Control Algorithm For Systems Research Articles

An Initialization-free Distributed Algorithm for Power Dispatch Problem with Multiple Resources of Future Distribution Network

With the gradual development of bidirectional interacted future distribution network, it is necessary to enter distributed clean power sources with various characteristics, including wind turbine and solar panel. Traditional centralized control has difficulties to fulfill the demands of future distribution networks for safe, stable, and efficient operation. Aiming at the constrained power allocation problem widely studied in smart grid, with the cooperative control algorithm of continuous multi-agent system, this paper proposed a distributed optimization allocation strategy, which is free by the initial state. The proposed distributed algorithm implements parameterization by adding auxiliary variables. In the iterative process, the algorithm only needs to know the state of the distributed power supply of the neighbor, and finally solve the global optimal solution of the system. The simulations prove that the proposed scheme can effectively improve the economic dispatch performance. Furthermore, comparing to the existing algorithm, the proposed algorithm achieves faster optimal solution.

A Covariance Feedback Approach to Covariance Control of Nonlinear Stochastic Systems

In this paper, the covariance control algorithm for nonlinear stochastic systems using covariance feedback is studied. Covariance control of nonlinear systems scenario involves the theory of covariance control based on the idea of the covariance feedback. Therefore, the proposed covariance control algorithm is derived for our case, firstly by applying the covariance control method and linear approximation of nonlinear systems, and then it is achieved by adopting this method for a class of nonlinear stochastic systems by using feedback linearization idea and a covariance feedback controller. The effectiveness of the proposed covariance feedback algorithm is studied using numerous simulations concerning different nonlinear case studies.

Support Vector Machines for Providing Selectivity of Distance Protection Backup Zone

The development of present-day power systems is associated with the wide use of digital technologies and intelligent algorithms in control and protection systems. It opens up new opportunities to improve relay protection and automation hardware and develop its design principles. Simulation modeling becomes a new tool not only for studying power systems operation but also for designing new relay protection methods. The use of simulation modeling in combination with machine learning algorithms makes it possible to create fundamentally new types of digital relay protections adaptable to a specific protected facility and able to use all the available current and voltage measurements to the fullest extent possible. Machine learning also allows developing auxiliary selective elements for improving the basic characteristics of existing relay protection algorithms such as selectivity, sensitivity, and speed of operation. The paper considers an example of designing an auxiliary element to provide selectivity in the backup zone of distance protection. The problem is solved using one of the most widely known machine learning techniques, i.e., the method of support vector machines (SVM).

A Probability-Based Short-Term Velocity Prediction Method for Energy-Efficient Cruise Control

Numerous automotive control systems depend on the prediction of the velocity of the controlled vehicle or other road users in close proximity. A forecast is indispensable for the design and operation of model predictive control strategies which are utilized for example in order to optimize fuel economy in cruise control systems. In this article, a novel velocity time series prediction method, which depends exclusively on past velocity measurements, is presented and analyzed. It is based on conditional probabilities of acceleration and deceleration, which are estimated from real driving data. The proposed velocity prediction is used in a new model predictive control algorithm for an adaptive cruise control system for heavy duty vehicles, where the predicted future velocity of a preceding vehicle is needed, as well as its probability distribution. Simulation results show that an accurate velocity prediction is crucial for achieving energy-efficiency goals.

PetriNet Editor + PetriNet Engine: New Software Tool For Modelling and Control of Discrete Event Systems Using Petri Nets and Code Generation

Petri nets are an important tool for creation of new platforms for digitised production systems due to their versatility in modelling discrete event systems. For the development of modern complex production processes for Industry 4.0, using advanced mathematical models based on Petri nets is an appropriate and effective option. The main aim of the proposed article is to design a new software tool for modelling and control of discrete event systems using Arduino-type microcontrollers and code generation techniques. To accomplish this task, a new tool called “PetriNet editor + PetriNet engine” based on Petri nets is proposed able to generate the code for the microcontroller according to the modelled Petri net. The developed software tool was successfully verified in control of a laboratory plant. Offering a graphical environment for the design of discrete event system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0).

