Advanced Control for Applications最新文献

Micro-grid islanding methods are gaining importance as the presence of renewable energy resources is becoming increasingly popular in power distribution. Among all the islanding methods, passive methods are easy to implement and applicable to all types of DERs provided the NDZ is minimized. This article presents a novel passive islanding detection method based on the CSoDoA of ellipse formed by the successive phase voltages to address the NDZ problem. The peak magnitude and phase angle difference of voltages are the parameters to be estimated to compute the area of the ellipse. State-of-the-art passive methods consider the effect of either magnitude or phase angle or frequency as an islanding detection metric. The CSoDoA metric combines the effect of both magnitude and phase angle thereby reducing the NDZ. The proposed method is validated on both synchronous and inverter-interfaced DERs. The test systems considered are both single-DER based MG and modified IEEE 13 bus multi-DER MG. The CSoDoA algorithm is implemented on a Raspberry-Pi controller and validated using HIL workbench with real-time simulator OP-4510. Various islanding and non-islanding events are simulated, tested and it is evident from the results that it has a very small NDZ.

Maintaining the terminal voltage of a power system is a crucial process and this can be achieved via a system named automatic voltage regulator (AVR). However, an AVR needs an appropriate control method. In this context, this article proposes a novel Harris hawks optimization (HHO) and simulated annealing (SA) technique which can be used for AVR. The proposed optimization technique (HHO-SA) combines the good exploration feature of HHO with the exceptional local search feature of SA. The HHO-SA algorithm is introduced as a novel design method to obtain the optimum parameters for proportional + integral + derivative plus second order derivative (PID + DD) controller adopted in the AVR. Time domain objective function of the system is effectively minimized and the best PID + DD parameters are obtained. The analysis of statistical tests, convergence, transient and frequency responses, root locus, and disturbance rejection along with robustness are conducted for verifying the efficiency of the HHO-SA algorithm. Also, the performance of the HHO-SA tuned PID + DD controller on AVR is compared with the original HHO tuned PID + DD along with PID, FOPID, and PID + DD controllers that are adjusted by state-of-the-art metaheuristic methods. The practical implementation of the proposed controller is also demonstrated in this work. The extensive simulation results and comparisons with the existing controllers adopting the same set of data demonstrate the superior control performance and good robustness of the HHO-SA tuned PID + DD controller.

The $��{�H�}_{�\infty �}�$ control problem for networked semi-Markovian jump systems (S-MJSs) with generally incomplete time-varying transition probabilities (GITTPs) and distributed delays is addressed in this article. Firstly, the TPs considered herein may be exactly known, merely known with lower and upper bounds, or unknown, meanwhile the distributed delays own a probability density function as its kernel. Secondly, the closed-loop networked S-MJSs is established with GITTPSs and distributed delays. Thirdly, to make full use of the characteristic of delay probability distribution, a generalized discrete-Bessel summation inequality and a Lyapunov–Krasovskii functional (LKF) are developed by distributed kernel. Then, by applying the Lyapunov method with generalized summation inequality and utilizing an equivalent transformation method to deal with the unknown TPs, several sufficient conditions that guarantee a prescribed $��{�H�}_{�\infty �}�$ performance for networked S-MJSs are established. Eventually, two simulation examples including a single machine infinite bus power systems are presented to illustrate the effectiveness of the proposed theoretical findings.

In this article, we propose a set-membership based localization approach for mobile robots using infrastructure-based sensing. Under an assumption of known uncertainties bounds of the noise in the sensor measurement and robot motion models, the proposed method computes uncertainty sets that over-bound the robot 2D body and orientation via set-valued motion propagation and subsequent measurement update from infrastructure-based sensing. We establish theoretical properties and computational approaches for this set-theoretic localization method and illustrate its application to an automated valet parking example in simulations, and to omnidirectional robot localization problems in real-world experiments. With deteriorating uncertainties in system parameters and initialization parameters, we conduct sensitivity analysis and demonstrate that the proposed method, in comparison to the FastSLAM, has a milder performance degradation, thus is more robust against the changes in the parameters. Meanwhile, the proposed method can provide estimates with smaller standard deviation values.

Adaptive optimal-control and model prediction are proposed for a class of susceptible-infectious-removed dynamics according to the COVID-19 data. From the practical point of view, data sets of COVID-19 pandemics are daily collected and presented in a discrete-time sequence. Therefore, the discrete-time mathematical model of COVID-19 pandemics is formulated in this work. By developing the time-varying transmission rate, the model's accuracy is significantly contributed to the actual data of the COVID-19 pandemic. Furthermore, the interventional policy is derived by the proposed optimal controller when the closed-loop performance is guaranteed by theoretical aspects and numerical results.

