State Error Research Articles

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

Reducción del Sobrepico de un Elevador de tijeras Mediante Observador de Estado, LQR y LQG

Scissor lifts are crucial in various industries, known for their robust design and ability to lift heavy loads to significant heights. Their interlinked scissor mechanism allows controlled elevation and descent of the work platform. Precision and safety in their operation are essential to prevent accidents and ensure efficiency. A critical issue is the "overshoot," where the lift exceeds the desired height before stabilizing, causing instability and safety risks. This phenomenon is related to the system's dynamics and control, which typically employ hydraulic or electric systems to regulate the extension and retraction of the scissors. Precision in these systems is vital to stop the platform at the correct height smoothly and accurately. This paper presents a solution based on MATLAB/Simulink to improve the overshoot in scissor lifts using advanced control techniques. Different control strategies are implemented and evaluated, including the use of a State Observer, LQR (Linear Quadratic Regulator), and LQG (Linear Quadratic Gaussian Controller). The State Observer is used to estimate the system's internal variables, allowing for more precise feedback. The LQR is employed to design a controller that minimizes a cost function, optimizing the balance between control effort and state error. Finally, the LQG incorporates a Kalman filter to handle system uncertainty and noise, providing robust and efficient control. This demonstrates a significant improvement in the precision and stability of the scissor lift, reducing overshoot and enhancing operational safety. This research contributes to the development of more advanced and safer control systems for industrial applications, optimizing the performance and reliability of scissor lifts.

Analysis of Load Frequency Control of Two Area Power System using Different Control Techniques

To maintain stability is a mandatory requirement of any kind of power network. Interconnected power systems have significant advantages in maintaining stability and frequency at their nominal values. The control of the interconnected power systems is performed by Automatic Generation Control (AGC), which has two parts: the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC). When a disturbance occurs in the system, Load Frequency Control (LFC) acts to re-establish the system into its steady state with zero error as well as the desired transient response characteristics. This paper exhibits the design and implementation of a PID and Fuzzy Logic Controller for two area power systems, taking the steady state error and percentage of overshoot, undershoot, and pre-shoot as comparison parameters. MATLAB/Simulink software was used to bring out the implementation and obtain results. System dynamic performance is observed for PID and Fuzzy Logic Controllers in Simulink. The comparison study indicated that the proposed Fuzzy Logic controller has better performance than the PID controller. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 62-79

Robust control algorithm designed for robotic legs of lunar-based equipment: Modeling and experiment

In lunar exploration, lunar-based equipment assumes the responsibility of mobility and transportation during the construction of unmanned lunar base. Legged robots are an important form of the lunar-based equipment due to their mobility and terrain adaptability. However, the influence of lunar terrain and lunar soil cannot be ignored when it comes to studying the mobile stability of lunar-based equipment. Utilizing the dynamic model of the robotic leg, this study proposes a new robust control algorithm, MBC (Model-Based with Continuous expression) algorithm, for lunar-based equipment’s robotic leg, and its effectiveness is established through experimentation and theoretical validation with the Lyapunov second method. In the design and verification, the contact force of lunar soil and the lunar environment with low gravity are considered, indicating the applicability of the proposed MBC method in special lunar scenarios. Compared to the conventional PD (Proportional-Differential) and MPD (Model-Based Proportional-Differential) techniques, MBC robust algorithm ultimately demonstrates superior stability and rapidity with a smaller steady state error.

LOAD FREQUENCY CONTROL STRATEGY FOR MICROGRID SYSTEMS USING COMPUTATIONAL INTELLIGENCE OPTIMIZED PID CONTROLLER

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm . Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms.

Sampled‐data‐based synchronization of complex networks under scaling attacks

In this study, our main objective is to address the issue of sampled‐data‐based synchronization of complex networks subjected to stochastic scaling attacks using a looped‐functional approach. To begin with, the design of a Lyapunov functional, which involves tuning parameters and slack variables, considers the error state information of and . Then, the scaling attack is a type of deception attack that is formulated using an interval matrix approach. In addition, the complex networks considered in this study incorporate uncertain inner coupling strengths, which are more general than the existing ones, owing to the presence of uncertainties in the coupling strengths. Using a looped‐functional approach to construct an appropriate Lyapunov–Krasovskii functional, we derive a set of sufficient conditions to achieve mean‐square asymptotic synchronization for considered complex networks under scaling attacks. In the end, the proposed results are illustrated using three examples, including a practical example that highlights their effectiveness and superiority.

Tuning the EKF using estimated measurements (EKF-EM) for balanced performances

In this paper, the conventional Extended Kalman Filter (EKF) algorithm has been systematically extended to the estimated measurement (EKF-EM) paradigm. The derivation of the EKF-EM state and state error estimates establishes the relation of the robustness and sensitivity metrics to the measurement projected Kalman gain. Recently, these metrics are being used ad-hoc for obtaining desired EKF performances in various signal processing applications. The EKF-EM is used to develop a process noise adjustment algorithm to tune the EKF for balanced performances. This proposed method is applied and validated in 3 diverse applications:(a) ensure convergent comparable filter performances for an originally divergent EKF in the 3D Lorenz attractor problem, (b) obtain spectral and audio improvement of a practical noise corrupted speech signal and (c) determine a suitable process model out of several options for balanced filter performances in a realistic 2D ballistic target tracking scenario.

