Closed-loop Simulation Research Articles

Study on simulation technology of multi-color temperature and multi-magnitude starlight source

The star simulator was designed to simulate the physical characteristics of real stars. Due to the different color temperatures and magnitudes of different stars, in order to simulate stars more realistically, a starlight source simulation system was required to achieve large-scale high-precision adjustment of color temperature and magnitude. The system consisted of an integrating sphere, light source module, control module and fiber spectrometer to form a closed-loop light source simulation system. In order to ensure the uniformity of the emitted light, a bromotungsten lamp and a variety of monochromatic LED mixed light were used as the light source, and the output brightness of the LED was controlled by constant current driving. The quantum behavior particle swarm algorithm was introduced as the spectrum matching algorithm to solve the LED coefficient ai. As the number of iterations increases, and the value of the fitness function reached the minimum, the simulation error of color temperature and magnitude became smaller. Experimental verification: The range of color temperature was 3 000, 4 300, 5 500, 6 500, 7 600, 20 000 K, the color temperature matching error was better than ±10%; the range of simulated magnitude was +2-+8.5 Mi, and the magnitude matching error was better than ±6%.

On the Identification of Social Cognitive Theory Models and Closed-loop Intervention Simulations Using Hybrid Model Predictive Control

Abstract This paper presents closed-loop intervention simulations for an identified, data-validated model of Social Cognitive Theory (SCT). A reduced-complexity SCT model structure consisting of dynamic operant conditioning and self-efficacy loops is considered for the prediction of physical activity behavior. Consistent with real-world requirements, including the need for hybrid decision rules policies, the proposed closed-loop intervention design follows a Hybrid Model Predictive Control formulation. The prime goal of this paper is to reinforce the viability of the system identification and control engineering frameworks in the design of optimized and perpetually adaptive behavioral health interventions.

Closed-loop Simulation of Buck Converter Based on PSIM

This paper introduces the working principle of Buck converter under voltage control, and studies the modeling method of Buck converter under special power electronics simulation software. Finally, the simulation of the converter is carried out under the condition of the mutation of input voltage and load. The simulation results show that the voltage control strategy has good transient response and anti-jamming ability, which has a good guiding significance for practical application.

MATLAB GUI Based Steady State Open-Loop and Closed-Loop Simulation Tools for Different LLC Converters With all Operation Modes

LLC resonant converters have been widely adopted in many different industrial applications due to their characteristics of high efficiency and high power density. Simulation tools are of great importance for the analysis and design of LLC resonant converters. For example, many LLC design methods require massive simulations to finalize the circuit parameters, iterations are necessary, which can be tedious and time-consuming. Therefore, in this work, accurate and complete steady state simulation tools based on MATLAB graphical user interface (GUI) are designed for LLC resonant converters. All common and possible operation modes and LLC converter topologies are included in the designed simulation tools, which makes them practical for different applications and operation conditions. An improved mode judgement method and guidelines for initial point setting are proposed to ensure fast simulation speed. More than that, the key circuit voltage and current values are summarized for the user to evaluate the converter performance, which is more convenient than commercial simulation software. Moreover, the designed simulation tools are suitable and convenient for automatic converter parameter design.

LPV Delay-Dependent Sampled-Data Output-Feedback Control of Fueling in Spark Ignition Engines

We propose a delay-dependent sampled-data output-feedback LPV control technique to address the air-fuel ratio (AFR) regulation problem in spark ignition (SI) engines. AFR control and advanced fueling strategies are essential for maximizing fuel economy while minimizing harmful exhaust emissions. The fuel path of the SI engine, as well as the three-way catalyst (TWC) simplified dynamics, have been captured by a continuous-time linear parameter-varying (LPV) system with varying time delay, where the system dynamics rely on the engine speed, defined as the system's scheduling parameter. The interconnection of the continuous-time plant and a digital controller through analog-to-digital and digital-to-analog converter devices forms a hybrid closed-loop configuration. Therefore, in order to benefit from continuous-time control synthesis tools, the input-delay method has been employed to transform the hybrid closed-loop system into the continuous-time domain with system inherent time delay and an additional delay imposed by the mapping approach. The designed sampled-data gain scheduled output-feedback controller seeks to establish the closed-loop asymptotic stability and a prescribed level of performance for the LPV system with an arbitrarily varying time delay and varying sampling time, where the synthesis results are provided in a convex linear matrix inequality (LMI) constraint setting. Finally, several closed-loop simulation scenarios are conducted, and comparisons are provided to demonstrate the proposed methodology's performance in achieving precise reference AFR tracking and disturbance attenuation.

