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Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy. [ABSTRACT FROM AUTHOR]

To address vehicle drivetrain vibrations that cause discomfort and poor drivability, this study proposes a new active damping strategy with simple backlash compensation based on the simultaneous perturbation stochastic approximation (SPSA) with norm-limited update vector. First, an experimental device developed for a simplified drivetrain mechanism is demonstrated. A mechanism for reproducing both the contact mode and the backlash mode is included in the device. For the contact mode, a model-based H 2 controller is employed as the baseline damping strategy. Further, to mitigate the backlash effect, a simple algorithm based on mode-switching-based compensation is used with the H 2 controller. In particular, for the critical controller parameters, this article presents a systematic design approach to search for their optimal values. The key parameters, which are needed for the backlash and contact mode controllers, are simultaneously auto-tuned using norm-limited update vector-based SPSA, which ensures the stability in the iterative tuning. The novelty of this study is that both the backlash mode controller and the contact mode controller are simultaneously optimized by the improved version of SPSA, thus realizing a comprehensive auto-tuning design of an active drivetrain damping system. Finally, the active controller is experimentally verified using the actual test device. Comparative studies show that the proposed approach significantly reduces drivetrain vibrations and is robust against fluctuations in the backlash. [ABSTRACT FROM AUTHOR]

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPSs). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPSs. The Affinity Propagation clustering approach is utilized to determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy.

This study focuses on the imbalance of vehicles in one-way station-based electric carsharing systems (OSECSs). A simulation-based optimization framework is proposed to determine optimal OSECS operation strategies, including dynamic pricing, vehicle relocation, and staff rebalancing. In this framework, the simulation module considers the nonlinear charging profiles for electric vehicles (EVs), EV-dependent road congestion, and price-dependent elastic demands to evaluate the performance of operation strategies. The optimization module triggered by the simulation model provides the optimized configuration of operation strategies based on the genetic algorithm and simultaneous perturbation stochastic approximationTo eliminate the decision variable combinatorial explosion for the optimization problem, we apply the K-means clustering algorithm to cluster stations into small groups of cluster zones, reducing the dimensions of decision variables. A case study in Chengdu demonstrates the proposed simulation-optimization framework's efficiency and further analyzes the impact of different operation strategies, nonlinear charging mode, and EV-dependent road congestion for the OSECSs. [ABSTRACT FROM AUTHOR]

Parameter calibration of the traffic assignment models is vital to travel demand analysis and management. As an extension of the conventional traffic assignment, boundedly rational activity-travel assignment (BR-ATA) combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns. The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models. In virtue of the multi-state supernetwork, this exploratory study formulates the BR-ATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem. Considering the temporal dimension, we also propose a dynamic formulation of the BR-ATA calibration problem. The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems. Numerical examples are presented to calibrate the activity-based travel demand for illustrations. The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.

Dual earthquake-resistant archetypes suffer residual damage following a severe earthquake leading to socio-economic drawbacks. A vertical isolated rocking core-moment frame (VI-RCMF) provides a resilience technique to curtail the seismic vulnerability using the isolation of subsystems. Viscous dampers, installed at each floor level of the VI-RCMF archetype, separate moment frame (MF) from rocking-core (RC). The self-centering RC performs as a passive strong-back core, which can uplift on its toes. Although the VI-RCMFs have exhibited superior performance compared to non-isolated dual frames, there are analytical challenges to come up with the optimal design due to the complex interaction of subsystems. This paper presents an optimization framework for quantifying the optimal design parameters of VI-RCMFs. Accordingly, the simultaneous perturbation stochastic approximation (SPSA) method is employed as the optimization algorithm. The objective function is defined to minimize the displacement of MF and the design vector includes mass, stiffness, and damping ratios. The SPSA optimization analyses are conducted for a set of archetypes using OpenSees software. Optimal normalized responses of subsystems are computed for 44 far-field ground motions and aleatory uncertainties are quantified for optimal design variables. The results demonstrate the effectiveness of the proposed procedure for optimizing VI-RCMFs. The derived optimal design vector can be used for the preliminary design of low-to mid-rise archetypes. [ABSTRACT FROM AUTHOR]

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3 σ bound. [ABSTRACT FROM AUTHOR]

