Supervisory enhanced genetic algorithm control for indirect field-oriented induction motor drive

An alternative control scheme including an enhanced genetic algorithm controller (EGAC) and a supervisory controller is developed for nonlinear dynamical systems in this study. In the EGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct a main controller to search the optimum control effort under the possible occurrence of uncertainties. To ensure the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Compared with enunciated GA control methods, the proposed control scheme possesses some salient advantages of simple framework, less executing time and good self-organising properties even for the time-varying system because the simple solution representation and the error back-propagation genetic operation are utilised in the GA process. In addition, the proposed scheme is applied to the position control of a decoupling induction motor (IM) drive, whose effectiveness is verified by the numerical simulation and experimental results and whose advantages are presented in comparison with existing position control schemes.

An alternative control scheme including a directional genetic algorithm controller (DGAC) and a supervisory controller is developed to control the position of an electrical servo drive in this study. In the DGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct a main controller to search optimum control effort under possible occurrence of uncertainties. In order to ensure the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Compared with enunciated GA control methods, the proposed control scheme possesses some salient advantages of simple framework, fewer executing time and good self-organizing properties even for nonlinear dynamical system. The effectiveness is demonstrated by simulation results, and its advantages are indicated in comparison with other GA control schemes for a field-oriented control induction motor drive.

This work presents a novel speed control scheme for an induction motor (IM) using an adaptive supervisory differential cerebellar model articulation controller (ASDCMAC). The ASDCMAC has a supervisory controller and an adaptive differential cerebellar model articulation controller (ADCMAC), and the ASDCMAC is utilized as the speed controller. The supervisory controller monitors the control process to keep speed tracking error within a predefined range, and the ADCMAC learns and approximates system dynamics. The connective weights of ADCMAC are adjusted online, according to adaptive rules derived in Lyapunov stability theory, to ensure system stability. The robustness of the proposed ASDCMAC against parameter variations and external load torque disturbances is verified via simulations and experiments, respectively. Three control schemes, the ASDCMAC, fuzzy control, and PI control, are investigated experimentally, and a performance index, root mean square error (RMSE), is utilized for each scheme. The experimental results demonstrate that the ASDCMAC outperforms the two other control schemes with external load torque variations.

The signed real measure of regular languages has been introduced and validated in recent literature for quantitative analysis and synthesis of discrete-event supervisory (DES) control systems, where all events are assumed to be observable. This paper presents a modification of the language measure for supervisory control under partial observation and shows how to generalize the analysis when some of the events may not be observable at the supervisory level. Thus, for synthesis of DES control systems, the language measure of partially observable discrete-event processes is expressed in a closed form, which is structurally similar to that of completely observable discrete-event processes.

Supervisory Control and Rehabilitation. Are there Common Interests?

The paper proposes the design of the supervisory heuristic fuzzy control system applied to a Sequencing Batch Reactor (SBR) in the Wastewater Treatment Plant (WWTP). Based on real data records and the biological process and aeration system models, a control system for biological processes has been designed. The supervisory control system performs management of the reactor work cycle, determines the phase length, controls the sludge age, calculates the setpoint of dissolved oxygen, and adapts parameters of the lower control layer. The results of simulation tests for the case study wastewater treatment plant are presented.

It is well known that the performance of the (industry standard) field‐oriented control (FOC) for induction motors is highly sensitive to uncertainties in the rotor resistance. In this paper we describe how to use supervisory control to obtain an adaptive implementation of FOC for current‐fed machines. The unknown rotor resistance is assumed to belong to a discrete set, while the uncertain load torque ranges in a given compact set. Even though no restrictions area prioriimposed on the size of these sets, their definitions reflect the prior knowledge of the designer, which is effectively incorporated in the supervisory control algorithm. The supervisor selects from these sets values for the parameters to be applied to the FOC, a choice that is made by continuously comparing suitably defined performance signals. We prove that the proposed supervisor achievesglobal stabilizationof the system when the load torque is known to belong to a given finite set of values. Apparently, this is the first globally convergent adaptive algorithm for current‐fed machines which simply adds adaptation to the widely popular FOC and is not a radically new complicated controller, hence it is more likely to be adopted by practitioners. Some simulation results illustrate the properties of the algorithm. Copyright © 2001 John Wiley & Sons, Ltd.