Robust linear parameter-varying output-feedback control of permanent magnet synchronous motors

This paper investigates the design of a robust linear parameter-varying (LPV) controller for speed regulation of surface permanent magnet synchronous motors (SPMSMs). Motor dynamics is defined in the α–β stationary frame and a parameter-varying model formulation is provided to describe its nonlinear dynamics. A robust gain-scheduled LPV dynamic output-feedback controller is designed to guarantee the asymptotic stability and performance requirements, as well as, guarantee robustness against parameter perturbations and torque load disturbances. As a novel aspect in the SPMSM control, the real-time impact of temperature variation on the winding resistance and magnet flux during motor operation is explicitly considered in the LPV modeling as a scheduling parameter and the subsequent LPV control design compensating for demagnetization effects in the motor response. The controller synthesis conditions guaranteeing stability and robust performance to norm-bounded uncertainty in the system matrices are formulated in a convex linear matrix inequality (LMI) optimization framework. Finally, the validity of the proposed method is assessed in simulation studies. The closed-loop simulation studies demonstrate that the proposed LPV controller provides improved transient response with respect to settling time, overshoot, and disturbance rejection in tracking the velocity profile under the influence of parameter perturbation, temperature variations, and load disturbances.

An Embedded Multi-Core Real-Time Simulation Platform of Basal Ganglia for Deep Brain Stimulation.

Closed-loop deep brain stimulation (DBS) paradigm is gaining tremendous favor due to its potential capability of further and more efficient improvements in neurological diseases. Preclinical validation of closed-loop controller is quite necessary in order to minimize injury risks of clinical trials to patients, which can greatly benefit from real-time computational models and thus potentially reduce research and development costs and time. Here we developed an embedded multi-core real-time simulation platform (EMC-RTP) for a biological-faithful computational network model of basal ganglia (BG). The single neuron model is implemented in a highly real-time manner using a reasonable simplification. A modular mapping architecture with hierarchical routing organization was constructed to mimic the pathological neural activities of BG observed in parkinsonian conditions. A closed-loop simulation testbed for DBS validation was then set up using a host computer as the DBS controller. The availability of EMC-RTP and the testbed system was validated by comparing the performance of open-loop and proportional-integral (PI) controllers. Our experimental results showed that the proposed EMC-RTP reproduces abnormal beta bursts of BG in parkinsonian conditions while meets requirements of both real-time and computational accuracy as well. Closed-loop DBS experiments using the EMC-RTP suggested that the platform could perform reasonable output under different kinds of DBS strategies, indicating the usability of the platform.

Water-energy nexus approach for optimal design of hybrid cooling system in direct reduction of iron plant

Direct reduction of iron process in steel industry has special production conditions. Low quality cooling water, low cold and high hot cooling water temperature, space limitation for new equipment installation, high value-added of product and severe effect of cooling water temperature on production rate are of these conditions. Considering technical and economic constraints and limitations, this situation makes this process an attractive case study for converting the existing wet cooling tower to hybrid cooling system. In this paper, based on integration of process, dry and wet cooling system and ambient conditions profiles, a new method for designing hybrid cooling system has been proposed. The calculation program has been developed as a MATLAB code and resulted: 1) Calculation of annual water saving and energy consumption with reasonable accuracy, 2) Control system of dry and wet cooling blocks to maximize water saving under various ambient conditions, 3) Closed loop simulation of cooling system considering thermal feedback of process to cooling system and 4) Obtaining optimum values for 16 design parameters of dry block cooling (consisting air cooled heat exchanger and plate heat exchanger). The optimization has been done via genetic algorithm, considering annual response of each design under various ambient temperature and relative humidity. Considering design standards for new equipment and operational and technical constraints, made the optimization model reliable as well as checking the design parameters with process equipment constructors. In optimum point, converting existing wet cooling tower to hybrid cooling system in direct reduction of iron plant of Mobarakeh Steel Complex located in Isfahan province in Iran, will decrease the annual water consumption by 70.15% and production loss by 98% with payback period of 1 years and 10 months.

Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks

Scenario-based model predictive control (MPC) methods introduce recourse into optimal control and can thus reduce the conservativeness inherent to open-loop robust MPC. However, the uncertainty scenarios are often generated offline using worst-case uncertainty bounds quantified a priori, limiting the potential gains in control performance. This paper presents a learning-based multistage MPC (msMPC) for systems with hard-to-model dynamics and time-varying plant-model mismatch. Gaussian Processes (GP) are used to learn state- and input-dependent plant-model mismatch in real-time and accordingly adapt the scenario tree online. Due to the increased computational complexity associated with incorporating the GP predictions into the optimal control problem, the learning-based msMPC (LB-msMPC) law is approximated by a deep neural network (DNN) that is cheap-to-evaluate online and has a small memory footprint, which makes it suitable for embedded applications. In addition, we present a novel algorithm for training the DNN-based controller that uses a GP description of the plant-model mismatch to generate closed-loop simulation data, which ensures the LB-msMPC law is evaluated in regions of the state space most relevant to closed-loop operation. The proposed LB-msMPC strategy is demonstrated on a cold atmospheric plasma jet with applications in (bio)materials processing. The simulation results indicate the promise of the approximate LB-msMPC strategy for control of hard-to-model systems with fast dynamics on millisecond timescales.

Thrust noise cause analysis and suppression of a cusped field thruster

The drag-free control technology is a critical technology to guarantee the ultra-low disturbance satellite platform. Thrust noise is the primary disturbance of the drag-free control system because the thruster acts as the actuator of this system, so it would seriously affect the performance of the control system. To study and suppress thrust noise, a cusped field thruster, which has the feasibility of being applied to the drag-free control tasks, is used in this research. According to the experimental results of thrust noise, it can be seen that the thrust noise spectrum under different working conditions is different slightly, but the trend is similar. The amplitude spectrum densities of thrust in the high-frequency region present white noise characteristics, while the amplitude spectrum densities increase with the decrease of frequency in the low-frequency region. Based on the input-output thrust model and the experimental data of the anode voltage and flow rate, the effects of the anode voltage fluctuation and flow rate fluctuation on the thrust noise are analyzed. The high-frequency part (above 0.05Hz) of thrust noise is mainly caused by the flow rate noise, while the drift of thruster operation states leads to low-frequency (10−3Hz–0.05Hz) fluctuation of thrust. Finally, based on the feedback control theory, a noise suppression method is designed by controlling the anode voltage. Closed-loop simulation results of the thruster verify that the method is effective.

Robust control of q-profile and βp using data-driven models on EAST

• A novel H ∞ robust control scheme is developed for the current profile control using data-driven models. • A decoupling technique enables the control scheme to exhibit multi-functional characteristics. • An anti-windup technique can compensate the effects from moderate time-delays and actuator dynamics. • An efficient procedure is provided to support the H-mode steady-state operation on EAST. • Three general performance indexes are proposed to characterize the current profile control performance. A new robust feedback controller for the safety factor profile and poloidal plasma pressure parameter has been developed using a two-time-scale data-driven model. The model describes the linear plasma responses of the ι profile and β p with respect to auxiliary heating & current drive (H&CD) powers, around a typical plasma equilibrium in an H-mode steady-state plasma discharge on EAST. The feedback controller comprises a carefully designed low-pass filter for the timescale separation, a decoupling module and three local controllers synthesized from the H ∞ norm optimization and the singular value decomposition. The actuators are the lower hybrid current drive (LHCD) system at 4.6 GHz and the ion cyclotron resonance heating (ICRH) system at 33 MHz. Taking into account the actuation dynamics, an anti-windup technique is employed to condition the controller online aiming to attenuate the negative effects from moderate time delays and power saturations. Extensive nonlinear closed-loop simulations with the METIS code suggest that high β p and negative central magnetic shear that characterize advanced tokamak plasma scenarios can be achieved and sustained on EAST with good tracking performance and reasonable robustness via the proposed control scheme. The feedback control of the core ι profile and β p with a range of time delays, power saturations and varying weighting functions are evaluated numerically, compared and discussed. The control robustness to plasma parameter uncertainties including the line-averaged plasma density 〈 n ¯ e 〉 , the H-mode enhancement factor H factor and the effective charge number Z eff are assessed and analyzed.

