Control Performance Evaluation Research Articles

Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems.

This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor-critic architecture. First, a fuzzy identifier is designed to approximate each agent's unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.

Disturbance observer-based fuzzy adaptive optimal finite-time control for nonlinear systems

This paper investigates the issue of disturbance observer-based fuzzy adaptive optimal finite-time control in light of the backstepping approach for strict-feedback nonlinear systems with bias fault term and full state constraints. Considering that external disturbance and bias fault signal can affect the stability of control and control quality, a disturbance observer is constructed to track the external disturbances and bias fault online. A disturbance observer-based finite-time control strategy is proposed to achieve optimized control by utilizing the fuzzy logic system approximation-based adaptive dynamic programming method under the critic-actor framework. The purpose of the critic is to evaluate control performance and the role of the actor is to execute control behaviour. In addition, it is proved that the proposed fuzzy adaptive optimal finite-time scheme not only realizes all signals in closed-loop system are bounded, but also ensures that system states are restricted within specific sets. Finally, simulation results are shown to demonstrate the effectiveness of the proposed control strategy.

Geotechnical Project Contract Management System

The contract management system is based on information technology and uses the advanced management ideas of modern enterprises to provide enterprises with all-round and systematic decision-making, planning, control and business performance evaluation of contract management platforms. With the advancement of science and technology, a perfect contract management system has become an important symbol of the healthy operation of the company. However, contract management can not be fully operated in most companies, and improving contract management requires the support of company resources, so how to adjust the contract management completely and scientifically is a major problem. Adhering to the tenet of "serving small and medium-sized enterprises", "Contract Management Expert CMExpert" has been successfully researched and developed, effectively solving the contradiction between simple and powerful and powerful, which provides nearly 100 contract templates, perfect combination of WORD, EXCEL, ACCESS SOFTWARE, CAN IMPORT THE PREVIOUS WORD FORMAT CONTRACT INTO THE SYSTEM, PROTECT THE USER'S INVESTMENT, SO THAT HALF AN HOUR CAN BE USED SMOOTHLY. Combined with the actual situation of various companies in China, the contract management system integrates financial management and project management into contract management, truly showing the purpose of optimizing the company's resources; The automatic contract settlement reminder effectively protects the company's capital operation security; The statistics function enables dragging and dropping fields to achieve different statistics, which really simplifies the tedious statistical process and is very efficient. This system combines the existing contract management, makes some of our adjustments, and can be well applied to some enterprise companies.

Performance evaluation of software-defined networking controllers in wired and wireless networks

Traditional networking solutions are unable to meet modern computing needs due to the expanding popularity of the internet, which requires increased agility and flexibility. To meet these objectives, software-defined networking (SDN) arises. A controller is a major element that will determine if SDN succeeds or fails. Various current SDN controllers in many sectors must be evaluated and compared. The performance of two well-known SDN controllers, POX and Ryu, is evaluated in this research. We used the Mininet-WiFi emulator to implement our work and the distributed internet traffic generator (D-ITG) to assess the aforementioned controllers using delay, jitter, packet loss, and throughput metrics. What is new in our research is the study of network performance in two different types of transmission media: wired and wireless. The speed of the wired medium was chosen to be fast ethernet, which was not previously studied. In addition, the size of the packet was varied among 128, 256, 512, and 1,024 bytes. The comparison was performed on three topologies (single, linear, and tree). The experimental results showed that Ryu offers significantly lower latency, jitter, and packet loss than POX in most scenarios. Also, the Ryu controller has higher throughput than POX, especially on wireless networks.

On the Cyber-Physical Needs of DER-Based Voltage Control/Optimization Algorithms in Active Distribution Network

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, the mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise classification and identification of suitable metrics. It has been identified that classical controller definitions, along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for performance evaluation of DER controllers; while providing the taxonomy. Majority of the control algorithms operate in multiple time scales, and therefore, algorithmic time-decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements with the algorithmic architectural subtleties is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in terms of presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Detailed discussion provided identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test bed.