A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

Today, the use of Ethernet-based protocols in industrial control systems (ICS) communications has led to the emergence of attacks based on information technology (IT) on supervisory control and data acquisition systems. In addition, the familiarity of Ethernet and TCP/IP protocols and the diversity and success of attacks on them raises security risks and cyber threats for ICS. This issue is compounded by the absence of encryption, authorization, and authentication mechanisms due to the development of industrial communications protocols only for performance purposes. Recent zero-day attacks, such as Triton, Stuxnet, Havex, Dragonfly, and Blackenergy, as well as the Ukraine cyber-attack, are possible because of the vulnerabilities of the systems; these attacksare carried by the protocols used in communication between PLC and I/O units or HMI and engineering stations. It is evident that there is a need for robust solutions that detect and prevent protocol-based cyber threats. In this paper, machine learning methods are evaluated for anomaly detection, particularly for EtherCAT-based ICS. To the best of the author's knowledge, there has been no research focusing on machine learning algorithms for anomaly detection of EtherCAT. Before testing anomaly detection, an EtherCAT-based water level control system testbed was developed. Then, a total of 16 events were generated in four categories and applied on the testbed. The dataset created was used for anomaly detection. The results showed that the k-nearest neighbors (k-NN) and support vector machine with genetic algorithm (SVM GA) models perform best among the 18 techniques applied. In addition to detecting anomalies, the methods are able to flag the attack types better than other techniques and are applicable in EtherCAT networks. Also, the dataset and events can be used for further studies since it is difficult to obtain data for ICS due to its critical infrastructure and continuous real-time operation.

Knowledge-Based Real-Time Control

The economic aspects of the online, programmable computer for supervisory control of producing operations are discussed. The material presented reflects the performance and economics of a pilot supervisory control installation that has been supervising and controlling producing operations for one year in two widely separated fields in southeastern New Mexico. Expansion of this pilot supervisory system is necessary to obtain an actual payout, but the performance of the pilot project does provide a reliable basis for predicting the economics of full scale computer controlled producing operations. Introduction A pilot installation to evaluate a supervisory, monitoring and control system for properties operated in southeastern New Mexico by Continental Oil Co. was installed in Oct., 1964. The pilot project included 56 wells in two fields to be controlled from the district office in Hobbs, N. M. Results of the pilot project would serve to evaluate the economics of expanding the system to include three additional areas and 326 additional wells in a full scale operation, with possible future expansion to a total of 600 to 700 wells. The pilot installation and three additional areas to be included in the full scale project are shown in Fig. 1.This supervisory control system is similar in concept to an earlier system installed during 1962 in the Big Horn Basin of Wyoming by the Rocky Mountain Region of Conoco. The pilot project has been sufficiently complex to prove equipment capability and performance, but limited enough to keep installation costs within reason should results indicate the total project to be economically unattractive leases in the Hobbs and El Mar fields of New Mexico comprise this two-field pilot project. These areas are separated by approximately 56 air miles (Fig. 1). The Hobbs field, located two miles from the central supervisory in the city of Hobbs, consists of three leases with 14 flowing or pumping wells producing 5 1 8 BOPD into one central tank battery. The El Mar field includes three leases with 42 pumping or flowing wells producing 700 BOPD into a central tank battery. The objective of the pilot program was to determine if a significant portion of the pumper functions could be performed by the supervisory system while maintaining producing efficiency at a level equal to or greater than before. It was recognized that not all of the pumper duties could be performed by computer control but that almost all of the check-and-see type routine could be surveyed by the system. The majority of pumper time involves travel and inspection duties; therefore, it was estimated that approximately 50 per cent of his assignments could be supervised by the control system. JPT P. 48ˆ