Controller design by symbolic regression

A novel method of empirical controller design is introduced with the potential to produce exotic controller forms. The controllers in this method are derived by symbolic regression (SR) to be in equation form, hence, they are legible in how the controller output is computed as a function of loop variables. Because SR is computationally costly due to its extensive search of controller space, requiring evaluation of millions, if not billions, of candidate controllers, the candidate controllers cannot be evaluated in closed-loop due to the high cost of simulation associated with such evaluation. This paper offers a recourse to this closed-loop evaluation by allowing evaluations to be performed algebraically. To this end, a method of inverse solution is introduced that estimates the plant input for a desired plant output. This estimated plant input is then used as the target output for candidate controllers that can be readily evaluated algebraically based on the available time series of loop variables associated with the desired plant output. Unlike traditional control design which relies on closed-loop performance metrics to provide controller performance guarantees, the proposed open-loop approach sacrifices such guarantees in favor of new controller forms that it may yield. Therefore, the fidelity, as controllers, of candidate controllers need to be verified post-design. For this purpose, the candidate controllers are first evaluated as controllers in closed-loop simulation. Once verified by simulation, they need to be validated for closed-loop stability, as demonstrated for one of the studied cases.

Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Hence, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

Design of Flight Closed Loop Simulation System Based on Runge-Kutta Algorithm

To solve the problem that the function/performance of the module cannot be fully verified in the laboratory or in the field during the design stage of the airborne radar system, the paper proposes and designs an airborne flight closed-loop simulation system. The flight simulator, which is the core of the entire simulation system, uses the Runge-Kutta algorithm to solve the model and solve the 6-degree-of-freedom nonlinear model of the aircraft in real time, providing realistic aircraft movement data for the airborne radar system. The design builds a Matlab/Simulink simulation model and embeds it in simulation test software to run independently as a model component. The verification test of the closed-loop simulation system shows that the system can realize the real-time simulation of the entire flight environment and meet the design performance requirements.

Consequences of error in natural mortality and its estimation in stock assessment models

Natural mortality (M) is often considered to be one of the most important parameters in a fish stock assessment and affects productivity estimates for the population. However, it is also among the most difficult parameters to estimate using commonly available data. The magnitude of error (both bias and variance) when estimating this parameter can be substantial and can be affected by ignoring its variation over time, space, age, and length. In this study we explore the implications of errors in M on estimation and management performance using simulations and illustrative examples. The error in management reference points such as F35% and FMSY is related directly to the error associated with M. Estimates of biomass are expected to be positively biased when M is over-estimated and vice versa. Use of feedback management strategies reduces the impact of errors in M, but performance in meeting management objectives is compromised when M is in error. Estimating M was found to perform better than pre-specifying M in closed-loop simulations. Also, we found that the consequences of setting M to an incorrect value were reduced if stock-recruitment steepness was estimated. Based on our study and a review of related work, we advocate estimating M within an assessment, ideally with a prior for M tailored to the stock concerned.

Closed-Loop High-Fidelity Simulation Integrating Finite Element Modeling With Feedback Controls in Additive Manufacturing

Abstract A high-precision additive manufacturing (AM) process, powder bed fusion (PBF) has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While finite element modeling and closed-loop control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. This paper builds a first-instance closed-loop simulation framework by integrating high-fidelity finite element modeling with feedback controls originally developed for general mechatronics systems. By utilizing the output signals (e.g., melt pool width) retrieved from the finite element model (FEM) to update directly the control signals (e.g., laser power) sent to the model, the proposed closed-loop framework enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in- and cross-layer thermomechanical interactions. From there, we build a repetitive control (RC) algorithm to attenuate variations of the melt pool width.

Investigation of Linear Higher Harmonic Control Algorithm for Rotorcraft Vibration Reduction

Abstract A linear quadratic Gaussian (LQG) controller for active vibratory loads reduction in helicopters is proposed based on a revisited higher harmonic control (HHC) input by active trailing-edge flaps (ATEFs). Conventional individual blade control (IBC) input is redefined using N − 1/rev interblade phase lead, N/rev collective, and N + 1/rev interblade phase lag signals where 1/rev frequency modulation originates from the multiblade coordinate (MBC) transform. A Mach-scaled flap blade is designed and analyzed by the multibody dynamics analysis DYMORE. A linear time-invariant representation is identified from N/rev envelopes of the input and output responses obtained by DYMORE analysis. A matlab/simulink closed-loop control simulation is designed using the identified state-space realization. The N/rev vibratory loads are reduced up to 52% with flap deflections and the linear control results match well with the nonlinear responses obtained from DYMORE. Furthermore, the multivariable closed-loop stability estimated by the loop transfer functions using disk margin analysis reveals sufficient gain and phase margins.