Variations in signal-to-noise characteristics of tissue-equivalent attenuators for mammographic automatic exposure control system performance evaluation.

This work investigates the impact of tissue-equivalent attenuator choice on measured signal-to-noise ratio (SNR) for automatic exposure control (AEC) performance evaluation in digital mammography. It also investigates how the SNR changes for each material when used to evaluate AEC performance across different mammography systems. AEC performance was evaluated for four mammography systems using seven attenuator sets at two thicknesses (4 and 8 cm). All systems were evaluated in 2D imaging mode, and one system was evaluated in digital breast tomosynthesis (DBT) mode. The methodology followed the 2018 ACR digital mammography quality control (DMQC) manual. Each system-attenuator-thickness combination was evaluated using For Processing images in ImageJ with standard ROI size and location. The closest annual physicist testing results were used to explore the impact of varying measured AEC performance on image quality. The measured SNR varied by 44%-54% within each system across all attenuators at 4 cm thickness in 2D mode. The variation appeared to be largely due to changes in measured noise, with variations of 46%-67% within each system across all attenuators at 4 cm thickness in 2D mode. Two systems had failing SNR levels for two of the materials using the minimum SNR criterion specified in the ACR DMQC manual. Similar trends were seen in DBT mode and at 8 cm thickness. Within each material, there was 115%-131% variation at 4 cm and 82%-114% variation at 8 cm in the measured SNR across the four imaging systems. Variation in SNR did not correlate with system operating level based on visual image quality and average glandular dose (AGD). Choice of tissue-equivalent attenuator for AEC performance evaluation affects measured SNR values. Depending on the material, the difference may be enough to result in failure following the longitudinal and absolute thresholds specified in the ACR DMQC manual.

Adaptive optimal trajectory tracking control of AUVs based on reinforcement learning

In this paper, an adaptive model-free optimal reinforcement learning (RL) neural network (NN) control scheme based on filter error is proposed for the trajectory tracking control problem of an autonomous underwater vehicle (AUV) with input saturation. Generally, the optimal control is realized by solving the Hamilton–Jacobi–Bellman (HJB) equation. However, due to its inherent nonlinearity and complexity, the HJB equation of AUV dynamics is challenging to solve. To deal with this problem, an RL strategy based on an actor–critic framework is proposed to approximate the solution of the HJB equation, where actor and critic NNs are used to perform control behavior and evaluate control performance, respectively. In addition, for the AUV system with the second-order strict-feedback dynamic model, the optimal controller design method based on filtering errors is proposed for the first time to simplify the controller design and accelerate the response speed of the system. Then, to solve the model-dependent problem, an extended state observer (ESO) is designed to estimate the unknown nonlinear dynamics, and an adaptive law is designed to estimate the unknown model parameters. To deal with the input saturation, an auxiliary variable system is utilized in the control law. The strict Lyapunov analysis guarantees that all signals of the system are semi-global uniformly ultimately bounded (SGUUB). Finally, the superiority of the proposed method is verified by comparative experiments.

Quaternion Attitude Control System of Highly Maneuverable Aircraft

In the era of rapid advancements in manned and unmanned aviation and robotics, there is a need for high-performance, robust attitude control of highly maneuverable fixed-wing aircraft, both manned and unmanned (UAVs). This paper presents an extension to research on spacecraft attitude control. The article extends existing concepts and applies them to the control problem of aircraft operating in Earth’s atmosphere. First, a general concept of quaternions is presented. Next, the attitude controller’s architecture is discussed. The controller synthesis is described using quaternion algebra. The quaternion-based attitude controller is then compared with a classical Euler-based attitude controller. The methodology for comparison and performance evaluation of both controllers is described. Lastly, the results of the simulations and a comparison of the two controllers are presented and discussed. The presented control scheme outperforms classical methods based on Euler angles, particularly at the aircraft’s high pitch and roll angles.

