Steady-state Performance Research Articles

Expanded virtual vector-based model forecastable control strategy for three-phase two-level inverters

Abstract A three-phase, two-level inverter based on model forecastable control has good transient performance but poor steady-state performance. Considering the limitation of a limited number of discrete voltage vectors in a two-level inverter topology, this paper proposes a virtual vector model prediction method based on the analysis of a time-stepped forecasting model for a three-phase grid-connected inverter. First, three fixed virtual voltage vectors are constructed in each sector, and the action time of the required fundamental vector is found. Then, the cost function is constructed, and the optimum is selected among the virtual and fundamental voltage vectors in each sector. Finally, a hardware-in-the-loop experiment was carried out based on the PE-Expert4 experimental platform developed by Myway Co., LTD. The results show that the proposed virtual vector model forecastable control strategy can effectively improve the transient and steady-state performance of three-phase two-level inverters.

Distributed fixed-time control for high-order multi-agent systems with FTESO and feasibility constraints

In this study, a fixed-time distributed anti-disturbance control strategy is designed for a class of higher-order multi-agent systems in an undirected static topology. Inspired by the existing literature, the strategy introduces a unique way of defining neighborhood errors during the design process to deal with the connectivity maintenance and collision avoidance problems among the neighbor agents in the system. The control strategy not only solves the full state constraint and transient steady state performance constraint control problems simultaneously, but also considers the effects of internal uncertainties and external disturbances of the system, which enhances the difficulty of controller design. To address these challenges, a fixed-time extended state observer is introduced to obtain the estimation of uncertainty and disturbance . Subsequently, an integrated backstepping control scheme is designed to feed-forward compensate for uncertainties and disturbances, while constraining the transient steady-state performance of the system to remain within a predefined performance boundaries. This is achieved by combining the fixed-time prescribed performance and the barrier Lyapunov function. Finally, a simulation case is presented to verify the effectiveness of the designed control strategy.

Experimental study on Joule-Thomson refrigeration system with R1150/R290/R601a for ultra-low temperature medical freezer

Since the global outbreak of the COVID-19, the requirement for ultra-low temperature medical freezers has increased rapidly. Joule-Thomson refrigeration systems, known for their simple structure and low cost, are an excellent choice for ultra-low temperature medical freezers. To further improve the performance of Joule-Thomson system for large-capacity ultra-low temperature freezer, this study conducted a series of experiments on the performance of a ternary hydrocarbon mixture R1150/R290/R601a. The impacts of refrigerant charge, capillary tube size, compressor speed, and environmental temperature on the steady-state and dynamic performance were investigated in detail. The results showed that under a fixed system configuration and charge, the freezer can achieve an ultra-low temperature of −82.2 °C and a COP of 0.256 at an environmental temperature of 25 °C, while maintaining a low compressor pressure ratio not exceeding 12. Furthermore, the matching relationship between capillary size and charge was emphatically studied, and it was found that the optimal refrigerant charge increased as the inside diameter of the capillary tube decreased or its length increased. The inside diameter had a more significant effect on system performance than the length. The suitable charge was 225–275 g for capillary tubes with an inside diameter of 1.4 mm, and increased to 400–450 g for an inside diameter of 0.7 mm. In addition, the experiments with various compressor speeds indicated that, before the cabinet temperature dropped to −40 °C, the variation of compressor speed had little effect on the pull-down rate. However, the higher the compressor speed, the lower the cabinet temperature that can be achieved. When the speed was 3500 rpm, the system achieved its lowest daily energy consumption, which was 8.42 kW·h/24 h.

Fractional-Order Robust Control Design under parametric uncertain approach

This paper presents a novel methodology that combines fractional-order control theory with robust control under a parametric uncertainty approach to enhance the performance of linear time-invariant uncertain systems with integer or fractional order, referred to as Fractional-Order Robust Control (FORC). In contrast to traditional approaches, the proposed methodology introduces a novel formulation of inequalities-based design, thus expanding the potential for discovering improved solutions through linear programming optimization. As a result, fractional order controllers are designed to guarantee desired transient and steady-state performance in a closed-loop system. To enable the digital implementation of the designed controller, an impulse response invariant discretization of fractional-order differentiators (IRID-FOD) is employed to approximate the fractional-order controllers to an integer-order transfer function. Additionally, Hankel’s reduction order method is applied, thus making it suitable for hardware deployment. Experimental tests carried out in a thermal system and the assessment results, based on time-domain responses and robustness analysis supported by performance indices and set value analysis in a thermal system test-bed, demonstrate the improved and robust performance of the proposed FORC methodology compared to classical robust control under parametric uncertainty.

