Set Of Control Parameters Research Articles

Relay feedback identification and PI-PD control of asymmetrical heating and cooling processes

A common type of nonlinear system in the field of process industry is asymmetrical heating and cooling processes. These systems have two operating modes, heating and cooling, each with a particular dynamic feature. This study deals with identifying and controlling such processes. The relay feedback identification method based on the state-space approach, which results in exact estimates, is used in the identification procedure to achieve two first-order plus dead time (FOPDT) process models. Following process identification, two sets of PI-PD controller parameters are acquired in line with the user-defined gain and phase margin specifications, and the system is controlled using a gain-scheduled PI-PD control method. Some simulation examples are provided to demonstrate the use and profitability of the suggested identification and control approaches. Comparisons with a similar study are provided for both identification and control approaches to clearly demonstrate the benefits of the suggested procedures.

К выбору параметров регулятора непосредственного понижающего преобразователя с одноконтурной системой управления с учетом динамических нелинейностей

The article is devoted to the issues of choosing the parameters of a proportional-integral-differentiating regulator of a single loop control system with a buck converter taking into account dynamic nonlinearities This problem arises when it is necessary to design pulsed voltage conver¬ters with increased performance, when it is necessary to increase the cutoff frequency of an open-loop control loop without the possibility of increasing the carrier frequency of pulse width modulation due to limitations of the element base, which in some cases leads to the appearance of nonlinear effects. This must be taken into account when choosing both the open-loop cutoff frequency and the stability margin. The article proposes a nonlinear dynamic model of a direct buck converter with a proportional-integral-differentiating regulator both in the form of a piecewise smooth system of differential equations and in the form of a Poincaré map. A method for selecting controller parameters is proposed for a given open-loop cutoff frequency, and a given stability margin, as well as for given ranges of input voltage and load resistance based on the use of numerical methods for solving systems of nonlinear equations with given restrictions. The nonlinear dynamics of the system was studied for various sets of controller parameters. It is shown that with an increase in the cutoff frequency and small stability margins, undesirable dynamic modes may arise in the system when the filter capacitance drops within the calculated values, while linear dynamic models show the stability of the system. To eliminate the possibility of the emergence of undesirable modes, it is necessary to increase the stability margin with subsequent monitoring of the results using a nonlinear dynamic model. The proposed method for selecting the cutoff frequency of an open loop and the phase stability margin, based on the analysis of nonlinear dyna¬mics, allows you to design power supplies with increased performance.

A fast and robust sliding mode controller for automatic voltage regulators in electrical power systems

In this paper, a fast and robust sliding mode controller (SMC) based on a novel multi-variable sliding function is proposed for Automatic Voltage Regulators (AVRs) in electrical power systems. Lyapunov stability theorem is used to guarantee the asymptotic stability of the proposed controller. As the proposed controller is multi-variable, there may be different optimum controller parameter sets that result in performance close to each other. Hence, two optimum sets of parameters are explored by two well-known algorithms, namely, Sine Cosine Algorithm and Grey Wolf Optimization. A new objective function is considered for the optimizations, including an overshoot constraint. The performance of the proposed controller with the optimum parameter sets is evaluated for five different parameter uncertainties and load disturbance scenarios, and the resulting time domain measurements are compared with the results of recently published SMCs. A 38.36 % improvement in settling time was observed compared to the most recent study using the same realistic AVR model. In addition, the proposed controller suppressed different load disturbances, including parasitic ones, and quickly recovered the AVR output. All results revealed that the proposed controller achieved the best performance in terms of speed and robustness compared to existing controllers, despite the controller and exciter limitations.

An enhanced adaptive differential evolution algorithm with dual performance evaluation metrics for numerical optimization

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer.

Hyper-Heuristic Approach for Tuning Parameter Adaptation in Differential Evolution

Differential evolution (DE) is one of the most promising black-box numerical optimization methods. However, DE algorithms suffer from the problem of control parameter settings. Various adaptation methods have been proposed, with success history-based adaptation being the most popular. However, hand-crafted designs are known to suffer from human perception bias. In this study, our aim is to design automatically a parameter adaptation method for DE with the use of the hyper-heuristic approach. In particular, we consider the adaptation of scaling factor F, which is the most sensitive parameter of DE algorithms. In order to propose a flexible approach, a Taylor series expansion is used to represent the dependence between the success rate of the algorithm during its run and the scaling factor value. Moreover, two Taylor series are used for the mean of the random distribution for sampling F and its standard deviation. Unlike most studies, the Student’s t distribution is applied, and the number of degrees of freedom is also tuned. As a tuning method, another DE algorithm is used. The experiments performed on a recently proposed L-NTADE algorithm and two benchmark sets, CEC 2017 and CEC 2022, show that there is a relatively simple adaptation technique with the scaling factor changing between 0.4 and 0.6, which enables us to achieve high performance in most scenarios. It is shown that the automatically designed heuristic can be efficiently approximated by two simple equations, without a loss of efficiency.

