State Error Research Articles

Adaptive Event-Triggered Fuzzy Positioning Control for Unmanned Marine Vehicles With Actuator Saturation and Hybrid Attacks

In this paper, we focus on the event-triggered observer-based fuzzy positioning control and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -gain analysis of nonlinear unmanned marine vehicle (UMV) systems in network environments, which is subject to a hybrid attack consisting of DoS attack and deception attack. Firstly, based on the T-S fuzzy modeling method, a unified switched system model is established for the positioning control of nonlinear UMV systems with actuator saturation, multiplicative gain uncertainties, external disturbance and hybrid attacks. Then, by resorting to introducing the integral-type state error, a novel adaptive memory-based event-triggered mechanism (ETM) is proposed to lighten the communication load in UMV systems. Furthermore, a sufficient criterion is proposed to make the mean square exponential stability of UMV systems with a non-weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -gain level be guaranteed, and gain parameters of the designed controller and observer are also solved efficiently. Finally, the simulation example demonstrates the effectiveness of the proposed nonfragile control strategy.

Improved performance of Hexacopter's roll balance control system

&lt;span lang="EN-US"&gt;One of the obstacles in determining the constant of the proportional integral derivative (PID) control system for the stability of the Hexacopter is due to the dynamic response of the system. Changing the speed and direction of the aircraft's motion through the throttle is translated into a control system concept into a set-point change. If you have used PID control, which is constant, cannot adjust to changes in set-points or external influences, the stability and reliability of the aircraft cannot be guaranteed. This study proposes PID control, with adaptable constants, using a fuzzy logic controller (FLC). The influence of internal changes and factors outside the aircraft control system, in principle, will accumulate on the size of the error and delta-error. Thus, FLC performs tuning for the PID constant according to the error and delta-error. The design of fuzzifications and defuzzification is based on the maximum limit value of error and delta-error, and sets the value of the constant obtained by the Ziegler-Nichols method as the default value. After the real-plant test, the system performance is obtained as follows: settling time = 34mS; peak time 21mS; rise time = 9.2 mS; delay time = 5.7mS; percent overshoot and steady state error = 1%.&lt;/span&gt;

Quadruple spherical tank systems with automatic level control applications using fuzzy deep neural sliding mode FOPID controller

The premier goal of this research is to develop the Fuzzy Deep Neural Sliding Mode Fractional Order Proportional Integral Derivative (FDN-SM-FOPID) controller system for controlling liquid in quadruple spherical tank systems. This is used in non-linear spherical systems to control the level of liquid in real time. These models' dynamics allow for a more accurate identification of the spherical tank system that generates control signals from liquid samples obtained at reference levels. However, because the system is susceptible to outside disturbances, error minimization is not done. Therefore, it requires the addition of a special controller to lessen this flaw. The suggested Deep Neural Fuzzy model's six-layered network is optimized using the back-propagation method. As a result, the system's efficient training reduces offset model errors, steady state errors, and unmeasured disturbances. The liquid level is maintained and controlled by this neural intelligence system, which meets the necessary design requirements such as no overshoot, time constant, less settling and rise time, which is used in various platforms. The FOMCON toolbox in MATLAB software is used for research simulation work. The chemical industry, wastewater treatment, the aerospace industry, and the pharmaceutical industry have all employed the suggested quadruple spherical tank system to test its practicality. The experimental and simulation results are demonstrated by a real-time liquid control experimental setup.

BUDAYA ARAB PRA DAN PASCA ISLAM

The social condition of the Arab nation before Islam was known as the ‘age of ignorance’ or the era of ignorance, which was caused by social, political, moral and social relations in a state of real error. In order to understand the customs of the ignorance of the Arabs, God sent a Messenger, Muhammad. In this study, researchers discussed the integration of Islam and Arabic culture in both pre- and post-Islamic times. The research method used is the Sociology of Religion approach, and in its refinement it will be supported by other approaches such as the Phenomenological, Historical and Anthropological approaches. The results of the research show that many of the pre-Islamic Arab nations worshiped idols, the political conditions at that time were divided, not recognizing central leadership or unity. While the condition of the post-Islamic Arab nation, the population has lived in prosperity. Changes both in terms of character, culture and beliefs. So that the Arabs at that time respected each other and because of that it was also detrimental to the disputes between tribes that often occurred during the Jahiliyya period.