For enhancement of the drivability and lifetime of the components, reduction in vehicle driveline oscillations has been addressed by advanced active control algorithms. However, most of the existing works involve subjective determination of their critical controller parameters, imposing heavy adjustment tasks on designers. This research presents an efficient tuning algorithm of the model-based driveline vibration controller that explicitly considers the adverse influences due to nonlinear backlash. First, a driveline dynamics model with a dead-zone effect of backlash is established. A dynamic output feedback $H_{2}$ controller is designed as a baseline controller to attenuate the low-frequency resonance of a driveline. A simple control mode switching algorithm is combined with the controller to deal with the backlash nonlinearity. The optimal values of their important design parameters included in the active control system are automatically searched by a computationally efficient algorithm, i.e., the simultaneous perturbation stochastic approximation (SPSA). The proposed active oscillation controller tuned by the SPSA is validated via several simulation tests. The robustness is evaluated for various patterns of driveline dynamics fluctuations such as the model parameters, the length of backlash, and the driving conditions. Moreover, the proposed controller is compared to traditional active controllers including a proportional-integral-differential (PID) controller tuned by the Ziegler-Nichols method. As a result, the improvement of the vibration suppression performance as well as its robustness originating from the compensation for backlash is revealed.

In order to realize the real-time control of photovoltaic power generation smoothly connected to the grid under the condition that the energy storage equipment can operate safely, a control strategy combining the simultaneous perturbation stochastic approximation (SPSA) algorithm with rule control is designed. Firstly, the photovoltaic data are processed to extract the data characteristics of the power ramp, and then the grid-connected reference power is obtained through SPSA algorithm. Secondly, considering the state of charge (SOC) of energy storage equipment and the safe operating power range of hydrogen storage equipment, 24 hybrid energy storage power allocation rules are formulated by using the rule control method. Finally, according to the sampling data of every 10 s interval in typical day, the simulation is carried out on MATLAB/simulink platform. The results show that, compared with the first-order low-pass filtering algorithm and recursive fuzzy neural network (RFNN) algorithm, the SPSA algorithm has stronger smoothing ability, and the rule control can also complete the allocation according to the characteristics of the hybrid storage device while ensuring its normal operation.

Using counterdiabatic (CD) driving—aiming at suppression of diabatic transition—in digitized adiabatic evolution has garnered immense interest in quantum protocols and algorithms. However, improving the approximate CD terms with a nested commutator ansatz is a challenging task. In this work, we propose a technique of finding optimal coefficients of the CD terms using a variational quantum circuit. By classical optimization routines, the parameters of this circuit are optimized to provide the coefficients corresponding to the CD terms. Then their improved performance is exemplified in Greenberger–Horne–Zeilinger state preparation on the nearest-neighbour Ising model. Finally, we also show the advantage over the usual quantum approximation optimization algorithm, in terms of fidelity with bounded time. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'. [ABSTRACT FROM AUTHOR]

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3σ bound.

Motivated by MMS mission observations near magnetic reconnection sites, we have developed a new empirical reconstruction (ER) model of the three-dimensional (3D) magnetic field and the associated plasma currents. Our approach combines both the measurements from a constellation of satellites and a set of physics-based equations as physical constraints to build spatially smooth distributions. This ER model directly minimizes the loss function that characterizes the model-measurement differences and the model departures from linear or nonlinear physical constraints using an efficient stochastic optimization method by which the effects of random measurement errors can be effectively included. Depending on the availability of the measured parameters and the adopted physical constraints on the reconstructed fields, the ER model could be either slightly over-determined or under-determined, yielding nearly identical reconstructed fields when solved by the stochastic optimization method. As a result, the ER model remains valid and operational even if the input measurements are incomplete. Two sets of new indices associated respectively with the model-measurement differences and the model departures are introduced to objectively measure the accuracy and quality of the reconstructed fields. While applying the reconstruction model to observations of an electron diffusion region (EDR) observed by NASA’s Magnetospheric Multiscale (MMS) mission, we examine the relative contributions of the errors in the plasma current density arising from random measurement errors and linear approximations made in application of the curlometer technique. It was found that the errors in the plasma current density calculated directly from the measured magnetic fields using a linear approximation were mostly contributed from the nonlinear configuration of the 3D magnetic fields.