Adaptation of Algorithms for Diagnostics of Steam Turbine Unit Equipment to Specific Conditions at Thermal Power Stations

At present, steam turbine manufacturers are interested in delivering diagnostic equipment systems for steam turbine units (STU) together with automatic control systems. These diagnostic systems should be adapted to a specific turbine size (or modification). The diagnostic system adaptation depends on the algorithm type used to detect defects. The following types of algorithms can be singled out: computational ones determining the defect using a computational model; expert ones based on probabilistic methods (using the summation of weighting coefficients of malfunction symptoms and the Bayes theorem); and digital ones based on the phenomenological relationships among process parameters, equipment state parameters, and other indicators obtained over the test period of equipment’s operation. The computational algorithms can be integrated into the turbine APCS and executed online (such as control system algorithms and steam admission system algorithms) or be used for handling postoperative tasks (such as diagnostics of auxiliary equipment or solving technical and economic problems). Expert algorithms are employed when development of a computational model involves great difficulties (e.g., in diagnosing a turbine thermal-expansion monitoring system or vibration-monitoring system). Digital algorithms are not related to the type of tasks, to specific equipment, or its features or operating modes. The degree of definiteness of a detected defect depends on the type of algorithm. The need to adapt diagnostic systems to other equipment involves not only the development of new diagnostic algorithms but also the formalization of the discourse. By this is meant the selection and justification of a standard state (prototype), i.e., a model state, to be used as a reference for development of diagnostic symptoms and a digital description of defect symptoms determined from the measurements and expert observations and opinions required for diagnosing.

A frequency band constrained filtered-x least mean square algorithm for feedback active control systems.

The generalized leaky filtered-x least mean square (GLFxLMS) algorithm can reduce the noise amplification caused by the waterbed effect in feedback active control systems effectively; however, it suffers from a high computation complexity. Hence, a frequency band constrained filtered-x least mean square algorithm is proposed to reduce the computation complexity of the GLFxLMS algorithm by replacing the penalty term containing a symmetric Toeplitz matrix in the cost function with the mean square of a penalty signal. The simulation results based on the measured transfer functions of an active headrest system show that the proposed algorithm has the same performance as the GLFxLMS algorithm, but with much lower computation complexity.

Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance

This study developed a control algorithm of torque vectoring system to improve the handling performance of a green-car and evaluate the performance of vehicle dynamics to improve the controllability and stability. Firstly, this study configured the control algorithm with the supervisory controller that decides the control mode by checking the current driving status of a vehicle, target yawrate calculator that reflects the steady-state and transient-state response characteristics according to the control mode, as well as the reference yawrate calculation, desired yaw moment calculation and transferred torque calculation. The control mode consists of the agile mode that controls the torque vectoring to improve the controllability and the safe mode that controls the torque vectoring to improve stability. Secondly, this study performed the modeling of the dual motor type torque vectoring system and an EV AWD vehicle. Lastly, this study defined the driving test scenario and evaluation method as well as the quantitative performance index for each vehicle test and established the co-simulation environment for the handling test evaluation. This study found that when a vehicle is controlled by applying the torque vectoring system, handling performance was improved according to controllability and safe mode by verifying the simulation.

Improvement of Quadruple Structure and Optimization of Control Algorithm for Co-Phase Traction Power Supply System

Co-phase power supply is one of the key technologies to solve the technical bottlenecks such as electrical phase separation zone, poor power quality and defects in structure and control algorithms in traditional traction power supply systems and single co-phase power supply schemes, and an inevitable way to realize the development of electrified railways in the direction of safety, high speed and heavy load. Based on the single co-phase power supply technology, a novel quadruple co-phase power supply scheme with negative sequence elimination and to suppress reactive power and harmonic better for the two aspects of the system structure improvement and control algorithm optimization is proposed by combining the technologies of power system flexible transmission grid-connected and multiple inverters. Finally, the simulation model for the novel co-phase traction power supply system was designed and built, and the present method was verified by a set of simulation experiments so as to obtain the expected results.