A Bottom-Up Methodology for Identifying Key Performance Indicators for Sustainability Monitoring of Unit Manufacturing Processes

With growing environmental concerns and regulatory requirements, manufacturers are increasingly required to monitor and reduce the environmental impacts of their production processes. Despite increasing digitalization and data-collection capabilities, manufacturers are challenged in collecting the right data and framing process improvement targets. To address this challenge, this paper presents a bottom-up methodology based on the life cycle assessment for identifying performance indicators with the goal of monitoring and reducing the overall environmental impacts of a manufacturing process. More specifically, process performance indicators are defined as a set of controllable process parameters, and their suitability for sustainability monitoring is evaluated based on their sensitivity, measurability, actionability, reliability, timeliness, and human-centricity with respect to a chosen environmental impact category. The bottom-up formulation of process performance indicators is demonstrated through a real-world case study on an infeed centerless grinding process in a large manufacturing company. Results from the case study show that the process performance indicators with regards to climate change impacts included (i) reduction in grinding time, (ii) reduction in total grinding power, (iii) reduction in sparkout time, and (iv) increase in batch size.

Design of Learning Algorithm Engineering Electrical Control System based on Fuzzy Neural Network

The traditional electrical control system has the problem of insufficient function control. This paper designs the engineering electrical control system through the learning algorithm based on fuzzy neural network. The control strategy design module combines fuzzy reasoning with neural network to design intelligent control strategy. Develop a variety of control principles and strategies that can meet the needs and characteristics of the system. The user interface module assists users to monitor and adjust the system, and has the functions of real-time data display and setting control parameters. Fuzzy neural network modeling module combines fuzzy logic with neural network to model the system. In this paper, the sliding window technology is used to collect and process data in real time. When the running time is 1 hour, the system output value is 100.2 and the output fluctuation value is 0.6; When the running time is 2 hours, the system output value is 99.8 and the output fluctuation value is 0.7. The learning algorithm based on fuzzy neural network has little fluctuation and stable performance.

Data-Based Control Synthesis and Performance Assessment for Moored Wave Energy Conversion Systems: The PeWEC Case

With a model-based control strategy, the effectiveness of the associated control action depends on the availability of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representative</i> control-oriented model. In the case of floating offshore wave energy converters (WECs), the device response depends upon the interaction between mooring system, any mechanical parts, and the hydrodynamics of the floating body. This study proposes an approach to synthesise WEC controllers under the effect of mooring forces building a representative data-based linear model able to include any relevant dynamics. Moreover, the procedure is tested on the moored pendulum wave energy converter (PeWEC) by means of a high-fidelity mooring solver, OrcaFlex (OF). In particular, the control action is computed with and without knowledge of the mooring influence, in order to analyse and elucidate the effect of the station-keeping system on the harvested energy. The performance assessment of the device is achieved by evaluating device power on the resource scatter characterising Pantelleria, Italy. The results show the relevance of the mooring dynamics on the device response and final set of control parameters and, hence, a significant influence of the station-keeping system on control synthesis and extracted mechanical power.

Periodicity and chaos of thermal convective flows in annular cylindrical domains using the method of isolation by spectral expansions

The dynamical and chaotic behaviors of natural convection flow in the semi-annular and full-annular cylindrical domains are studied using the method of isolation by spectral expansion. For each of the flow cases, spectral models with different orders are obtained and dynamical characteristics of the flow are investigated using numerical simulations. For the semi-annular case of study, results reveal that in all the generated spectral models, chaotic solution appears, but the onset of chaos between each system requires different set of control parameters. Bifurcation diagrams are provided for all the systems which show the ranges of periodic and chaotic behavior of the flow. Strange attractors are captured for all the systems with different orders. For the lowest-order model, a Lorenz-like attractor is produced, which has two distinguishable scrolls similar to Lorenz's famous attractor and for the other high-order models, the shape of strange attractor gets different. For the full-annular case of study, the physical characteristics of the flow are obtained. The results show that the produced thermal plume on the top of annulus gets higher fluctuations as the flow Rayleigh number gets higher. At a specific Rayleigh number, the flow behavior gets completely unstable, which shows a transitional regime change. At this state of flow, the domain is dominated by several vortices.