Elasticsearch is currently the most popular search engine for full-text database management systems. By default, its configuration does not change while it receives data. However, when Elasticsearch stores a large amount of data over time, the default configuration becomes an obstacle to improving performance. In addition, the servers that host Elasticsearch may have limited resources, such as internal memory and CPU. A general solution to these problems is to dynamically tune the configuration parameters of Elasticsearch in order to improve its performance. The sheer number of parameters involved in this configuration makes it a complex task. In this work, we apply the Simultaneous Perturbation Stochastic Approximation method for optimizing Elasticsearch with multiple unknown parameters. Using this algorithm, our implementation optimizes the Elasticsearch configuration parameters by observing the performance and automatically changing the configuration to improve performance. The proposed solution makes it possible to change the configuration parameters of Elasticsearch automatically without having to restart the currently running instance of Elasticsearch. The results show a higher than 40% improvement in the combined data insertion capacity and the system's response time.

• An efficient simulation system is proposed for the urban rail networked operation. • The interaction between passenger and train is delicately depicted in simulation. • A simulation-based constrained SPSA algorithm is used to coordinate train timetables. • The improved solution reduces passenger waiting time without adding operating cost. Synchronous optimization of train timetables is to solve the problem of train service coordination among different lines in the urban rail transit network, which can reduce passenger waiting time and subsequently enhance passenger satisfaction. This paper develops an efficient simulation system to reproduce the train operation of an urban rail network. It takes into account the realistic characteristics of urban rail operations, including time-dependent demand, dynamic dwell time, and train capacity constraint. In the simulations, both the train trajectories and the passenger waiting time can be easily obtained. On this basis, a simulation-based algorithm (i.e., simultaneous perturbation stochastic approximation, SPSA) is designed to search an improved flexible-interval dispatch solution in the network. A case study of the Chengdu urban rail transit network is implemented to verify the effectiveness of the simulation system and the optimization algorithm. The simulation system is found to be time efficient since it takes less than 1 s to simulate the network with 4 h of operation and more than 200,000 passengers. The optimization algorithm is also effective as it can reduce the average passenger waiting time by 9.52 % compared to the equal-interval dispatch solution without increasing the train service frequency. It is also found that the reduction in waiting time is mainly due to the improved coordination of train services in the transfer station. [ABSTRACT FROM AUTHOR]

In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies iterative learning control (ILC) using faded measurements without system information. The measurements are transmitted through fading channels, where the fading phenomenon is modelled by a multiplicative random variable. The system matrices are assumed unknown a priori and a random difference technique is applied to estimate the gradient using the available tracking data. An online ILC algorithm is established with strict convergence analysis along the iteration axis, followed by practical variants and discussions. The generated input sequence is proved to converge to the desired one in the almost sure sense. Illustrative simulations are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

Summary: Our problem of interest is to minimize a sequence of scalar‐valued loss functions fk(·) or to locate the root(s) of a sequence of vector‐valued functions gk(·) corresponding to time τk with respect to a parameter θ∈ℝp. The available information for the minimization/root‐finding is the noise‐corrupted observation(s) of either fk(·) or gk(·) evaluated at a few of certain design points only. Taking both the dynamics and randomness into consideration, we use stochastic approximation (SA) algorithms to solve the time‐varying problem, but with a nondecaying gain (stepsize). The tracking performance of the nondecaying gain SA algorithm is guaranteed by a computable bound for the root‐mean‐squared error and mean‐absolute‐deviation. The only assumption imposed on the drifts is that the average distance between two consecutive underlying true parameters is bounded from above—this assumption allows the varying target to change abruptly. The error term arising from using the noisy evaluation of fk(·) or gk(·) in constructing the search direction is assumed to have bounded second‐moment—this allows for biased estimator to be used in the SA algorithms. Given the lenient assumptions on the drift and error term, the error bounds apply to a broad class of time‐varying scenarios and are useful for finite‐sample analysis. Since the tracking capability characterizes the average performance across all possible sample paths, it may not provide much guidance on the gain‐tuning strategies useful for one single run. Hence, we propose a data‐dependent gain‐tuning strategy based on estimating the Hessian information and the noise level. The adaptive rule is advantageous as it enables the step size to vary with information gathered during the progress of the estimation procedure. [ABSTRACT FROM AUTHOR]