Global Tracking Control for High-Order Odd-Rational-Powers Systems with Unknown Nonlinearities and Powers

The global control problem of high-order nonlinear systems with unknown odd rational powers and nonlinearities has been solved in this paper for the first time. Barrier functions were introduced into the controller to set the controller free from rational powers, which solved the problem caused by unknown odd rational powers. In utilizing barrier functions and their inverse functions, two transformations were constructed for the state and tracking errors, which resulted in the global tracking controller for the high-order odd-rational-powers nonlinear systems with unknown system nonlinearities. No knowledge of system nonlinearities and odd rational powers is required to construct the controller in this paper, and only control gains are assumed to be positive to guarantee the controllability of the system, which implies that the proposed method is model-free and has much more relaxed conditions than all the existing methods for high-order nonlinear systems. The global boundedness of all closed-loop signals was proved based on the Lyapunov stability theorem. Finally, simulation results were given to demonstrate the effectiveness of the proposed method.

Three-peak evolution characteristics of supporting stress on a super-long working face in a thick coal seam

Increasing working face length is the development trend of intelligent and efficient longwall fully mechanized mining. By combining field measurement and theoretical calculation, change characteristics of the frequency, peak value, and range of weighting in a long working face in a 1000 m-deep shaft of Kouzidong Coal Mine (Fuyang City, Anhui Province, China) were studied. Based on the mechanical model of the hydraulic support group of the elastic independent support, force characteristics and posture change characteristics of the support in the 121304 working face of the mine were studied and analyzed. The supporting stress characteristics of the deep super-long working face were revealed, and the theoretical calculation was in good agreement with the actual measurement. Based on the aforementioned model, support parameters and control technology of the 140502 working face were studied. The results show that as the length of the working face increases, the supporting stress gradually transforms from a single peak to multiple peaks and expands to both ends of the working face. The weighting in different areas of the working face is characterized by an obvious time sequence and great difference in intensity. When the working face length is 300 m, the multi-peak characteristic of super-long working faces appears. The M-shaped three-peak characteristic can be used as the criterion for super-long working faces. A reasonable working face length should be determined by comprehensively considering occurrence conditions of coal seams, working face parameter, and equipment ability. According to the different attributes of hydraulic supports in space and function and combining with zonal characteristics of the long working face, the criterion for the super-long working face and the principle of zonal cooperative control of hydraulic support groups were revealed. In addition, a cooperative control method of equipment groups in the working face based on the state error and cost functions was put forward, and the three-level cooperative control strategy and implementation method were formulated. It can effectively guide the equipment group in the super-long fully mechanized working face in deep thick coal seams to achieve optimal coordinated control.

IETS: State space model for intermittent demand forecasting

Inventory decisions relating to items that are demanded intermittently are particularly challenging. Decisions relating to termination of sales of product often rely on point estimates of the mean demand, whereas replenishment decisions depend on quantiles from interval estimates. It is in this context that modelling intermittent demand becomes an important task. In previous research, this has been addressed by generalised linear models or integer-valued ARMA models, while the development of models in state space framework has had mixed success. In this paper, we propose a general state space model that takes intermittence of data into account, extending the taxonomy of single source of error state space models. We show that this model has a connection with conventional non-intermittent state space models used in inventory planning. Certain forms of it may be estimated by Croston’s and Teunter–Syntetos–Babai (TSB) forecasting methods. We discuss properties of the proposed models and show how a selection can be made between them in the proposed framework. We then conduct a simulation experiment, empirically evaluating the inventory implications.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

Improving power system low-frequency oscillations damping based on multiple-model optimal control strategy using polynomial combination algorithm