Stabilization and Tracking Control Algorithms for VTOL Aircraft: Theoretical and Practical Overview

Control theory applied to multirotor aerial systems (MAS) has gained attention with the recent increase on the power computation for embedded systems. These systems are now able to perform the calculations needed for a variety of control techniques, with lower cost of sensors and actuators. These types of control algorithms are applied to the position and the attitude of MAS. In this paper, a brief overview evaluation of popular control algorithms for multirotor aerial systems, especially for VTOL - Vertical Take-Off and Landing aircraft, is presented. The main objective is to provide a unified and accessible analysis, placing the classical model of the VTOL vehicle and the studied control methods into a proper context. In addition, to provide the basis for beginner users working in aerial vehicles. In addition, this work contributes in presenting a comprehensive analysis of the implementation for the Nonlinear and Linear Backstepping, Nested Saturation and the Hyperbolic Bounded Controllers. These techniques are selected and compared to evaluate the performance of the aircraft, by simulations and experimental studies.

Recent Progress on Control Techniques of Shaking Table and Array Systems in China: An Overview

Shaking table is one of the important equipments for seismic research. It can accurately reproduce the seismic wave in the laboratory to accurately study the structural response. Based on the summary of domestic shaking table and array system control algorithms, the applications of various control algorithms and their advantages and disadvantages are summarized. The development trend of the shaking table control algorithm is analyzed and explained. It can be used as a reference for research on control technology of shaking tables at home and abroad.

Adaptive Maximum Torque per Ampere Control of Sensorless Permanent Magnet Motor Drives

Interior permanent magnet synchronous motor (IPMSM) efficiency can be improved by using maximum torque per ampere control (MTPA). MTPA control utilizes both alignment and reluctance torques and usually requires information about the magnetization map of the electrical machine. This paper proposes an adaptive MTPA algorithm for sensorless control systems of IPMSM drives, which is applicable in industrial and commercial drives. This algorithm enhances conventional control schemes, where the output of the speed controller is the commanded stator current and the direct current is calculated using an MTPA equation; therefore, it can be easily implemented in the previously developed drives. The proposed algorithm does not use any motor parameters for the calculation of the MTPA trajectory, which is important for systems operating in changing environmental conditions, because motor inductances and flux linkage strongly depend on the stator current and the rotor temperature, respectively. The proposed algorithm continuously varies the current phase and in such a way it tries to minimize the magnitude of the stator current at the applied load torque. The main contribution of this paper is the development of a technique to overcome the main disadvantage of seeking algorithms–the necessity of a precision information about the rotor position. The proposed method was verified experimentally.

Neural Network–Based Event-Triggered Adaptive Control Algorithms for Uncertain Nonlinear Systems with Actuator Failures

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network–based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.

A HOME APPLIANCE CONTROL SYSTEM FOR ENERGY MANAGEMENT

In this work, a Home Appliance Control System is proposed as a Home Energy Management System for preventing high peak demand and reducing electricity cost while keeping user comfort. The proposed Home Appliance Control System consists of a home equipped with smart appliances, grid, communication network and a Main Controller and it is based on appliance interference under certain conditions. Appliances are separated as controllable, semi-controllable and uncontrollable according to their effects on user comfort. Main Controller is not allowed to interfere with uncontrollable appliances, but with semi-controllable and controllable appliances as much as it is permitted. Appliance interference is done on real time in the order of appliance priority defined by the user due to expected power consumption and the grid limit. The corresponding control algorithm of Home Appliance Control System is also developed. Simulation results demonstrate the effectiveness of the proposed system on achieving the intended goals.