Cooperative optimal operation of hybrid energy integrated system considering multi‐objective dragonfly algorithm

AbstractTo meet the recent energy demand, hybrid energy systems (HESs) play an important role in providing more stable power as well as backup power to the grid. It is always preferable for the power system network to be operated in a secure, dependable, stable, and cost‐effective manner. The optimal power flow (OPF) problem is used to determine the ideal power system network control parameter settings. The majority of the OPF problem, fuel cost reduction is treated as an objective function, but the environmental impact of the generating is ignored. Both fuel expense and emission are treated as objective functions of the work in this case. This research describes a solution for multi‐objective optimal power flow (MOOPF) with a HES integrated conventional power system. HES in this work is made up of a wind park and a compressed air energy storage system (CAES). In this case, locational marginal pricing (LMP) is employed to determine the best location of HES in the power system. To achieve the goals of this work, the multi‐objective dragonfly algorithm (MODA) is used. The IEEE 30 bus system is used to evaluate the MODA. The MODA method findings are validated against other well‐known optimization algorithms such as the multi‐objective ant colony system (MOACS) algorithm, multi‐objective enhanced self‐adaptive differential evolution (MOESDE) algorithm, modified multi‐objective evolutionary algorithm‐based decomposition (MOEA/D), multi‐objective particle swarm algorithm (MOPOSO), and non‐dominated sorting genetic algorithm (NSGA‐II).

Identification of Control Parameters Using Taguchi Method for Hybrid Real-Binary Differential Evolution Algorithm and Its Applications in Electromagnetic Optimization

The hybrid real-binary differential evolution (HDE) algorithm has been proficient in addressing electromagnetic optimization problems (EOPs) involving both real and binary variables. However, its optimization performance on different control parameter (CP) settings is not further studied, and the method to determine the values of CPs is more likely to use the trial-and-error method, which lacks universality on both unimodal and multimodal benchmarks. To completely account for the effect of CPs in HDE, the Taguchi method is utilized to identify the values of each CP. The orthogonal experiment result is the average rank of the mean values of 23 benchmark functions obtained by HDE and other classic optimization algorithms. Based on the analysis of variance results, three CPs that have a major effect on the performance of HDE are selected, and each of them is changed from level 1 to level 5 to further obtain the best combination of CPs, which is indicated as HDEN1. To further enhance the local search ability of HDEN1 for the global best, a modified algorithm (HDEN2) is proposed based on a novel mutation strategy selection method, and the simulation results demonstrate that the minimum values obtained by HDEN2 are smaller than those obtained by HDEN1. Two EOPs, including planar microwave absorber and Yagi-Uda antenna designs, are solved to validate the performance of HDEN1 and HDEN2. The results reveal that the HDEN1 and HDEN2 outperform HDE, demonstrating the efficacy of the proposed method for identifying the CPs of HDE. In the end, a low profile and wideband RCS reduction pixelated checkboard metasurface is optimized utilizing the HDEN2, proving that the proposed algorithm can be a good candidate for hybrid real-binary electromagnetic problems.

ASSESSING AND FORECASTING THE STATE OF DETERIORATING SYSTEMS WITH THE USE OF MODIFIED REGRESSION POLYNOMIALS ON THE BASIS OF FUNCTIONAL APPROXIMATION OF THEIR COEFFICIENTS