The development of intelligent technology has led to the more frequent use of robots in industrial production, but the control problems in the redundant robotic arms of industrial robots have hindered their normal development. Therefore, in the study, in order to achieve automatic control of the redundant robotic arm of industrial robots, a multi-objective redundant robotic arm automatic control algorithm based on improved generalized weighted least squares and multi-objective feedback teaching and learning optimization algorithm is proposed. In the results, it is shown that the multi-objective optimization algorithm is effective for the control of the redundant robotic arm, and from the rectification results it is shown that the speed and position errors are only manifested at 0.005 s after time and there is no difference between its speed and position values. The above results show that for the automatic control of redundant robotic arms of industrial robots, the use of multi-objective optimization control is effective and can provide theoretical support for industrial production and industrial development, thus promoting the development of China's market economy.

A new method is presented for evaluating the performance of a nonlinear control allocation system within a linear control loop. To that end, a worst-case gain analysis problem is formulated that can be readily solved by means of well-established methods from robustness analysis using integral quadratic constraints (IQCs). It exploits the fact that control allocation systems are in general memoryless mappings that can be bounded by IQCs. A data-driven approach is used to find an optimal bound of the input/output mapping of the control allocation. Additionally, an iterative procedure based on local IQCs is introduced to determine meaningful sampling limits for less conservative yet accurate results. The effectiveness of the proposed data-driven performance analysis is shown at the example of an actively controlled flexible wing in a wind tunnel.

In fault diagnosis, we study model-based approaches to diagnose the discrete event system that efficiently represents the determined ambiguity, unpredictability, and the observation and judgment of several real-life problems. Diagnosability is a crucial task for system reliability. This article presents a strategy for verifying the diagnosability of discrete event systems (DESs) using conjunctive normal form (CNF). This strategy introduces CNF-based finite state machine (FSM). First, the scheme considers the model of the system for diagnosis, and CNF represents all transitions of the DES. CNF-based FSM constructs a diagnoser known as CNF-based diagnoser. Diagnoser tests whether faulty events can be detected or not in a given system model, that is, DES. The diagnoser verifies the diagnosability of the given DES-based FSM using the resolve rule. The construction of diagnoser and diagnosability verification with respect to a real-world industrial system is illustrated. The complexity of the diagnoser construction and diagnosability verification are shown to be efficient.

A magnetically levitated spindle was designed for fatigue testing of cylinders made of fiber reinforced plastic. In these fatigue tests, the speed of the cylinders is varied cyclically between 15,000 and 30,000 rpm until their mechanical failure occurs. Several eigenfrequencies have to be passed to reach the operational speed range. During long-term operation, the rotor of the spindle is prone to overheating due to various losses. One way of reducing the rotor temperature is to decrease the bias current of the radial active magnetic bearings. Since the bias current influences the dynamic behavior of the system, the control of the bearings has to be adapted as well. This article describes a controller design for the system with different bias currents to determine the smallest usable bias current. A detailed model of the plant is developed, which is then used to optimize the parameters of the utilized controller with a predefined structure using the weighted $��{\mathscr{H}}_{\infty }�$ norm as the objective function. Since the rotor is highly gyroscopic, its eigenfrequencies change with the rotational speed. To ensure that the system meets certain robustness criteria at all rotational speeds, the parameters of the controller are simultaneously optimized for the plant model at different speeds. This approach leads to a controller which can be used in the entire speed range without the need for gain scheduling. The functionality of the controller and the influence of the bias current on the rotor temperature are investigated through measurements.

Despite all the research being done in an attempt to bridge the gap between control systems and artificial intelligence, there is still an immense risk of failure and instability that exists. One particular application that this research will look into and expand on is aircraft control mechanisms. This article will examine the existing uncertainties within these systems that could be suspected as the cause of failure in the artificial control operation of an aircraft. This study will act as a further extension of research on the feedback linearization of an aircraft's control architecture using adaptive neural networks to decrease the probability of an uncontrolled error resulting from the nonlinearity of the aircraft's dynamic characteristics. The stability of previously implemented mechanisms to control aircraft systems will also be investigated. This research will require a thorough approach and understanding of various possible areas of malfunction and instability caused by multiple factors, including, external interferences and inefficiencies that accumulate within the controller that can mislead or cause an undesirable effect on the system. Examining similar areas where this study may be used for further research, while also discussing opportunities to apply these procedures to relatable applications will be analyzed, as it is of key importance for the progression of this technology.