The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake

Abstract. In this paper, a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at a low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics. This is achieved by creating so-called observation points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computationally lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to Simulator for Offshore Wind Farm Applications (SOWFA) simulations in three- and nine-turbine cases under static and changing environmental conditions. The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three-turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model-based dynamic wind farm control.

Control design and performance evaluation of a grid connected PV–wind power system: A case study of Dongola, Sudan

Globally, considerable emphasis is being placed on developing clean and renewable energy systems. However, the utilization of such resources in Africa has not been fully exploited, especially in remote areas. Given the abundance of solar radiation and wind resources, Sudan has a lot of promise for clean energy solutions. This study describes a grid-connected PV–wind hybrid system’s comprehensive design, control strategy, and performance assessment in Dongola city located in Sudan’s northern region. The grid-connected hybrid system consists of a 3 MW wind turbine and a 1 MW solar system which is directly connected to the DC-link without any intermediate stages. The proposed configuration uses the grid-side converter to regulate the PV system terminal voltage, while Particle Swarm Optimization (PSO) algorithm determines the reference DC-link voltage. The PSO parameters have been thoroughly examined and selected for the maximum extraction of the PV system’s power. The hybrid system performance has been evaluated and validated using MATLAB/SIMULINK environment. Moreover, System Advisor Model (SAM) has been utilized to assess the proposed system’s expected energy considering the site’s historical solar irradiance and wind speed data. The simulation results revealed that the proposed system could produce 8,884 MWh annually, with an 89% share of wind generation and 10% produced by the PV system.

Optimized Backstepping Control Using Reinforcement Learning of Observer-Critic-Actor Architecture Based on Fuzzy System for a Class of Nonlinear Strict-Feedback Systems

In this article, a fuzzy logic system (FLS)-based adaptive optimized backstepping control is developed by employing reinforcement learning (RL) strategy for a class of nonlinear strict feedback systems with unmeasured states. For making the virtual and actual controls are optimized solution of the corresponding subsystem, RL of observer-critic-actor architecture based on FLS approximations is constructed in every backstepping step, where the observer aims to estimate the unmeasurable states, and the critic and actor aim to evaluate control performance and perform control behavior, respectively. In the proposed optimized control, on the one hand, the state observer method can avoid to require the design constants making its characteristic polynomial Hurwitz, which is universally demanded in the existing observer methods; on the other hand, the RL is significantly simple in algorithm, because the critic and actor training laws are derived from the negative gradient of a simple positive function, which is produced from the partial derivative of Hamilton–Jacobi–Bellman (HJB) equation, instead of the square of approximated HJB equation. Therefore this optimized scheme can be more easily applied and widely extended. Finally, from two aspects of theory and simulation, it is demonstrated that the desired objective can be fulfilled.

Optimized Uncertainty and Disturbance Estimator Based Robust Controller for Micro Aerial Vehicle

Abstract The work presents optimized uncertainty and disturbance estimator (UDE) based robust controller to achieve the fixed wing micro-aerial vehicle (MAV) longitudinal and lateral stability. In the proposed control methodology, genetic algorithm (GA) is used to find the optimal value of UDE filter parameter (GAUDE) which is filter time constant. GA uses minimization of integral absolute time error (IATE) based fitness function. In this work, the proposed controller is GAUDE-based adaptive sliding mode control (SMC). The Lyapunov theory is used to establish the stability of the presented controller. The performance of proposed SMC-GAUDE controller is analyzed through comparative analysis using numerical simulations. The comparative analysis consists of the proposed controller performance evaluation with existing UDE-based and conventional controllers. The comparative study shows the faster response to attain desired states along with smooth and chattering free control efforts offered by SMC-GAUDE controller. The results present viability of the proposed controller. To show the robustness of the proposed controller, IATE performance index is evaluated. Also, the Monte Carlo simulations are done to highlight the efficacy of the proposed controller in the presence of parametric variations in MAV aerodynamic coefficients and velocity.