Object of research is technical state of deteriorating systems whose operating conditions depend on a large number of interacting factors. The caused inhomogeneity of the sample of initial data on the technical state leads to impossibility of correct use of traditional methods of assessing the state of a system (meaning methods using mathematical tools of regression analysis). Subject of research is developing a method for constructing a regression polynomial based on the results of processing a set of controlled system parameters. Non-linearity of the polynomial describing the evolution of the technical state of real systems leads to an increase in the number of regression polynomial coefficients subject to estimation. The problem is further complicated by the growing number of factors affecting the technical state of the system. In these circumstances, the so-called &lt;small sample effect&gt; occurs. Goal the research consists in developing a method for constructing an approximation polynomial that describes evolution of the system state in a situation where the volume of the initial data sample is insufficient for correct estimating coefficients of this polynomial. The results obtained. The paper proposes a method for solving the given problem, based on implementation of a two-stage procedure. At the first stage a functional description of the approximation polynomial coefficients is performed; and this radically reduces the number of regression polynomial parameters to be estimated. This polynomial is used for preliminary estimation of its coefficients with the aim of filtering out insignificant factors and their interactions. At the second stage, parameters of the truncated polynomial are estimated by means of using standard technologies of mathematical statistics. Two approaches to constructing a modified polynomial have been studied: the additive one and the multiplicative one. It has been shown that the additive approach is, on average, an order of magnitude more effective than the multiplicative one.

Adaptive Ant Colony Optimization With Node Clustering for the Multidepot Vehicle Routing Problem

This article deals with the novel metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle. It implements several novel mechanisms that improve its overall performance, lower the optimization time, and reduce the negative behavior which is typically connected with ACO-based algorithms (such as prematurely falling into local optima, or the impact of setting of control parameters on the convergence for different problem configurations). The most significant novel techniques, implemented for the first time to solve the Multi-Depot Vehicle Routing Problem (MDVRP), are as follows: (a) node clustering where transition vertices are organized into a set of candidate lists called clusters; (b) adaptive pheromone evaporation which is adapted during optimization according to the diversity of the population of ant solutions (measured by information entropy). Moreover, a new termination condition, based also on the population diversity, is formulated. The effectiveness of the proposed algorithm for the MDVRP is evaluated via a set of experiments on 23 well-known benchmark instances. Performance is compared with several state-of-the-art metaheuristic methods; the results show that the proposed algorithm outperforms these methods in most cases. Furthermore, the novel mechanisms are analyzed and discussed from points of view of performance, optimization time, and convergence. The findings achieved in this article bring new contributions to the very popular ACO-based algorithms; they can be applied to solve not only the MDVRP, but also, if adapted, to related complex NP-hard problems.

A framework for dynamical distributed flocking control in dense environments

The bio-inspired flocking model has been widely utilized in self-organized swarms. However, existing potential field methods fail to guarantee safe and orderly swarm movement in dense environments featuring both static and dynamic obstacles. In this study, we propose an innovative, dynamically optimized flocking control framework that integrates an improved and tunable self-propelled flocking model with an enhanced dynamic particle swarm optimization (DPSO) algorithm. Our model incorporates pigeon-inspired obstacle gap selection based on a probability approach to optimize steering decisions. Moreover, we introduce the DPSO algorithm, which combines a collaboration-based particle swarm optimizer with fractional order velocity incorporated history-guided estimation. This hybrid algorithm dynamically determines the optimal set of control parameters to maintain the desired swarm state. Through numerical comparison experiments with state-of-the-art models, we demonstrate that our approach enhances the safety, synchronization, and efficiency of the swarm even within a limited visual field, without prior environmental information. These features bring the simulations closer to real-world experiments.

Validation of a modified visual analogue scale to measure user-perceived comfort of a lower-limb exoskeleton

User perceived exoskeleton comfort is likely important for device acceptance, but there is currently no validated instrument to measure it. The Visual Analogue Scale (VAS) is an existing tool to measure subjective human feedback by asking the user to mark a point on a line with each end of the line representing an opposing anchor statement. It can be modified to show the previous response, allowing the subject to directly indicate if the current condition is better or worse than the previous one. The goal of this study was to determine how well the modified VAS could measure user-perceived comfort as the exoskeleton control parameters were varied. To validate the survey, 14 healthy subjects walked in a pair of ankle exoskeletons with approximately ten distinct sets of control parameters tested in a prescribed order. Each set of control parameters was tested twice. After each trial, user-perceived comfort was measured using a two-question VAS survey. The repeatability coefficient was approximately 40 mm, similar to the total range of responses. The results were also inconsistent, with relative rankings between consecutive pairs of conditions matching for approximately 50% of comparisons. Thus, as tested, the VAS was not repeatable or consistent. It is possible that subject adaptation within the trial and over the course of the experiment may have impacted the results. Additional work is needed to develop a repeatable method to measure comfort and to determine how perceived comfort varies as subjects’ gain exoskeleton experience.