Desensitized optimal trajectory for hopping rovers on small bodies

Future exploration tasks of small bodies will need to sample or visit multiple points on the target to obtain more scientific returns, requiring rovers to have the ability to hop on a small body surface. This paper proposes an approach to generate a desensitized optimal trajectory for hopping rovers, aiming at reducing the sensitivity of hopping trajectory in the presence of uncertainties. Firstly, considering parameter uncertainties and initial state errors, analytical expressions of optimal initial states are derived on a planar scene, based on ballistic dynamics. Then, similar methods are developed in both uphill and downhill cases of inclined scenes. Subsequently, the desensitization performance of long-distance hopping trajectory is analyzed under single-hop, identical, and non-identical N-hop strategies. To facilitate the application of the proposed analytical solution to the simulated surface environment of small bodies, a prediction-correction procedure is presented. Finally, Monte Carlo simulations are carried out to verify the effectiveness of the proposed methods. The results indicate that the sensitivity of the hopping trajectory to uncertainties can be effectively diminished by employing the desensitized optimal trajectory and multiple hopping strategy.

Distributed iterative learning consensus tracking for singular partial differential multi-agent systems under fixed and iteration-varying topologies

This paper investigates the consensus tracking problems of singular partial differential multi-agent systems (SPD-MASs) with initial state errors under fixed and iteration-varying network topologies, where only certain follower agents have access the trajectory defined by virtual leaders. To solve this problem, a distributed iterative learning consensus protocol with initial state learning is proposed, ensuring accurate tracking of the leader's trajectory by all follower agents with initial state errors. The convergence conditions for consensus tracking error are derived through mathematical analysis between adjacent agents. Theoretical analysis shows that, under convergence conditions, the consensus error between any two agents can converge to zero along the iterative axis. Finally, numerical simulations show validate the effectiveness of the proposed distributed control protocol with initial state learning compared to traditional protocols without state learning.

Optimisation of MHD flow within trapezoidal cavity containing hybrid nanofluid by artificial neural network

PurposeThis study aims to numerically investigate heat transport in a trapezoidal cavity using hybrid nanoparticles (Ag-$Al_2O_3$). Unlike previous studies, this one covers magnetohydrodynamics, joule heating with viscous dissipation, heat absorption and generation. The left and right sides of the chasm are frigid. The upper wall heats, whereas the bottom wall remains adiabatic.Design/methodology/approachAfter reducing the system of dimensional equations to dimensionless equations, the authors use the Galerkin finite element method to solve them numerically. Geometric parameters affect heating efficiency; thus, the authors use flow metrics such as the Reynold number Re, magnetic parameter M, volume fraction coefficient, heat absorption and Eckert number Ec. The authors use the finite volume method to solve the governing equations after converting them to dimensionless form. The authors also try the artificial neural network method to predict the innovative cavity’s heat response in future scenarios. Transition state charts, regression analysis, MSE and error histograms accelerate, smooth and accurately converge solutions.FindingsAs the magnetic parameter and Eckert number increase, the enclosure emits more heat. As Reynold and volume fraction coefficients rise, the Nusselt number falls. It rose as magnetic, Eckert and heat absorption characteristics increased. The average Nusselt number rises with Reynolds and volume fraction coefficients. The magnetic, Eckert and heat absorption characteristics have inverse values.Originality/valueThis study numerically investigates heat transport in a trapezoidal cavity using hybrid nanoparticles (Ag-$Al_2O_3$). Unlike previous studies, this one covers MHD, joule heating with viscous dissipation, heat absorption and generation. The left and right sides of the chasm are frigid. The upper wall heats, whereas the bottom wall remains adiabatic.

Hybrid FOT2F-FOPD controller for permanent magnet synchronization motor based on ILA optimization with SRF-PLL

Permanent magnet synchronous motor (PMSM) systems have gained popularity in various fields due to their advantages such as high speed, high accuracy, low maintenance, and high reliability. This paper presents the speed tracking control of a permanent magnet synchronous motor (PMSM) using a hybrid fractional order PI and type 2 fuzzy control with fractional order PD control (FOT2F-FOPD). The SRF-PLL observes the motor speed and estimates the rotor's position by interpreting the input voltages of the motor instead of using a sensor. Then, the controller parameters (gain, μ and λ) are tuned based on a novel optimization algorithm called Incomprehensible but Intelligible-in-time (IbI) Logics algorithm (ILA). The proposed controller enhances the performance of the system and regulates the speed of the motor under parameter variations such as the speed and the load. So, the proposed ILA (FOT2F-FOPD) controller is assessed using MATLAB/Simulink simulation and compared with other controller techniques. The proposed technique reduces the settling time, steady state error and overshoot by at least 65%, 54% and 53% respectively under load conditions compared with (PSO, optimized FOPD, FOPI and PI). While at no load condition, the settling time and the error are reduced by 31% and 12.5% respectively with no overshoot in output response. The results show a significant improvement in the performance of motors used with the application of the proposed controller and the employment of the (ILA) optimization compared with FOPI and PI controllers.