In this paper, the damping of low-frequency oscillations (LFOs) in a power system containing Static VAR Compensator (SVC) is investigated based on Multiple-Model Linear Optimal Control (MMLOC) Strategy. The performance of MMLOC is enhanced using Polynomial Combination Algorithm (PCA). A linear optimal controller (LOC) generates the optimal control signal with linearization the nonlinear state equations of the system and solving the Riccati equation. One LOC generates synchronized control signals for the synchronous generator excitation system as well as SVC. The LOC is assigned to generate each model’s control signal. The final control signal is made by interpolating the LOC output signals obtained from each model using PCA. Considering a three-phase symmetrical fault on a near bus to the generator, the proposed control strategy is evaluated. This strategy not only maintains stability but also reduces LFO effectively. Furthermore, steady state error of rotor speed and rotor angle tend to zero favorably. Simulations are done for single- and multi-machine power systems via a MATLAB m-code.

A multi-level cell for ultra-scaled STT-MRAM realized by back-hopping

The development of advanced magnetic tunnel junctions with a footprint in the single-digit nanometer range can be achieved using structures with an elongated and composite ferromagnetic free layer. Using the spin drift-diffusion model, we investigated the back-hopping effect in ultra-scaled STT-MRAM devices. Unwanted switching of the last layer of the structure has been identified as a possible cause of the back-hopping effect, which leads to a writing error in the magnetization state of the free layer. To understand the switching of the free layer, the torque acting on both parts of the composite free layer must be studied in detail. A reduction in the size of MRAM components to increase the memory density may lead to back-hopping. Moreover, the observed back-hopping effect can be exploited for the realization of multi-level cells. For this purpose, we have carefully investigated the switching behavior of a device with several tunnel barrier interfaces and a few nanometers in diameter, in particular the impact of changes in the material parameters.

Continuous gradient temperature control of microfluidic chip based on thermoelectric cooler

High-efficiency and accurate temperature control is the key to the application of microfluidic technology in biology, chemistry and medicine fields. At present, gradient temperature control of microfluidic chip in a wide range below room temperature still faces challenges. Based on a microfluidic platform of double emulsion droplets, this study builds a temperature control system and attempts to achieve continuous gradient temperature control on time scale of microfluidics based on thermoelectric cooler. Temperature control unit is designed and relevant control strategies are discussed to achieve accurate temperature control. Meanwhile, the strategy to achieve continuous gradient temperature control of microfluidic chip is mainly explored. Finally, combining with the working characteristics of thermoelectric cooler, continuous gradient temperature control of microfluidic chip is experimentally realized based on adaptive fuzzy PID control, while PID control shows remarkable overshoot, deviation and oscillation. Moreover, small gradient temperature control from 10 °C down to −15 °C, −20 °C and then −25 °C is precisely realized by using adaptive fuzzy PID control, as well as large gradient temperature control from 20 °C down to 0 °C and then −20 °C. Specifically, the maximum deviation of temperature is 0.192 °C in small gradient temperature control, and the steady state error is within ±0.07 °C. In large gradient temperature control, the maximum deviation of temperature is 0.309 °C, and the steady state error was within ±0.1 °C. This study provides diversified temperature control in a wide range for microfluidic analysis.

Fault diagnosis method of dissolved oxygen sensor electrolyte loss based on impedance measurement

Dissolved oxygen affects the growth and survival of aquatic organisms and considers as an important indicator of aquaculture water quality. Moreover, the electrochemical dissolved oxygen sensor may become faulty due to flow shock or damage by organisms which destroys the dissolved oxygen permeable membrane and results in loss of electrolytes. In this paper, a method was developed for detecting electrolyte loss faults in polarographic dissolved oxygen sensors by directly measuring impedance of sensing electrodes without additional sensing elements or modifying the sensor. First, the factors interfering electrolyte concentration-impedance relationship and the requirement for temperature compensation was determined. Secondly, a fault diagnosis of internal electrolyte loss was achieved by using the difference between the theoretically calculated value and the impedance measurement value in combination with the electrode reaction law. Finally, the impedance detection method was demonstrated using artificially simulated faults. The results showed that the developed method had accurate tracking of electrolyte consumption with a Pearson coefficient of 0.987. Three different levels of breakage failures were simulated and detected as 4.48 %, 21.07 % and 73.47 %, respectively. The mean absolute error of the normal state was about 1.67, which increased to 16.3 after a period of failure. The developed method meets the requirements of embedded development because it uses a simple fault detection device and easy data processing using a simple microprocessor. Furthermore, the diagnosis result has been demonstrated as an indicator of membrane health and breakage conditions of dissolved oxygen sensors.

