Control Performance Evaluation Research Articles

Modelling and performance evaluation of PV controller in various reference frame

Modelling and performance evaluation of PV controller in various reference frame

A new evaluation method for sand control performance of slotted screen

Evaluation method of slotted screen performance is a critical concern for sand control. Despite its significance, comprehensive investigations into quantitative study of sand retaining during the complex flow process need further in-depth study, particularly in numerical simulation research model of the slotted screen. This paper adopts the two-way coupling calculation method of computational fluid dynamics (CFD) and discrete element method (DEM) for the particle movement and fluid flow at the slotted screen and establishes a numerical model of the sand particle migration process through the slotted screen. The sand particle deposition and plugging dynamics on the surface of the sand retaining medium and the micro sand plugging mechanism and blockage pattern are studied. The fractal dimension of formation sand is introduced to characterize different distribution of formation sand. The effects of fluid viscosity, width of the slotted screen, sand production concentration, fluid velocity and particle size distribution on sand production, sand passing rate and particle size distribution of formation sand passing through the slot and plugging behavior are studied. The sand plugging process of slotted screen can be divided into three stages: the beginning of blockage, the intensification of blockage, and the balance of blockage. Under simulated conditions, the initial sand plugging rate is 87%, and as the blockage intensifies, the sand plugging rate gradually increases to 96.5%. Based on the analysis of numerical simulation results, the index of total seepage resistance damage amplitude and velocity, general sand retention ratio and passed-sand size were defined to construct the integrated evaluation method of slotted screen media, which involves the performance of sand retention, anti-plugging and flow ability. A brand new set of ideas on quantifying the integrated evaluation method were introduced to calculate the individual and integrated performance indicators of the slotted screen based on simulated data. The quantitative evaluation of the sand control performance of the slotted screen provides guidance and reference for the parameter design of the slotted screen in the target oil region and the accuracy optimization of the sand control medium.

Reinforcement learning-based adaptive event-triggered control of multi-agent systems with time-varying dead-zone

In this paper, a novel reinforcement learning (RL)-based adaptive event-triggered control problem is studied for non-affine multi-agent systems (MASs) with time-varying dead-zone. The purpose is to design an efficient event-triggered mechanism to achieve optimal control of MASs. Compared with the existing results, an improved smooth event-triggered mechanism is proposed, which not only overcomes the design difficulties caused by discontinuous trigger signals, but also reduces the waste of communication resources. In order to achieve optimal event-triggered control, RL algorithm of the identifier-critic-actor structure based on fuzzy logic systems (FLSs) is applied to estimate system dynamics, evaluate control performance, and execute control behavior, respectively. In addition, considering time-varying dead-zone in non-affine MASs brings obstacles to controller design, which makes system applications more generalized. Through Lyapunov theory, it is proved that the optimal control performance can be achieved and the tracking error converges to a small neighborhood of the origin. Finally, simulation proves the feasibility of the proposed method.

High-efficiency adaptive temperature control for thermoelectric system based on the OBPPID strategy

Thermoelectric cooler (TEC) is widely used for temperature control in optoelectronics, machinery, biomedicine, and other fields. However, the unstable temperature control over wide ranges and different gradients restricts the efficiency of TEC. To highlight the neural network’s effectiveness in controlling TEC systems, the optimized BPPID (OBPPID) strategy is proposed. In the OBPPID strategy, the factors s̃ and G are decided through formula derivation and the particle swarm optimization (PSO) algorithm, respectively contributing to the backward adjustment and forward propagation of BPPID. The OBPPID solves three issues that the BPPID faces: gradient vanishing of network weights, over-reliance of initial network weights and range constraint of control parameters. The OBPPID and BPPID have been simulated 1000 times to evaluate control performance. The results show that, with random initial network weights, the OBPPID strategy reduces the cost variability of the BPPID strategy from 92 to 17, achieving an 81.52% reduction. Physical tests have confirmed that in the temperature control over wide range and the temperature gradients of 5°C, 20°C, and 80°C, the OBPPID strategy can adaptively adjust the control parameters and provide higher efficiency than PID, BPPID and fuzzy PID strategies for TEC system.

Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

Dynamic positioning system of ships with RBF neural network compensator

Abstract In Dynamic Positioning, traditional control methods lack stability, while neural networks and reinforcement learning have become research hotspots. By learning decision-making strategies from environmental observation data, we can adapt to environmental changes in real time and achieve optimal control effects. We also design a reward function to evaluate control performance. Dynamic inversion control is a control strategy that transforms nonlinear systems into linear systems. To address the issue of ship dynamic positioning, we propose a novel controller design method by combining RBF neural networks with dynamic inversion control. Its effectiveness has been verified through simulation experiments, demonstrating its potential and value in practical applications. This method will help improve the positioning accuracy of ships and optimize control performance.

