurate to compensate voltage sags in power system. Total harmonics distortions is half of the deduced in the system.Keywords: Adaptive System Based Fuzzy Interface System (ANFIS), Dynamic Voltage Restorer (DVR), Recursive Least Squares (RLS), Saturation, Series Transformers, Voltage Sag

Adaptive informative path planning in metric spaces

In contrast to classic geometric motion planning, informative path planning (IPP) seeks a path for a robot to sense the world and gain information. In adaptive IPP, the robot chooses the next sensing location conditioned on all information acquired so far, and the robot’s goal is to minimize the travel cost required for identifying a true hypothesis. Adaptive IPP is NP-hard, because the robot must trade-off information gain and travel cost optimally. In this paper we present Recursive Adaptive Identification (RAId), a new polynomial-time approximation algorithm for adaptive IPP. We prove a polylogarithmic approximation bound when the robot travels in a metric space. Furthermore, our experiments suggest that RAId is practical and provides good approximate solutions for two distinct robot planning tasks. Although RAId is designed primarily for noiseless observations, a simple extension allows it to handle some tasks with noisy observations.

Adaptive Recursive Prediction of the Desired Torque of a Hybrid Powertrain

Hybrid powertrain provides driving torque based upon the vehicle acceleration pedal position. Due to the slow response of the internal combustion (IC) engine, the electric motor(s) (EM) often has to provide additional torque during transient vehicle operations. During cold-start operations, the available battery power is significantly reduced due to cold temperatures, and due to the starting delay of the IC engine, the battery has to provide power at the level beyond its normal operation limit for the electrical motor to meet the torque demand, which would dramatically shorten the battery life. However, if the desired powertrain torque can be predicted, the IC engine could be started early when the predicted future powertrain power is higher than the battery normal operational range, and hence, the duration of battery operated over its normal operational range could be significantly reduced, leading to extended battery life. This makes predicting multistep future torque demand, based upon current and past torque demand data, an important subject. Amid many existing studies, few research studies were focused on this subject. This paper proposes an adaptive recursive prediction algorithm to estimate the future desired powertrain torque and compares the performance of the proposed algorithm with two existing prediction algorithms, i.e., step-by-step and fixed-gain algorithms. These three prediction algorithms are studied under the federal test procedure (FTP) and another four typical driving cycles. The simulation results show that the adaptive recursive prediction algorithm reduces the prediction error significantly with a 4% maximum prediction error, and in addition, the reduced computational load makes it feasible for real-time implementation. The introduced weighting factors for the past and current data are the key to improve prediction accuracy and avoid prediction calculation over- and underflow. In addition, due to the online update of regression gain, the proposed algorithm is also robust to driving behavior variations.

Design Realization and Performance Evaluation of an Acoustic Echo Cancellation (AEC) in Hands-Free Communications Using Recursive Adaptive Algorithm

Design Realization and Performance Evaluation of an Acoustic Echo Cancellation (AEC) in Hands-Free Communications Using Recursive Adaptive Algorithm

Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning.

Mutual information, a general measure of the relatedness between two random variables, has been actively used in the analysis of biomedical data. The mutual information between two discrete variables is conventionally calculated by their joint probabilities estimated from the frequency of observed samples in each combination of variable categories. However, this conventional approach is no longer efficient for discrete variables with many categories, which can be easily found in large-scale biomedical data such as diagnosis codes, drug compounds, and genotypes. Here, we propose a method to provide stable estimations for the mutual information between discrete variables with many categories. Simulation studies showed that the proposed method reduced the estimation errors by 45 folds and improved the correlation coefficients with true values by 99 folds, compared with the conventional calculation of mutual information. The proposed method was also demonstrated through a case study for diagnostic data in electronic health records. This method is expected to be useful in the analysis of various biomedical data with discrete variables.

Evolutionary computing based approach for the removal of ECG artifact from the corrupted EEG signal.

Electroencephalogram (EEG) is an important tool for clinical diagnosis of brain-related disorders and problems. However, it is corrupted by various biological artifacts, of which ECG is one among them that reduces the clinical importance of EEG especially for epileptic patients and patients with short neck. To remove the ECG artifact from the measured EEG signal using an evolutionary computing approach based on the concept of Hybrid Adaptive Neuro-Fuzzy Inference System, which helps the Neurologists in the diagnosis and follow-up of encephalopathy. The proposed hybrid learning methods are ANFIS-MA and ANFIS-GA, which uses Memetic Algorithm (MA) and Genetic algorithm (GA) for tuning the antecedent and consequent part of the ANFIS structure individually. The performances of the proposed methods are compared with that of ANFIS and adaptive Recursive Least Squares (RLS) filtering algorithm. The proposed methods are experimentally validated by applying it to the simulated data sets, subjected to non-linearity condition and real polysomonograph data sets. Performance metrics such as sensitivity, specificity and accuracy of the proposed method ANFIS-MA, in terms of correction rate are found to be 93.8%, 100% and 99% respectively, which is better than current state-of-the-art approaches. The evaluation process used and demonstrated effectiveness of the proposed method proves that ANFIS-MA is more effective in suppressing ECG artifacts from the corrupted EEG signals than ANFIS-GA, ANFIS and RLS algorithm.