Sliding mode prediction fault-tolerant control method for multi-delay uncertain discrete system with sensor fault

In this paper, we design a novel sliding mode prediction fault-tolerant control algorithm for multi-delays discrete uncertain systems with sensor fault. The global sliding surface is designed to replace the traditional linear sliding surface as a predictive model to ensure the global robustness of the system. For sensor fault and sliding mode buffeting, a power-dependent function reference trajectory with fault compensation is designed to attenuate chattering and achieve better stability. In the process of rolling optimization, an improved whale optimization algorithm is developed. On the premise of obtaining good convergence speed and accuracy, the optimization process can avoid falling into the local minimum value and solve the problem of premature convergence. Finally, the comparison experiments on the quad-rotor simulation platform prove the rationality and superiority of the algorithm.

Completely distributed neuro-learning consensus with position constraints and partially unknown control directions

In this paper, we propose a new neural network-based iterative learning leader-less consensus control algorithm for the second-order uncertain time-varying nonlinear multi-agent systems, where the position of each agent is constrained in an open interval which can be pre-selected and the control direction can be non-identical and partially unknown. With the help of a novel barrier composite energy function and multiple piece-wise Nussbaum functions, the designed completely distributed learning protocols can make the positions and velocities of the agents achieve the exact consensus on a finite time interval as the iteration number increases to infinity. Meanwhile, the position trajectories of the agents at each iteration do not exceed the constraints bounds. Besides, to testify the feasibility and utility of the presented algorithm, two simulation examples are given in the end.

Analysis of the Dynamic Characteristics of Options for the Automatic Load-Frequency Control System of 800 MW Power Unit 1 of the Perm TPP Islanded to Energize a Power District

The results of using a mathematical model to study advanced algorithms for the automatic load-frequency control (ALFC) system of 800 MWPower Unit 1 of the Perm TPP islanded as part of the Perm-Zakamsky energy hub from the UES of Russia are presented. To prevent long-term oscillations of power and frequency of the power unit during power surges due to the reduced capacity of the power lines, advanced ALFC algorithms are proposed and analyzed. A criterion for the activation of these algorithms is proposed, and the optimal one is chosen.

The use of fuzzy logic for the clean-up systems control for bunkers, containing bulk solids

The issues of eliminating the bulk solids overhanging in bunkers using pneumatic pulse devices are considered in the following article. An algorithm for automatic control of pneumatic collapse systems based on the use of fuzzy logic methods is proposed. A constructive solution for this algorithm is given, based on the assessment of the level of bulk material on the conveyor installed at the exit of the hopper. The Mamdani algorithm is used as a fuzzy inference algorithm. The fuzzy Logic Toolbox package of the MATLAB computing environment was used for modeling fuzzy systems. The use of mid-maximum and center-of-gravity methods as a defuzzification method is considered. It is demonstrated that the use of fuzzy logic makes it possible to significantly simplify the development of the control system algorithm.

Оптимизация алгоритмов системы управления электровозов 3ЭС5К с поосным регулированием силы тяги

Оптимизация алгоритмов системы управления электровозов 3ЭС5К с поосным регулированием силы тяги

An intelligent controller design based on the neuroendocrine algorithm for the plasma density control system on Tokamak devices

Abstract Realization of real-time and accurate control of plasma electron density is one of the key points for the long-time steady-state operation of Tokamak. Based on the mechanism model of the neuroendocrine regulatory principle, this paper designs an intelligent controller and studies its application on the plasma density control system (PDCS) for the J-TEXT Tokamak. This PDCS uses the HCN laser interferometer to obtain the density signal, and controls the gas puffing valve through the proposed intelligent control algorithm to achieve accurate density control. The controller mainly includes a hypothalamic regulation module, a single neuron proportional-integral-differential (PID) control module and an ultrashort feedback module. It is designed and referenced to the long feedback, short feedback, and ultra-short feedback loop mechanisms of the human neuroendocrine hormone regulation. The proposed controller makes the PID parameters be adjusted on-line in real-time and achieves rapid and stable elimination of errors. The simulations and experimental results show that the proposed method, compared with the conventional PID control method, can synthetically improve the performance of the control system.