Performance evaluation of PI and FLC controller for shunt active power filters

Performance evaluation of PI and FLC controller for shunt active power filters

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

Exploring the effects of faults on the performance of a biological wastewater treatment process

ABSTRACT To prioritise which faults should be detected in a biological wastewater treatment process, and with what level of urgency, it is necessary to understand the effect that they have on the process. Using the Benchmark Simulation Model No. 1 and 2. (BSM1 and BSM2), several process and sensor faults were considered and their impacts on various cost, quality, and controller performance evaluation metrics analysed. Both the cost of treating the wastewater and the quality of the effluent were impacted in varying degrees of severity by the faults tested. The most influential faults in both models were decreases to autotrophic and heterotrophic growth rates, decreases to the heterotrophic death rate, and the inhabitation fault. It was shown that only larger fault sizes were significant, and the required speed of detection is dependent on the fault profile. Prioritising detection of the most influential faults was shown to have significant effects on monitoring requirements for fault detection and the subsequent complexity required of a fault detection system. A valuable takeaway was the similarity of results from BSM1 and BSM2; the consistency of the influential process faults suggests that systems that can be described by these models are likely affected by the same faults.

Design and Performance Evaluation of a Hybrid Active Power Filter Controller

This paper introduces a novel hybrid filter topology that combines passive and active components to enhance harmonic filtering and resonance damping in electrical power systems. The design integrates a three-phase two-level voltage-source converter with a double-tuned passive filter in parallel, significantly reducing the power rating and operational costs while maintaining good harmonic filtering performance and reactive current compensation. Double-tuned passive filters, compared to single-tuned ones, offer improved harmonic attenuation at multiple frequencies, enhancing overall system efficiency. Moreover, when used with the proposed hybrid filter topology, the double-tuned version allows for even lower dimensions of the active part, thereby further reducing system cost. A state-feedback controller is designed to enhance the performance of the hybrid filter, proving particularly effective in environments with complex impedance conditions. This paper also examines the impact of variations in passive component parameters, demonstrating the design’s robustness against potential deviations expected over the operational lifespan. The results indicate that the hybrid filter effectively mitigates harmonics and maintains operational stability under various transient conditions, as confirmed by analytical and simulation studies on a real industrial network model. These findings underline the hybrid filter’s potential to significantly improve power quality in modern electrical networks.

Performance evaluation of PID and Fuzzy Logic controllers for residential ORC-based cogeneration systems

Cogeneration systems have been identified as a way to reduce primary energy consumption, especially the ones using fossil fuels. Although this technology has already reached maturity on a medium-to-large scale, it is not very well explored on a micro-scale, especially at a residential level where the market is currently filled with carbon-fueled boilers and combi-boilers. For implementation at a residential level, the organic Rankine cycle has been mentioned as a suitable technology because of its simplicity and thermal/ electrical efficiencies. Despite its performance, one of the most important aspects of these applications is the response time, which is mainly connected to the system’s control. Due to its influence, this study aims to provide insights for developing flexible control strategies and analyze two different control methodologies, a standard PID and a Fuzzy Logic controller, considering the operating conditions of an experimental ORC-based micro-cogeneration test rig.Before being implemented in the test rig, both control methodologies were optimized and tested in a virtual environment in order to avoid the exposure of the test rig to unsafe operating conditions. While both controllers show a reasonable response in operating conditions similar to the ones used in the virtual environment, the Fuzzy Logic approach proved to be more adaptable to changes in the system’s dynamics, which are caused by intrinsic (e.g., unpredicted evaporative phenomena) or external disturbances (e.g., a pressure ratio variation), obtaining an overall better performance. Additionally, the limitations of static PID controllers for complex nonlinear processes, like the ORC, were highlighted by their incapacity to stabilize the system when the organic fluid is diverted from the bypass line to the expansion valve. Conversely, it was concluded that the ability to incorporate expert knowledge and heuristics makes the Fuzzy Logic controller well-suited for ORC systems with uncertain, unstable, and fluctuating behavior.

An evaluation of the internal quality control performance of the general biochemistry analytes in a laboratory of a tertiary care hospital of Sri Lanka

An evaluation of the internal quality control performance of the general biochemistry analytes in a laboratory of a tertiary care hospital of Sri Lanka

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

Vibration Mitigation via Tuned Mass Damper for Adjacent Stay Cables Interconnected with Rigid Cross-Tie

The characteristics of low damping and closely spaced natural frequencies make stay cables susceptible to wind-induced vibrations. This work proposed a novel damping device consisting of tuned mass damper (TMD) and rigid cross-tie (RCT) to mitigate the vibration of stay cables effectively. Two adjacent stay cables interconnected with RCTs can provide installation locations for TMDs, thus making the novel TMD-RCT control system feasible. Experimental control tests were carried out on a two-cable model of adjacent stay cables built in laboratory, where one TMD was designed based on the fundamental vibration mode of the stay cable. The effects of CT stiffness, number of RCTs, mass ratio, and installation position on the vibration reduction effect of the device were studied. To validate the efficacy of the proposed TMD-RCT device in vibration mitigation of stay cables under different loading cases, simulation tests and corresponding control performance evaluation were conducted.