Online identification of semi-empirical model parameters for PEMFCs

In this paper, a semi-empirical model has been proposed for online identification in order to improve the performances of Fuel Cell Vehicles (FCV). The Adaptive Recursive Least Square (ARLS) method has been considered to update the semi-empirical model parameters online to cope with Fuel Cell System (FCS) parameter variations. After introducing a literature review of the semi-empirical model for online identification to show its importance. A Matlab/Simulink model has been designed to define the initial parameters of the ARLS algorithm for the online identification in real time. Then, an experimental test bench has been performed to verify the ARLS algorithm in real time on a Proton Exchange Membrane Fuel Cell (PEMFC). In order to simplify the semi-empirical model, an extensive ANalysis Of VAriance (ANOVA) technique has been used to evaluate the impact of each model parameter. It has been confirmed that the ARLS algorithm is suitable to take into account the operating parameters that changes the PEMFC performance. The simulation using ANOVA results has shown that a parameter with relative low impact (mass transport) is very relevant for the energy management. Therefore, the proposed study can be used for the control and energy management of the Hydrogen Research Institute FCV taking into account the influence of operating conditions.

Adaptive RLS-Received Signal Strength Algorithm in Wireless Network Area for Multi-Mobile Nodes Location Estimation System

the current Received Signal Strength based positioning systems have been designed to monitor the location information of mobile nodes and Patients. The positioning systems are designed with different wireless communication technologies and adapted algorithms in wireless network area. The adaptive algorithms improve the accuracy of location of mobile nodes in RSS (Received Signal Strength) based positioning systems. The proposed work introduced adaptive Recursive Least Square - Received Signal Strength (RLS-RSS algorithm) algorithm to reduce the effect of multipath propagation in Trilateration Localization system. In the proposed work the entire three reference node receives the RSS-values from the multi-mobile nodes and then RLS- RSS algorithm is used to estimate the RSS-values at each reference node. The estimated RSS-value provides the coordinates of multi-mobile nodes. From the simulation results it is shown that the accuracy of the coordinate's point of the multi-mobile nodes is improved for different environments.

Adaptive Recursive Least Squares Algorithm based on Echo State Neural Network

In order to model time-varying dynamical systems with more accuracy, and to further exploit the potential capacities of recurrent neural networks, we propose a novel recursive least square (RLS) algorithm based on echo state network (ESN), and note it as RLSESN in this paper. ESN is a new paradigm for using recurrent neural networks (RNN) with a simpler training method. RLSESN consists of three main components: an ESN, a recursive least square (RLS) algorithm with adaptive forgetting factor and a change detection module. At first, the change detection module modifies the forgetting factor online according to ESN output errors. And then, the RLS algorithm regulates the ESN output connection weights. The simulation experiment results show that the proposed ESN-based filters can model nonlinear time-varying dynamical systems very well; the modeling performances are significantly better than those autoregressive moving average (ARMA) model based filters.

On the Recursive Adaptive Control for Free-floating Space Manipulators

This paper is devoted to investigating the recursive implementation schemes of adaptive control for free-floating space manipulators. Using spatial vector tool and some physical properties that free-floating space manipulators enjoy, we establish a general framework on the seeking of the centripetal and Coriolis matrix that satisfies the skew symmetry requirement. Under this general framework, we propose a recursive adaptive algorithm for free-floating manipulators, which is composed of two parts: the first part is the recursive derivation of the required manipulator control torques, and the second part is the recursive updating of the spacecraft reference velocity and acceleration. To guarantee the uniform positive definiteness of the estimated spacecraft inertia, we present a parameter projection algorithm to project the estimated parameters into some pre-specified parameter region. In the next, we extend the proposed recursive adaptive algorithm to task-space control of free-floating space manipulators. We examine the performance of the proposed recursive adaptive algorithms via numerical simulation on a six-DOF space manipulator.

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth’s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.

Online Aerodynamic Model Structure Selection and Parameter Estimation for Fault Tolerant Control

This paper describes a new recursive algorithm for the approximation of time varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called Adaptive Recursive Orthogonal Least Squares (AROLS), and is an extension and modification of the classical Recursive Orthogonal Least Squares (ROLS). This algorithm is considered to be particularly useful for indirect fault tolerant flight control, making use of model based adaptive control routines. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on some simulation data sets.