Implementing a novel fault prognosis technique based on nonlinear fault observer and online parameters estimation

&lt;span lang="EN-US"&gt;This research presents a methodology for predicting errors of parameters, as the algorithm tries to monitor the parameters in order to maintain or replace them when needed to avoid excessive expenses. The presented implementation mechanism is based on monitoring parameters according to a specific number of batches and each batch consists of a number of iterations, which in turn are a number of samples. The proposed algorithm involves designing a new nonlinear observer and writing a secondary algorithm for parameter estimation based on the online nonlinear recursive least squares algorithm associated with the observer states. In addition, the algorithm presents an attempt to find a relationship between the error states and the state of the parameters by creating a new function to determine the weight of the error according to four components; parameter changes, output residuals, output errors and the error diagnosed by the new observer. The algorithm also includes introducing the probability form of the weights using the kernel density function for the average and maximum weights for each batch. Finally, relying on the results, it is possible to take the appropriate decision to maintain or change the parameters as shown a non-linear direct current motor model case study.&lt;/span&gt;

A Multiscale Four-Dimensional Variational Data Assimilation Scheme: A Squall-Line Case Study

Abstract This study presents a multiscale four-dimensional variational data assimilation (MS-4DVar) scheme that aims to assimilate multiscale information from conventional and radar observations. The MS-4DVar scheme separately assimilates conventional and radar data in different outer loop iterations of an incremental 4DVar with varied resolutions in the tangent linear and adjoint models (TLM/ADM) and time window lengths in the 4DVar. The MS-4DVar scheme was evaluated through a series of single observation tests and several cycled assimilation and forecasting experiments for a real squall-line case. Our results indicated that different TLM/ADM resolutions and time window lengths applied to the conventional and radar observations improved the multiscale analysis. In addition, the MS-4DVar scheme was more efficient than the common 4DVar because of the low-resolution TLM/ADM used for conventional data and the shortened time window length for radar data. Verification of the squall-line forecasts suggested that the MS-4DVar scheme improved the hourly accumulated precipitation and radar reflectivity forecast skills and reduced the forecast errors of both large-scale environmental and convective-scale states. Further diagnosis revealed that the improvement of precipitation forecast skill was attributable to the stronger cold pool, deeper saturated water vapor layer, and stronger updraft of the simulated squall-line system, as well as a more favorable convective environment.

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pragmatic</i> , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

Battery States Monitoring for Electric Vehicles Based on Transferred Multi-Task Learning

State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and computational efficiency. The monitoring results under extremely current working profiles and variable internal and external conditions are evaluated. Results show that the mean absolute error and root mean square error of state of charge and state of energy estimation are less than 2.31% and 3.31%, respectively. The above errors in the prediction of future temperature five steps ahead are less than 0.89℃ and 1.29℃, respectively. The framework is also suitable for monitoring second-life batteries retired from electric vehicles. This paper illustrates the potential application of data-driven multi-state monitoring throughout the entire battery life.