Turbofan Performance Estimation Using Neural Network Component Maps and Genetic Algorithm-Least Squares Solvers

Computational models of turbofans that are oriented to assist the design and testing of innovative components are of fundamental importance in order to reduce their environmental impact. In this paper, we present an effective method for developing numerical turbofan models that allows reliable steady-state turbofan performance calculations. The main difference between the proposed method and those used in various commercial algorithms, such as GasTurb, GSP 12 and NPSS, is the use of neural networks as a multidimensional interpolation method for rotational component maps instead of classical β parameter. An additional aspect of fundamental importance lies in the simplicity of implementing this method in Matlab and the high degree of customization of the turbofan components without performing any manipulation of variables for the purpose of reducing the dimensionality of the problem, which would normally lead to a high condition number of the Jacobian matrix associated with the nonlinear turbofan system (and, thus, to significant error). In the proposed methodology, the component behavior can be modeled by analytical relationships and through the use of neural networks trained from component bench test data or data obtained from CFD simulations. Generalization of rotational component maps by feedforward neural networks leads to an average interpolation error up to around 1%, for all variables. The resulting nonlinear system is solved by a combined genetic algorithm and least squares algorithm approach, instead of the standard Newton’s method. The turbofan numerical model turns out to be convergent, and results suggest that the trend in overall turbofan performance, as flight conditions change, is in agreement with the outputs of the GSP 12 software.

High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

Adaptive event-triggered finite-time prescribed performance control of PMSM stochastic system considering time-varying delays

This paper investigates the finite-time prescribed performance tracking control problem of permanent magnet synchronous motor (PMSM) considering stochastic disturbances and time-varying delays under event-triggered mechanism. A quintuple polynomial finite-time prescribed performance function (FPPF) is introduced to ensure the transient and steady-state performance of the system output, and a nonlinear transformation function is employed to convert the constrained error into an unconstrained one. The Lyapunov-Krasovskii function is constructed to address time delays. And the system uncertainties are approximated by the radial basis function neural networks (RBFNN). For the “explosion of complexity” caused by backstepping method, a tracking differentiator (TD) is employed. By combining finite time control, command filtering backstepping control, and event-triggered mechanism, the effect of the filter errors is decreased, and the update frequency of the control signals are reduced. It is shown that the proposed controller can guarantee finite time convergence bounded of all signals in the closed-loop system, and the tracking error can converge in finite time. Finally, simulation results are presented to illustrate the effectiveness of the proposed controller.

Switching Current Predictive Control of a Permanent Magnet Synchronous Motor Based on the Exponential Moving Average Algorithm

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Enhancing PEM fuel cell dynamic performance: Co-optimization of cathode catalyst layer composition and operating conditions using a novel surrogate model

Optimizing the cathode catalyst layer (CCL) composition and operating conditions to enhance the dynamic performance of proton exchange membrane fuel cells garners significant attention. Although machine learning surrogate models are efficient for fuel cell analysis and optimization, the varied voltage dynamic response patterns (e.g., loading failure, voltage undershoot, and voltage hysteresis) challenge regression surrogate models designed for steady-state performance predictions. In response, this study introduces a joint framework combining classification and regression models for dynamic performance prediction. For training, a transient, two-phase, non-isothermal fuel cell model with integrated catalyst agglomerate is developed. The dynamic voltage deviation (σV) is proposed as an index to characterize the dynamic performance of the fuel cell. This joint surrogate model achieves correlation coefficients of 0.9976 and 0.9961 for predicting σV in training and test sets, respectively. Through this model, sensitivity analyses of the CCL composition and operating conditions are conducted to quantify their impact and interactions on the fuel cell's dynamic performance. Besides, the analysis reveals a trade-off between dynamic performance and steady-state output. To balance these, a multi-objective optimization is conducted. The results indicate that, compared to the base case, dynamic and steady-state performance improved by 44 % and 8 %, respectively.

Active noise control without tap length selection: a model order weighting method

Conventional active noise control systems typically rely on fixed tap lengths in control filters. Determining an appropriate tap length is challenging, even within a straightforward time-invariant environment. This challenge is exacerbated when essential system responses, such as primary noise characteristics or primary path responses, remain partially unknown and necessitate the use of an adaptive filter. A trade-off emerges between convergence rate and steady-state performance when selecting a fixed tap length in adaptive filters. In time-varying environments, the optimal tap length may even dynamically shift over time. Thus, prior attempts have been made to introduce variable tap length algorithms to dynamically adapt tap length in real time. However, the performance of variable tap length algorithms can be sensitive to the choice of additional parameters and noise level. This paper exploits a model order weighting approach by combining the outputs of filters of different tap lengths based on their predicted noise control performance. This proposed method demands minimal additional prior information compared to the variable tap length methods. The noise control performance, as demonstrated by specific examples of active noise control, is presented and analyzed.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

Adaptive neural network fault-tolerant control of hypersonic vehicle with immeasurable state and multiple actuator faults

This paper proposes a fault-tolerant control scheme for tracking and controlling hypersonic vehicles with unknown dynamics, actuator failures, and unmeasurable states. The approach involves using a radial-based neural network to approximate the unknown dynamics and reconstruct the entire system model. Additionally, a neural network state observer is proposed to estimate the unmeasurable state of the system. To address the impact of actuator faults, a nonlinear observer is designed to estimate and compensate for the approximation error of the neural network system and fault values. Furthermore, a prescribed performance function is introduced to ensure both transient and steady-state performance of the system. The bounded stability of the closed-loop system is demonstrated through Lyapunov stability analysis.