Induction motor is a cast of alternating current motor where charge endures allotted to the rotor close-at-hand deputation of conductive charge. These motors are broadly applied in industrial claim due to they are arduous along with adhere no contacts. The speed controller of deltoid phase induction motor is applied to alleviate the aberration of speed. The central constructivist of this paper is to accrue the performance of speed sensorless control of three phase induction motor. To increase its performance, this paper presents a modified method for speed controller of an indirect vector-controlled induction motor drive using cloud computing technique. Our methodology depends on speed sensorless scheme to obtain the speed signal feedback; the speed estimator is based on model reference adaptive control that uses the stator current and rotor flux as state variables for estimating the speed. In this method, the stator current error is represented as a function of first degree of the estimated speed error. An analysis and simulation of the tried algorithm is birthed and applied easing a TMS320C31 floating-point notational alert Processor. And accumulate the action of the three phase induction motor we conceived our appraisals affixed to the accountant based on cloud computing tactics. This intelligent policy uses the guidelines of the speed controller efficiently. Simulation and experimental results depicted that the motor speed is decelerated articulately to destine its illusion apprise without above and inferior smack and with about zero steady state error. The apprised accelerate alert and its dispatching buoy amassed off line from burlesque. After effects display an advantageous affinity among the accounted speed alert and it's dispatching allocated as well as aped speed flares.

This paper displays a unique adaptable Neuro-Fuzzy Controller (NFC)-based speed control for three-phase induction motor drive. The suggested NFC integrates fuzzy logic idea with a four-layer Artificial Neural Network (ANN). Speed and change in speed are sent as input to Neuro-Fuzzy Controller and it winds up noticeably fit for real-time electromechanical drives. The complete simulation model for indirect vector control of induction motor including the suggested NFC is developed. Induction motor assumes an imperative part in the field of electric drives. Without genuine controlling of the speed, it is difficult to accomplish required errand for specific application. AC motors are solid, less cost, reliable, and maintenance free. In light of absence of capacity of regular control strategies like PID and PI controllers to work in a broad range of operations, AI-based controllers are extensively utilized as a part of the industry like Neural Networks, Fuzzy Logic, and Neuro-Fuzzy controller. The principle issue with the typical fuzzy-based controllers is that the parameters related with the membership functions and the rules depend predominantly on instinct of the specialists, fuzzy logic cannot naturally get the rules utilized for settling on the decision, however, great at clarifying the decision. To overcome from this issue, Neuro-Fuzzy Controller [ability to learn without anyone else alongside decision-making] is recommended. Keeping in mind the end goal to prove the predominance of the proposed Neuro-Fuzzy Controller, the results of the suggested NFC technique is compared with the results of PI controller. NFC-based control of induction motor will end up being more trustworthy than other conventional control techniques.

Material handling systems, as an important part of different type of manufacturing processes, face the same challenges as manufacturing industries pushed nowadays forward by innovative ideas and technologies to the next level loudly announced as industry 4.0. Development of the next generation of automated manufacturing systems involves advanced approaches to material handling systems design and their close integration with the higher levels of manufacturing and production control and management, e.g. manufacturing execution systems (MES), enterprise resource planning (ERP). In the presence of increasing demands for manufacturing process optimization, the role of supervisory level of material handling systems is much more advanced today, ensuring not only data acquisition, visualization, monitoring, supervisory control, as well as synchronization with the higher control levels (FEM, ERP), but also providing functionality for supporting maintenance and decision-making processes to reduce downtimes, operations and maintenance costs. The article deals with the integration of control and maintenance functions in the hierarchical control system of a crane. The supervisory system for supporting control and proactive maintenance is prototyped at the laboratory overhead travelling crane. The article presents the control-measurement equipment and intelligent software tools implemented in the supervisory control and data acquisition (SCADA) system to aid decision-making process in proactive maintenance. The overview of the main components of the supervisory control and proactive maintenance subsystems is provided, and their respective role in control, supervision, and proactive maintenance is explained. The crane’s supervisory control includes the stereovision-based subsystem applied to identify the crane’s transportation workspace, determine the safety and time-optimal point-to-point trajectory of a payload. The proactive maintenance module consists of the human machine interface (HMI) supporting decision-making process, intelligent tools for upcoming downtime/failure prediction, and the crane's girder inspection using the metal magnetic memory technique.

Control of high performance induction motors: theory and practice