Advances in research and applications of CO2-based demand-controlled ventilation in commercial buildings: A critical review of control strategies and performance evaluation

The carbon dioxide (CO 2 )-based demand-controlled ventilation (DCV) has attracted prompt attention from the Heating, Ventilation, and Air-Conditioning (HVAC) industry since its very first invention. Thereafter it has gone through several revolutions due to the rapid advancement in control and sensing technologies. Although a great variety of CO 2 -based DCV control strategies have been developed in the last two decades, there lacks a holistic literature review that systematically analyzes and summarizes advances and applications of CO 2 -based DCV in commercial buildings. This paper examines the recent advances in the CO 2 -based DCV in commercial buildings and focuses on discussing the control-related issues in the applications of the CO 2 -based DCV by collecting and assessing the available case studies in the recent two decades in terms of principles, complexity, and performance. First, principles of the different CO 2 -based DCV control strategies are elaborated, and their application scenarios are summarized from the case studies. Second, advancements in sensing technologies and actuating control devices are presented. On top of that, performance evaluation of the CO 2 -based DCV is conducted to (1) quantify the benefit achieved from the state-of-the-art CO 2 -based DCV; and (2) identify common issues and challenges associated with the design and field implementation of the CO 2 -based DCV. Finally, conclusions, limitations, and perspectives for future research are summarized. • Focused on discussing control-related issues in CO 2 -based DCV. • Classified and reviewed different CO 2 -based DCV control strategies. • Reviewed the advancements and challenges of CO 2 sensing in DCV. • Conducted performance evaluation of CO 2 -based DCV control strategies. • Summarized limitations and perspectives for future research.

Ventilation indices for evaluation of airborne infection risk control performance of air distribution

Air distribution is an effective engineering measure to fight against respiratory infectious diseases like COVID-19. Ventilation indices are widely used to indicate the airborne infection risk of respiratory infectious diseases due to the practical convenience. This study investigates the relationships between the ventilation indices and airborne infection risk to suggest the proper ventilation indices for the evaluation of airborne infection risk control performance of air distribution. Besides the commonly used ventilation indices of the age of air (AoA), air change effectiveness (ACE), and contaminant removal effectiveness (CRE), this study introduces two ventilation indices, i.e., the air utilization effectiveness (AUE) and contaminant dispersion index (CDI). CFD simulations of a hospital ward and a classroom served by different air distributions, including mixing ventilation, displacement ventilation, stratum ventilation and downward ventilation, are validated to calculate the ventilation indices and airborne infection risk. A three-step correlation analysis based on Spearman's rank correlation coefficient, Pearson correlation coefficient, and goodness of fit and a min-max normalization-based error analysis are developed to qualitatively and quantitatively test the validity of ventilation indices respectively. The results recommend the integrated index of AUE and CDI to indicate the overall airborne infection risk, and CDI to indicate the local airborne infection risk respectively regardless of the effects of air distribution, supply airflow rate, infectivity intensity, room configuration and occupant distribution. This study contributes to airborne transmission control of infectious respiratory diseases with air distribution.

Unified and Separated Buck/Boost Averaged Current Control Strategies of Bidirectional DC-DC Converter for DC Microgrid Systems

The main objective of this paper is to present analysis and performance evaluation of averaged current controller for bidirectional DC-DC converters. Two control strategies will be discussed and evaluated: the unified current controller and the separated buck/boost mode current controller or well known as logic-based current control strategy. The feedback current controller designed in this work is a conventional proportional-integral with a feedforward control strategy. The investigation of the control system performance is carried out based on the voltage conversion ratio between the high and low-voltage side of the converter (VH/VL). Based on the simulation studies, the control performance is relatively the same for the small voltage conversion ratios, whereas for the large voltage conversion ratios, the logic-based current control strategy appears to be superior compared to the unified current controller.