Barrier Lyapunov function-based adaptive optimized control for full-state and input-constrained dynamic positioning of marine vessels with simulation and model-scale tests

For practical marine engineering, ensuring system performance and avoiding violations of safety-related system constraints in various scenarios remains a challenging problem. To tackle this problem, this paper addresses adaptive neural network (NN) optimized control for dynamic positioning (DP) of marine vessels suffering from unknown model uncertainties and control gains, ocean disturbances, and full-state/input constraints. To realize model-free optimized control, the reinforcement learning (RL) with a critic–actor–identifier architecture is constructed on the basis of NN approximation, in which the actor, critic, and identifier NNs are designed to perform control actions, evaluate control performance, and estimate unknown dynamics, respectively. To effectively guarantee the system safety, the critics and actors in the corresponding subsystems are decomposed into a guaranteed safety-related constraint term with respect to the barrier Lyapunov function (BLF) and an unknown dynamic term to be learned. Such a control strategy can ensure that all the state variables are restricted to a predefined safety region, even in the learning control process. From the above logical ideas, the adaptive optimized kinematic and kinetic control laws are presented to guarantee that the whole vessel system is optimized. Moreover, the motion control responses of vessels in various scenarios are realistically simulated through convincing simulations and model-scale tests. The validation results prove the applicability and feasibility of the proposed DP control strategy for practical offshore engineering.

A Comparative performance evaluation of a complex-order PI controller for DC–DC converters

Switched-mode DC–DC converters are required to maintain constant output under uncertainties and variations in input voltage and load. The controller’s robustness is crucial in such systems, and hence various controllers have been proposed in the past few decades for DC–DC converter control. Fractional order PID controllers have attracted the attention of researchers due to their robustness and flexibility in control of power converters. Such controllers use fractional orders of integration and differentiation. Complex order controllers are the generalized form of the fractional order PID controllers and have complex orders of integration and differentiation. These controllers are very robust in the control of nonlinear systems with time-varying parameters. But complex order controllers have been very sparingly used in power electronic control. This paper proposes a complex order PI controller with a complex order integrator for controlling DC–DC buck and boost converters. The complex PID controller has four parameters to be tuned. The complex order PI controller is designed by optimization using the metaheuristic Cohort Intelligence algorithm. The results are compared with that of a fractional-order PID controller. It was observed that the complex PI controller gave a better response than the FOPID controller and was more robust to parameter variations.

Intelligent learning approach for transition control and protection of solar PV integrated electric vehicle charging station

Recently, the control of distributed generations (DGs) with electric vehicles (EVs) parking lot charging systems has received much attention for supporting emergency local loads during grid outages. To achieve optimal transition control of grid-connected (GC) and standalone (SA) modes, an intelligent learning-based algorithm is implemented for optimal handling of the disturbance in the system by injecting a fault condition over certain cycles during a weak/strong grid operation. Here, a fuzzy Q-learning (FQL) based algorithm is implemented for detecting and monitoring the distributed grid condition during different scenarios and a seamless transitioning control in GC and SA modes of operation. In this context, the implementation of designed FQL algorithm enables dynamic adjustments of control signals in response to real-time uncertainties in two major applications (grid synchronization and islanding detection mode). The FQL algorithm focuses on real-time decision-making in response to sudden fault scenarios, addressing the problem under various modes of operation. To validate the proposed FQL controller accuracy, a real-time hardware-in-loop (HIL) simulation and their experimental validation is carried out of a 16kWp grid integrated PV sub-system with different load profiles. The performance evaluation of the FQL controller is investigated under linear and nonlinear local load scenarios.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Optimal design of short-period structures equipped with sliding tuned liquid column damper and numerical and experimental control performance evaluation

Optimal design of short-period structures equipped with sliding tuned liquid column damper and numerical and experimental control performance evaluation

Design, Construction and Performance Evaluation of Solar Charge Controller for Street Lighting Application

Solar powered equipment and applications are gradually making their ways into various sector of our day to day life. We need a storage or battery to store the solar energy harnessed during day. A Solar Charge Controller (SCC) which is a part of a solar power system has been designed using Proteus Software, to maintain batteries used in solar power harvesting and utilization in a charged state, without risk of over-charging or over-discharging, ensuring battery durability. The charge controller has battery management system in built, responsible for executing the desired charging algorithm based on the battery chemistry characteristics. After the circuit design and construction, tests like Power Supply test, Accuracy test, charging test and discharging test were conducted. From the results of the tests, the power supply met its requirement to out-put a constant voltage of 5Vfor the microcontroller and LCD circuit. The charging test result shows the charging characteristics of the charge controller. The discharging test result is a negative linear graph showing that the battery is discharging from 14.4 V to about 9.5 V where the microcontroller disconnected the battery from the load. The results showed that the charge controller was able to control and regulate the charging process well. Calculated Mean percentage error was 0.04.