Guidance, Navigation, and Control System for Landing near Plume Source on Enceladus

Saturn’s moon Enceladus is one of the most promising candidates in the solar system for hosting microbial life. Plumes emanating from its south pole are ejecting water directly from the ocean to space. The Enceladus Explorer (EnEx) lander mission concept aims to land near a plume source and deploy a melting probe to sample liquid pockets under the plumes for life. Landing there would be exceptionally challenging due to the rough canyonous topography, polar lighting conditions, a surface covered by superfine snow, and strict planetary protection regulations. In this work the Guidance, Navigation, and Control (GN&C) system and operations for the critical approach phase of such a landing are investigated. An advanced GN&C system is defined, comprising extended Kalman filter simultaneous localization and mapping (EKF-SLAM)–based navigation, fuzzy-reasoning-based hazard detection and avoidance, and convex guidance. To verify the landing feasibility and further refine the concept, the system is investigated using a closed-loop simulation tool we created. The performance of each function is analyzed separately with sensitivity and worst-case analyses. A Monte Carlo simulation encompassing all functions is performed to estimate mission success statistics.

Closed-loop platoon simulation with cooperative intelligent transportation systems based on vehicle-to-X communication

The continuous enhancement of X-in-the-Loop (XiL) simulation methods is one key to efficiently test advanced vehicle control functions and ensure high software quality under a rising cost and time pressure. Systems, which control vehicles based on sensor perception can already be evaluated in XiL simulations. However, future vehicles will not exclusively represent stand-alone systems anymore, but involve Cooperative Intelligent Transport Systems (C-ITS), distributed among different traffic participants. While several simulation environments for C-ITS feasibility studies are available, common virtual test frameworks cannot incorporate multiple interacting and communicating road users with the required fidelity to secure C-ITS software, e.g. for platooning applications.Within the scope of this publication, a method for virtual testing and calibration of in-vehicle C-ITS using a high fidelity vehicle platoon model is presented. Multiple closed-loop controlled vehicles and the mutual interferences between them are simulated in a common 3D environment alongside other traffic. Each high fidelity vehicle model individually simulates the longitudinal and lateral response, the generation of sensor data plus the exchange of vehicle-to-vehicle data. As a proof of concept, a model-in-the-loop simulation for a Cooperative Adaptive Cruise Control (CACC) was conducted. The simulation environment included five high fidelity vehicle models forming a platoon, coupled with CACC controller models under test.The results reveal that the developed simulation environment captures the interaction within collaborative vehicle groups while modeling individual vehicles as controlled systems in the same tool. At the same time the method satisfies all requirements for XiL testing. Functional tests of multiple interacting vehicle controllers have been successfully carried out. In the performed feasibility study simulation-based tests uncovered an insufficient control stability of the CACC. Upcoming series developments and homologations of in-vehicle C-ITS software and hardware can be supported by the new virtual test environment to overcome connectivity, reproducibility and cost efficiency limitations of physical test environments. Next steps include proving the method in use for series development and extending it to cover vehicle-to-infrastructure use cases.

Control assessment in coupled local district heating and electrical distribution grids: Model predictive control of electric booster heaters

Intelligent control schemes are essential for the implementation of smart energy systems, where district heating and electric networks are tightly interconnected. The increasing complexity of such networked infrastructure has resulted in the need to test and assess control algorithms before field deployment. This work presents a method to assess advanced control schemes for thermal-electric appliances with explicit consideration of coupled heat and power networks. It is based on closed loop simulation of high-fidelity physical system models, using dynamic thermal-hydraulic district heating and electric distribution network models, and low-fidelity time-discrete advanced control models. Co-simulation is used to perform coupled simulations of the different involved domains and tools. A test case is presented where a model predictive control scheme for grid friendly operation of domestic hot water electric booster heaters is implemented in a low-temperature district heating and low-voltage electric distribution network. Test case results show that the control is able to reduce peaks in district heating and electric networks compared to a simple reference controller. A comparison between using perfect and naive forecasts shows that control performance highly depends on the availability of accurate predictions. The results underline the versatility of the method to assess different control schemes in integrated networks.