Graph-Based vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MAV Indoor Pose Estimation

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an Error State Kalman Filter (ESKF) and a Pose Graph Optimization (PGO) approach to address this gap. We systematically evaluate the performance of the derived approaches for real-time MAV localization in realistic scenarios with 5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5G technologies in this domain. In order to experimentally test and compare our localization approaches, we augment the EuRoC MAV benchmark dataset for visual-inertial odometry with simulated yet highly realistic 5G ToA measurements. Our experimental results comprehensively assess the impact of varying network setups, including varying base station numbers and network configurations, on ToA-based MAV localization performance. The findings show promising results for seamless and robust localization using 5G ToA measurements, achieving an accuracy of 15 cm throughout the entire trajectory within a graph-based framework with five 5G base stations, and an accuracy of up to 34 cm in the case of ESKF-based localization. Additionally, we measure the run time of both algorithms and show that they are both fast enough for real-time implementation.

Design of an artificial neural network and proportional‐integral‐derivative controller using particle swarm optimization for Boeing 747‐400 aircraft pitch control

AbstractThis paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747‐400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747‐400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747‐400 with small disturbance theory linearization methods and ANN based non‐linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747‐400 commercial aircraft was presented. The ANN controller outperformed the PID‐PSO and PID‐GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID‐GUI, PID‐PSO, and ANN controllers for pitch control of the Boeing 747‐400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID‐PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory.

A comparative study between PI, PD and PID controllers to control motor speed

DC Motors are widely used in industries for various purposes. Many situations demand changes in the speed of the DC Motor, and this makes necessity to develop a method to effectively control the speed of DC motor. There are conventional and digital controller types, Proportional Integral (PI), Proportional Derivative (PD), and Proportional Integral Derivative (PID) used to control speed of the DC motor. In this project PI, PD and PID is modeling and simulated in MATLAB SIMULINK to control speed of the DC Motor. Aforementioned Controllers analyzed and evaluated on four terms overshoot, rise time, steady state error and settling time, the purpose of the evaluation to find the best performance to control the speed of DC motor. As a result of this, PID is the best controller due to it is improved performance when compared with others controllers.

A moment-based Kalman filtering approach for estimation in ensemble systems.

A persistent challenge in tasks involving large-scale dynamical systems, such as state estimation and error reduction, revolves around processing the collected measurements. Frequently, these data suffer from the curse of dimensionality, leading to increased computational demands in data processing methodologies. Recent scholarly investigations have underscored the utility of delineating collective states and dynamics via moment-based representations. These representations serve as a form of sufficient statistics for encapsulating collective characteristics, while simultaneously permitting the retrieval of individual data points. In this paper, we reshape the Kalman filter methodology, aiming its application in the moment domain of an ensemble system and developing the basis for moment ensemble noise filtering. The moment system is defined with respect to the normalized Legendre polynomials, and it is shown that its orthogonal basis structure introduces unique benefits for the application of Kalman filter for both i.i.d. and universal Gaussian disturbances. The proposed method thrives from the reduction in problem dimension, which is unbounded within the state-space representation, and can achieve significantly smaller values when converted to the truncated moment-space. Furthermore, the robustness of moment data toward outliers and localized inaccuracies is an additional positive aspect of this approach. The methodology is applied for an ensemble of harmonic oscillators and units following aircraft dynamics, with results showcasing a reduction in both cumulative absolute error and covariance with reduced calculation cost due to the realization of operations within the moment framework conceived.

Adaptive Takagi–Sugeno (T–S) fuzzy observer based fault tolerant control for DC–DC converters with robustness against uncertainties

AbstractThis paper proposes a Takagi–Sugeno fuzzy system model based fault tolerant control scheme for DC–DC converters, which is robust against parameter uncertainties and achieves the output voltage of an ideal converter. The control involves estimating the duty cycle change in the form of a fault parameter required to track the output voltage, in the presence of several uncertain conditions including converter losses, variation in input voltage, and unknown and changing output load. An adaptive law is designed to estimate the fault parameter that guarantees state and parameter error convergence. The adaptive law is derived using the Lyapunov stability theorem and the required parameters are evaluated by solving a linear matrix inequalities optimization problem. The load resistance is estimated in parallel by using a Kalman filter and fed to the fault parameter estimation scheme. Furthermore, a fast and robust method to detect short and open circuit switch faults is also presented. The proposed technique offers a simple, yet effective method to regulate the output voltage under several faulty and uncertain conditions. The proposed technique is tested on a DC–DC boost converter simulation model and the demonstrated MATLAB/Simulink results show the effectiveness of the proposed algorithm.

Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis.

There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.