Adaptive dynamic‐surface repetitive control for uncertain nonlinear systems with mismatched disturbances and input delay

AbstractBased on additive state decomposition technique, this article presents an adaptive dynamic‐surface repetitive control method for a class of uncertain nonlinear systems with mismatched disturbances and input delay. First, the original uncertain nonlinear system is decomposed into a linear time invariant (LTI) primary system responsible for the periodic signal tracing and rejection task, and a nonlinear secondary system with input time delay responsible for the robust stabilization task. A modified repetitive controller with a small correction to the delay constant is then independently designed for the LTI primary system, while an adaptive dynamic surface controller is designed for the nonlinear secondary system. Stability analysis is performed for each subsystem and the design procedure of the whole system is presented in detail. Finally, comparative simulations with other repetitive control and dynamic surface control methods show that the presented method not only relaxes the constraints on the reference inputs and exogenous disturbances, but also ensures that the system has better disturbance rejection and periodic‐signal tracking performance in both transient and steady states. As a result, this method broadens the application of both repetitive control and dynamic surface control.

Manufacturing trials of PFCs with low thermal conductivity features for limiters

In future demonstration fusion power plants, plasma facing components will face high steady state and ultra-high transient heat loads from the plasma. Introducing low thermal conductivity features to the component can reduce the peak temperatures seen by the coolant pipe during transient loads, but at the trade-off of a loss of steady-state performance. Producing low-conductivity tungsten by additive methods has been investigated elsewhere, so this trial investigates production through subtractive means i.e., conventional milling. This method preserves the original temperature limits and majority of the microstructure and is much higher Technology Readiness Level (TRL) so requires less development to reach series manufacture. In this trial, diamond drilling produced holes in monoblocks down to 0.6 mm and ligaments down to 0.7 mm, showing that this method is capable of producing small features in a controlled way. With the geometric design of lattice used in this trial, these samples achieved thermal conductivity reductions of 15% to 40%. One monoblock assembly was tested up to 1100 °C for 500 cycles in the Heating by Induction to Verify Extremes (HIVE) High Heat Flux test rig and showed no signs of ligament cracking or thermal degradation. Electromagnetic modelling has been validated against the experimental results, so future designs can be accurately modelled ahead of testing in HIVE.

Least total logistic distance metric algorithm and its variable step-size version

Classical mean-square error (MSE)-based adaptive filtering algorithms are useful for noise-free inputs. However, if the input signal of the adaptive filter with such an adaptive filtering algorithm is corrupted by noise, it will suffer from serious degradation of steady-state performance. To address the problem, a new adaptive filtering algorithm is proposed in this work. This algorithm not only uses the total method to compensate the bias caused by noisy input but also minimizes the cost function based on logistic distance metric (LDM) to achieve robustness against impulsive noise. Compared with the existing algorithms, the proposed least total logistic distance metric (LTLDM) algorithm has less misalignment when the input signal is disturbed by noise and has good robustness to impulsive noise. This work also tackles the crucial trade-off between convergence rate and misalignment by developing a variable step-size for LTLDM. In addition, to give deep insight into the stochastic behavior of the proposed LTLDM, its mean-square deviation is analyzed at steady state. The advantage of LTLDM and the accuracy of theoretical expressions are verified by extensive simulations.

Output feedback adaptive neural network control for uncertain nonsmooth systems with application

The output feedback adaptive neural network control problem is addressed for uncertain nonsmooth systems with network communication constraints. First, a funnel function is provided to ensure that the tracking error restrains the pre-specified performance and improves the transient and steady-state performance simultaneously. Second, an event-triggered controller including the control input, a fixed threshold and the tracking error is designed to alleviate the communication burden. It is rigorously mathematically proved that there exists a positive lower bound to avoid the Zeno behavior. With the designed observer, the control algorithm assures that all the signals are semi-globally uniformly ultimately bounded. Finally, the validity of the proposed scheme is demonstrated through an application example.