Shafting error parameters identification and compensation of non-orthogonal laser theodolite without reference end on laser axis

The research about non-orthogonal shafting laser theodolite (N-theodolite) is motivated by the growing demand for instrument. The reference pose of laser axis in motion state cannot be obtained due to the absence of reference end, and the error compensation of the N-theodolite is lack of basis. Therefore, the shafting error parameters identification and compensation method without reference end is proposed in this paper. Based on the derived kinematics model of the N-theodolite, the laser axis pose error in motion state can be described accurately. Then the aiming error is defined as the key reference parameters to construct the linear equations for effective identification and iterative compensation of shafting error. The experimental results have shown that the average aiming error of the compensated N-theodolite is less than 0.04 mm within 10 m. Compared with the existing calibration approaches, the shafting parameters adjustment performance of the proposed method is more prominent.

A Novel Closed-Loop Structure for Drag-Free Control Systems with ESKF and LQR.

Space-borne gravitational wave detection satellite confronts many uncertain perturbations, such as solar pressure, dilute atmospheric drag, etc. To realize an ultra-static and ultra-stable inertial benchmark achieved by a test-mass (TM) being free to move inside a spacecraft (S/C), the drag-free control system of S/C requires super high steady-state accuracies and dynamic performances. The Active Disturbance Rejection Control (ADRC) technique has a certain capability in solving problems with common perturbations, while there is still room for optimization in dealing with the complicated drag-free control problem. When faced with complex noises, the steady-state accuracy of the traditional control method is not good enough and the convergence speed of regulating process is not fast enough. In this paper, the optimized Active Disturbance Rejection Control technique is applied. With the extended state Kalman filter (ESKF) estimating the states and disturbances in real time, a novel closed-loop control structure is designed by combining the linear quadratic regulator (LQR) and ESKF, which can satisfy the design targets competently. The comparative analysis and simulation results show that the LQR controller designed in this paper has a faster response and a higher accuracy compared with the traditional nonlinear state error feedback (NSEF), which uses a deformation of weighting components of classical PID. The new drag-free control structure proposed in the paper can be used in future gravitational wave detection satellites.

Closed-Loop Deep Brain Stimulation Using a Type of Fixed-Time Sliding Mode Controller for Avoiding Epileptiform Discharge in a Human Cortical Model

The cortical excitability rate correlates with epileptic seizures, which should be modulated in a finite time to prevent brain damage. Deep brain stimulation (DBS) is a successful treatment for patients with refractory epilepsy. This study proposes a closed-loop DBS control method that uses a Fixed-Time Integral Super-Twisting Sliding Mode Controller (FTISTSMC) to stop simulated epileptiform discharges within a limited time by modifying cortical excitability in the human Cortical Model (CM). First, the simulated stimulus current in the DBS method is injected into the state variable of the excitatory neurons population in CM. Then, the FTISTSMC is applied to the state variable described above to regulate the stimulus current value during the stimulation process to reduce the possible destructive effects of electrical pulses on the behavior of the healthy neurons. The proposed controller has some characteristics causing the simulated epileptic activity to become normal and the tracking error of the healthy state to be completely zero after 4.7[Formula: see text]s. In addition, it is possible to resolve the chattering and singularity problems and completely control epileptic seizures within a fixed and finite time frame.

Speed Control of Three Phase Induction Motor using PI Controller with Space Vector Width Modulation (SVPWM) Technique

This article aimed to design and simulated the speed control of a three-phase induction motor using a PI controller with Space Vector Pulse width modulation technique. The induction motor used in this article is was designed at the Electrical Energy Conversion Laboratory, Riau University, with a power of 1.1 kW, 380 V, 2-pole. Meanwhile, the PI controller constants used in designing this induction motor were determined using the Fine Tunning method to obtain KP and KI values of 3.539 and 9.526, respectively. The tests were carried out by running simulations in three conditions, namely no load, full load, and variable load at a speed of 2800 rpm. The test results showed that the use of a PI controller can improve the speed response of induction motors by eliminating the steady state error. This is in addition to increasing the rise time response of the motor speed by 0.012s and 0.046s at no load and full load, respectively, when the rise time analysis is at the same value. It can also accelerate the motor to reach a peak speed of 0.247 s and 0.166s at no load and full load. In addition, SPVWM with PI controller can maintain speed setting even though there is a load change during operation, which can be verified with load testing.