Performance Evaluation Fuzzy Logic Control on Low-Cost Medical Equipment Sterilizer

The equipment is not sterile when the health equipment is used repeatedly. Thus, it is at risk of infection. Data published by WHO reveals that one out of every ten hospital patients gets an infection. In developed countries, the number of infected hospital patients reaches 30%, while in developing countries it is at least 2-3 higher. One way to reduce the occurrence of infection is to sterilize medical equipment through a dry heat sterilization process at 1700 C for 60 minutes. The high price of sterilizers means that many health centers and clinics do not have this equipment. The aim of this study was to design a low-cost dry heat type health equipment sterilizer using fuzzy logic as a controller and evaluate the performance of the controller. Regarding to the research result, it indicated that the controller performance evaluation was very good with the highest overshoot value of 1.1%, the steady state error value was 1.1%, and the fastest rise time was 240 seconds.

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

Evaluation of nonlinear control performance of air handling units under variable operation conditions using root distribution approach

The control systems at Air handling units (AHUs) have nonlinear nature, resulting potentially in sluggish, aggressive, or oscillatory responses under variable operation conditions. This paper is to develop an approach for evaluating the nonlinear control performance of AHUs under variable operation conditions and for identifying the impact of varying multiple operation parameters which guides controller design. To do so, an operation parameter-based root distribution technique, intended to sketch the paths traced by closed-loop poles as multiple operation parameters are varied, is first introduced. The technique—which may be viewed as an extension of conventional, single-variable root locus—is then applied to a fan speed control system that includes a motor with a variable frequency drive and a supply fan with an air duct under variable fan speed and duct static pressure. Using the technique, the system’s stability region is graphically identified. The system is also found to be aggressive and gradually lose its stability as the fan speed decreases and the duct static pressure increases, which is validated by the field tests. The root distribution approach is an effective tool to evaluate the performance of any nonlinear control systems at AHUs with multiple variable operation parameters without requiring time-domain simulations.

Execution energy of continuous flight auger piles as an assessment tool to evaluate the mechanical response of the soil mass

Allying technology, theory and engineering practice is one of the main challenges of modern foundation engineering. Current foundation and retaining walls designs may lead to oversizing or undersizing due to the spatial variability of the stratigraphic profile. As the design procedure of piled foundations involves defining the setting depth of the tip, sometimes the piles may not behave as expected, as the soil mass may change significantly even in a small region. In this paper, a study was conducted on the geoenergetic behavior of the soil mass and of the piles of a construction site located in Brasília, DF, Brazil. Previous studies showed that the energy needed to drill a continuous flight auger pile (CFAP) can be related to the strength of the drilled strata. Therefore, a methodological framework was built to further discuss how the geomechanical behavior of CFAPs foundations could be assessed by analyzing the energy needed to drill such piles. Statistical methods, in special the Bootstrap method, were used to assess the possible influences of construction procedures on pile behavior. It was studied how the execution energies of piles vary with initial depth and relative position in the terrain, as well as the influences that they suffer because of the order of execution within a same foundation group and due to the proximity to surrounding retaining walls. The proposed methodology can be used to improve the energy control procedure and performance evaluation of CFAPs, allowing a complementary reliability assessment for the foundation and retaining walls designs and implementations.

Optimized tracking control based on reinforcement learning for a class of high-order unknown nonlinear dynamic systems

The article is to develop an optimized tracking control using fuzzy logic system (FLS)-based reinforcement learning (RL) for a class of unknown nonlinear dynamic single-input–single-output (SISO) system under canonical form. From mathematical viewpoint, optimal nonlinear control depends on the solution of Hamilton–Jacobi-Bellman (HJB) equation, but finding the equation’s analytic solution is almost impossible because of the strong nonlinearity. For addressing the problem in this work, the RL approximation strategy is employed by on-line iterating both critic and actor FLSs, where critic FLS aims for evaluating control performance and making feedback to actor, and actor FLS aims for executing the modified control behavior. Different with the previous RL-based optimization approach, the proposed approach derives RL training laws from negative gradient of a simple positive function rather than the square of HJB equation’s approximation. As a result, the control algorithm can be significantly simplified. Furthermore, it can avoid two general conditions, persistence excitation and known dynamic, required in most RL optimal control methods. Finally, the proposed optimized control is demonstrated from both aspects of theory proof and computation simulation.