Integral sliding mode control for an anthropomorphic finger based on nonlinear extended state observer

Due to the disturbance of couplings, the anthropomorphic finger lacks sufficient stability and accuracy in joint motion control, which further affects the performance of complex grasping and operating for anthropomorphic hands. In order to obtain stable and accurate joint motion control effect, an anthropomorphic finger control strategy is proposed for an anthropomorphic finger driven by pneumatic artificial muscles (PAMs) in this paper. A nonlinear extended state observer (NESO) is presented to observe the disturbance of couplings for the anthropomorphic finger. An integral sliding mode controller (ISMC) is proposed to realize joint motion control and improve steady state performance. The convergences of the NESO and the ISMC are demonstrated by Lyapunov methods. Furthermore, experimental results illustrate the validity of the proposed control strategy.

Rapid Start-up of Continuous Autotrophic Nitrogen Removal Reactor by Hybrid-inoculating PN and PN/A Granular Sludges

The rapid cultivation of partial nitritation/ANAMMOX （PN/A） granular sludge in a continuous-flow mode is one of the key technologies for efficient biological nitrogen removal in domestic wastewater treatment. Compared with that in PN/A granular sludge, PN granular sludge demonstrates a shorter incubation period and suitability for batch culture. It is also a good carrier for enriching ANAMMOX （AMX） bacteria. In this study, we established a continuous-flow autotrophic nitrogen removal process in three continuously stirred tank reactors （CSTR） （R1-R3） by hybrid-inoculating PN/A and PN granular sludge at the mass ratios of 3∶1, 1∶1, and 1∶3, respectively. By implementing high ammonium nitrogen loading and short hydraulic retention time, continuous autotrophic nitrogen removal processes were successfully started up in the three CSTRs. The results showed that compared with that of R1 and R2, R3 had a longer start-up time but a similar steady-state nitrogen removal performance. The total nitrogen removal load of R3 could be more than 2.6 kg·（m3·d）-1. Intriguingly, the inoculated PN granular sludge served as a precursor for PN/A granular sludge cultivation. This approach facilitated the enrichment of anaerobic ammonia-oxidizing bacteria （AMX） by introducing abundant ammonium-oxidizing bacteria （AOB） and nitrite nitrogen substrates into the CSTR. According to the results of high-throughput sequencing, the microbial abundance and diversity of the mature granules in R1-R3 were significantly higher than those of the inoculation sludge. AOB （genus Nitrosomonas）, AMX （genera Candidatus Kuenenia and Candidatus Brocadia）, and symbiotic heterotrophs, such as Chloroflexi, Bacteroidetes, and Chlorobi, drove the autotrophic nitrogen removal process and maintained the stability of the granular structure. In summary, a novel start-up strategy of hybrid-inoculating granular sludge was provided for a continuous-flow autotrophic nitrogen removal in engineering application.

A new EWMA chart for simultaneously monitoring the parameters of a shifted exponential distribution

In various scenarios where products and services are accompanied by warranties to ensure their reliability over a specified time, the two-parameter (shifted) exponential distribution serves as a fundamental model for time-to-event data. In modern production process, the products often come with warranties, and their quality can be manifested by the changes in the scale and origin parameters of a shifted exponential (SE) distribution. This paper introduces the Max-EWMA chart, employing maximum likelihood estimators and exponentially weighted moving average (EWMA) statistics, to jointly monitor SE distribution parameters. Additionally, we extend two additional charts, namely the Max-DEWMA and Max-TEWMA charts to enhance early-stage shift detection. Performance evaluations under zero-state and steady-state conditions compare these charts with the existing Max-CUSUM chart in terms of expected value and standard deviation of the run length (RL) distribution. Our findings reveal that among the Max-EWMA schemes, the Max-EWMA SE chart outperforms the others in terms of steady-state performance, while the Max-TEWMA chart surpasses the Max-EWMA and Max-DEWMA SE charts in respect to zero-state performance. Moreover, the proposed Max-EWMA schemes demonstrate advantages over Max-CUSUM, especially for small to moderate smoothing constants. We also provide an illustrative example to demonstrate the implementation of the proposed schemes.

Series solution of the Reynolds equation in finite-length tilting-pad journal bearing including pad deformation

This paper applies the variable separation method to the hydrodynamic pressure calculation of finite-length tilting pad journal bearings under Reynolds boundary conditions, and a series solution of the Reynolds equation is obtained based on the Sturm-Liouville theory, which considers pad deformation. The thermal effect is also taken into account. A comparison of the present solutions is made with the results obtained by other applied methods, as well as past and recent published data, and good agreement is noted. The steady-state and dynamic performance of a four-pad tilting pad journal bearing are investigated by the load capacity, pressure distribution, friction force, lubricant effective viscosity, journal center velocity, journal center orbits, and phase portrait under the proposed series solution of the Reynolds equation. This study contributes to quickly evaluating the nonlinear dynamic performance of a tilting pad journal bearing rotor system and supporting the nonlinear dynamic design of the system.