VISCAL (VISSIM calibration) is an automated calibration tool for microscopic simulation parameters in VISSIM environment, based on three heuristic optimization algorithms: (a) genetic algorithm (GA); (b) simultaneous perturbation stochastic approximation (SPSA); (c) simulated annealing (SA). It is developed with a goal to automate and ease the tedious process of calibration, offering greater flexibility to the users by providing control on every aspect of the calibration process. It includes multiple features for a generic application tool with the ability to test the significance of the appropriate decision parameter set for a particular network, to determine the most suitable objective function to reflect network characteristics, and to check the suitability of any of the three heuristic optimization algorithms for a particular network. VISCAL also offers four objective function choices into the system: (1) speed, (2) flow, (3) delay, and (4) multi-objective criteria. It is able to calibrate all the driving behavior parameters for any type (urban, rural) and extent of network (small or large network). However, for this study, the operation of the tool is tested by a dataset obtained from a 3.26 km freeway of Dhaka, Bangladesh.

[Display omitted] • A model-free vibration control strategy is examined. • New parameter tuning technique is constructed for the model-free controller. • The proposed tuning method is more efficient than a previous one. • The effectiveness of the present method is verified by experiments. In active vibration control, controller tuning is necessary to obtain a sufficient damping performance. This study presents an efficient tuning technique for model-free vibration controller based on a virtual controlled object (VCO). A model-free vibration control system is constructed by using an actuator model and the VCO. A reference controlled object (RCO), which is a designer-defined single-degree-of-freedom (SDOF) structure, is used to tune the VCO-controller offline. A novel parameter tuning technique based on the RCO and the simultaneous perturbation stochastic approximation (SPSA) is proposed considering the difference in scale of the tuning parameters. The proposed tuning scheme automatically determines the tuning parameter of the controller without manual trial-and-errors such as experiments with actual plants and is much faster than previous methods. Therefore, the proposed method significantly enhances the practicability of the VCO-based model-free vibration suppression, bridging the gap between the basic study of the VCO-method and its implementation. The effectiveness of the proposed method is verified by applying it to the VCO-based H 2 controller. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) is one of the control methods for discrete time systems. The optimal input is calculated by using Linear Quadratic Regulator (LQR). The weight matrices in the evaluation function for LQR are determined by a designer with professional experience and a trial & error approach. Therefore, even if the same system is targeted, the performance can differ depending on the designer. This paper proposes a new weight selection algorithm using Simultaneous Perturbation Stochastic Approximation (SPSA) for MPC. A new evaluation function is proposed for the selection algorithm. Numerical values of the overshoot and the settling time are directly applied as the user’s requirements in this evaluation function. The optimal weight matrices numerically satisfying those requirements can be selected by the proposed algorithm. Simulation study of a zero momentum spacecraft shows that the proposed method is effective for the weight selection with consideration of performance.

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Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5-10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions. [ABSTRACT FROM AUTHOR]

Model-based controllers with adaptive design variables are often used to control an object with time-dependent characteristics. However, the controller’s performance is influenced by many factors such as modeling accuracy and fluctuations in the object’s characteristics. One method to overcome these negative factors is to tune model-based controllers. Herein we propose an online tuning method to maintain control performance for an object that exhibits time-dependent variations. The proposed method employs the poles of the controller as design variables because the poles significantly impact performance. Specifically, we use the simultaneous perturbation stochastic approximation (SPSA) to optimize a model-based controller with multiple design variables. Moreover, a vibration control experiment of an object with time-dependent characteristics as the temperature is varied demonstrates that the proposed method allows adaptive control and stably maintains the closed-loop characteristics. [ABSTRACT FROM AUTHOR]

To meet the demands of the modern power system for satisfactory operation and control, here, a novel data‐driven control strategy is proposed to solve the load frequency control (LFC) problems of power systems, with complete convergence analysis. This data‐based LFC approach is designed based on the simultaneous perturbation stochastic approximation (SPSA) method and neural network ensemble. The data‐based controller is constructed using a function approximator, which is fixed as a neural network. Being the control parameters, the connection weights of the neural network controller are updated at each iteration step. In order to improve the overall control accuracy and get more stable control performance, the idea of neural network ensemble is introduced for the data‐based controller structure design. The proposed data‐based controller takes past and current system information as input and generates a control signal that can affect future system performance as output, and during the whole process, it is not necessary to build mathematical model for the controlled plant. A one‐area LFC problem with system parametric uncertainties as well as a typical two‐area LFC problem have been introduced for simulation tests, and the feasibility and effectiveness of this newly proposed data‐based LFC strategy is well revealed through simulation results. [ABSTRACT FROM AUTHOR]