Active flutter suppression of nonlinear fin-actuator system via inverse system and immersion and invariance method

Structural nonlinearities such as freeplay will affect the stability and even flight safety of the fin-actuator system. There is a lack of a practical method for designing Active Flutter Suppression (AFS) control laws for nonlinear fin-actuator systems. A design method for the AFS controller of the nonlinear all-movable fin-electromechanical actuator system is established by combining the inverse system and the Immersion and Invariance (I&I) theory. First, the composite control law combining the inverse system principle and internal model control is used to offset the nonlinearity and dynamics of the actuator, so that its driving torque can follow the ideal signal. Then, the ideal torque of the actuator is designed employing the I&I theory. The unfavorable oscillation of the fin is suppressed by making the output torque of the actuator track the ideal signal. The simulation results reveal that the proposed AFS method can increase the flutter speed of the nonlinear fin-actuator system with freeplay, and a set of controller parameters is also applicable for wider freeplay within a certain range. The power required for the actuator does not exceed the power that can be provided by the commonly used aviation actuator. This method can also resist a certain level of noise and external disturbance.

Automated Control Systems in Greenhouses

Objective. The purpose of the research is to develop an innovative automated program associated with a completely new irrigation regime based on a closed water supply system.Method. Methods of modeling, formalized description, optimal design and control of technological processes and production were used.Result. An automated control system in greenhouse farming based on Arduino controllers and ONI PLR Studio software for its operation are presented. The scientific novelty of the work lies in the creation of appropriate modes of the climate control system in greenhouses. The ONI PLR Studio program is written in FBD language. It solves important problems of controlling the cultivation of agricultural products, breeding new plant varieties, and controlling the technological process using certain signals. User input is limited to software adjustments and direct setting of controller parameters. The adjustment can be carried out remotely through a computer connected to the controller. The automatic operating mode is provided by sensors and a controller with electronic control circuits, which ensure that the actuators operate in the required mode.Conclusion. An automated control system (ACS) allows you to control air and soil temperature, soil and air humidity, soil nutrition, disinfection, air pressure, lighting, the presence of carbon dioxide in the greenhouse, and climate control.

Improvement of predictive control algorithm based on fuzzy fractional order PID

Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.

Model-free control applied to a direct injection system: Experimental validation

This work presents the design and application of the Model-free controller with algebraic estimator and ultra-local model on the common-rail high-pressure in a Direct Injection Fuel System of an internal combustion engine. In this application, the PID controller is traditionally used, but it needs different settings as modifications in the operating point occur. These changes encompass motor rotation, injection time, or torque demand. On the other hand, this paper considers only a single set of control parameters that provides good performance considering a broad range of operating points. A commercial electronic control unit (FlexECU) offering the possibility of new software development was used to implement the control algorithm and manage all engine functions. The tuning process of the controller is presented and discussed. Tests were carried out, and the results were graphically demonstrated, evidencing the easy applicability, robustness, and ability of the considered Mode-free scheme to control nonlinear systems, even facing important dynamic variations.

Braking Intention Identification Strategy of Electric Loader Based on Fuzzy Control

As a widely-used construction machinery, the electric loader has the potential to recover braking energy due to its large mass and frequent starts and stops. Identifying braking intention accurately is the foundation of braking energy recovery. The typical braking condition of an electric loader is analyzed; the braking intention is divided into sliding brake, mild braking, moderate braking, and emergency braking. A large number of braking data were collected under different braking intentions, which are used as the basis for fuzzy control variable partitioning, fuzzy controller parameter setting and fuzzy control rule formulation. The control strategies of deceleration intention identification based on accelerator pedal, braking intention identification based on brake pedal and sliding brake intention identification are proposed in this paper, respectively. This paper takes the hydraulic brake pressure as a feedback parameter, even if there is hysteresis or failure based on the intention of pedal identification, the system can still provide sufficient braking strength to ensure the braking safety. The AMESim(R12)-Matlab/Simulink co-simulation model and prototype are built to verify the feasibility of the control strategy under different braking intention identification. The results show that the braking strength under stronger braking intention can satisfy the braking demand whether at a speed of 2 m/s or 12 m/s, which ensures the safety of emergency braking, and the electric loader can provide stable braking strength under different braking intention and different speed. This has good braking stability.