Closed-loop Simulation Research Articles

Fractional order multi-scheduling parameters based LPV modelling and robust switching H∞ controllers design for steam dump system of nuclear power plant

In this research work, the highly challenging problem of novel modelling and nonlinear control of steam dump system of Pressurized Water Reactor (PWR) type Nuclear Power Plant (NPP) is attempted. The Fractional Order Multi- Scheduling Parameters based Multi-Input Single- Output Linear Parameter Varying (FO-MSP-MISO-LPV) model of Steam Dump System (SDS) is estimated with uncertain dynamics under sudden load variation transients. MSP for uncertain dynamics of SDS in FO framework is the most challenging problem and attempted in a novel fashion for the first time in nuclear industry. Scheduling parameters are dynamic in nature that makes the control problem more challenging. The Model is estimated experimentally by least square method using innovative plant operational data of opening positions of different valves as input variables and steam pressure as an output variable and cold leg coolant temperature coefficient of reactivity, hot leg coolant temperature coefficient, steam flow rate and turbine power as dynamic scheduling parameters. A switching controller is designed to address variable conditions of steam pressure for the actuation of dump valves, relief valves and safety valves in SDS. A robust fractional order LPV switching H∞ (RFO-LPV-SWH∞) controllers are formulated and designed for FO-MSP-MISO-LPV model. The design of RFO-LPV-SWH∞ controllers is another significant contribution in switching mode with non-integer and LPV hybrid framework. RFO-LPV-SWH∞ controllers are tested, simulated and validated against benchmark transients as laid down in Final Safety Analysis Report (FSAR) of PWR-type NPP. The input and output variables at first and second vertex of polytope are fast reference tracking under highly nonlinear uncertain dynamics of SDS. Closed loop simulation experiments are conducted and proved that the proposed closed framework is robust in performance under parametric uncertainty.

Free Final-Time Fuel-Optimal Powered Landing Guidance Algorithm Combing Lossless Convex Optimization with Deep Neural Network Predictor

The real-time guidance algorithm is the key technology of the powered landing. Given the lack of real-time performance of the convex optimization algorithm with free final time, a lossless convex optimization (LCvx) algorithm based on the deep neural network (DNN) predictor is proposed. Firstly, the DNN predictor is built to map the optimal final time. Then, the LCvx algorithm is used to solve the problem of fuel-optimal powered landing with the given final time. The optimality and real-time performance of the proposed algorithm are verified by numerical examples. Finally, a closed-loop simulation framework is constructed, and the accuracy of landing under various disturbances is verified. The proposed method does not need complex iterative operations compared with the traditional algorithm with free final time. Therefore, the computational efficiency can be improved by an order of magnitude.

Physics-based modeling of homogeneous charge compression ignition engines with exhaust throttling

Homogeneous charge compression ignition (HCCI) engines create a more efficient power source for either stationary power generators or automotive applications. Due to the absence of direct actuation of ignition, HCCI based combustion control is quite challenging. For the purpose of control synthesis and design, a crank-angle based dynamic model of an HCCI engine with internal exhaust gas recirculation (EGR) is proposed in this paper. The HCCI autoignition timing is controlled by the amount of internal EGR induced by the cost effective exhaust throttling strategy. The zero dimensional single zone model is developed based on conservation of mass and energy and ideal gas law. The model is comprised of five subsystems: cylinder volume, intake, and exhaust runners along with combustion and heat-transfer models. Inputs to the model include engine speed, intake temperature, fueling rate, intake, and exhaust throttle positions. Outputs of the model contain the pressure, temperature, mass and burned gas fraction in the intake, exhaust and cylinder, air-to-fuel ratio (AFR), heat release rate, combustion phasing, and the indicated mean effective pressure (IMEP). The model is developed based on MATLAB/Simulink and validated against experimental data under steady-state and transient conditions at various intake temperatures, fueling levels, and exhaust throttle positions. Results demonstrate that by changing the exhaust throttle position, the pressure in the exhaust runner is regulated. As a result, the residual gas fraction is altered, leading to changes in mixture temperature and thus control of the HCCI combustion phasing. In the end, closed loop simulation is conducted with a linear quadratic Gaussian (LQG) controller applied to the crank-angle based model for regulation of combustion timing CA50 using exhaust throttle during load (fueling level) changes. In the future, this model can be utilized for control development and hardware in the loop (HIL) simulation.

An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

An Integrated Dynamic Closed Loop Simulation Platform for Elbow Flexion Augmentation Using an Upper Limb Exosuit Model

Wearable robotic devices are designed to assist, enhance or restore human muscle performance. Understanding how a wearable robotic device changes human biomechanics through complex interaction is important to guide its proper design, parametric optimization and functional success. The present work develops a human-machine-interaction simulation platform for closed loop dynamic analysis with feedback control and to study the effect of soft-robotic wearables on human physiology. The proposed simulation platform incorporates Computed Muscle Control (CMC) algorithm and is implemented using the MATLAB -OpenSim interface. The framework is generic and will allow incorporation of any advanced control strategy for the wearable devices. As a demonstration, a Gravity Compensation (GC) controller has been implemented on the wearable device and the resulting decrease in the joint moments, muscle activations and metabolic costs during a simple repetitive load lifting task with two different speeds is investigated.

Dynamic Characteristic Study of Supercritical CO2 Recompression Brayton Cycle System

The supercritical carbon dioxide (SCO2) Brayton cycle has been regarded as the main development direction of future nuclear power generation by more and more scholars, due to its high environmental efficiency and high thermoelectric conversion rate. However, due to fluctuations in the operation of the primary loop of the system with nuclear energy, parameters such as the power of the heat source and the mass flow of the working medium in the system will change, which will affect the dynamic performance and operation of the SCO2 Brayton cycle system. Therefore, it is necessary to study the dynamic response of the system performance under disturbance conditions, analyze the operating characteristics of the SCO2 Brayton cycle system. In this paper, a comprehensive dynamic model of SCO2 recompression Brayton cycle, which analyzes the response curves of critical parameters under the disturbance of heat source heating power and system mass flow rate, is accurately developed based on Simulink software. In order to verify the validity of the proposed model, the simulation results are compared with the experimental results conducted by Sandia Laboratory under the same conditions. The results show that the model has high accuracy, and can reflect the dynamic response of system performance under parameter perturbation. In this paper, the closed-loop simulation is innovatively performed to show the dynamic response to step-change in the heat source power and mass flow rate. And the thermal efficiency is about 31.85%, when the system operates stably at the design point of working condition. If a disturbance is applied to the system, the temperature change will be mainly concentrated near the heat source of the cycle, and the change near the precooler will be relatively small. The change of the heat source power will lead to a large monotonic variation of cycle efficiency. By contrast, an inflection point in cycle efficiency will be resulted in by changing the system mass flow rate. The results of this paper would provide good approaches for the design, control, and improvement of the SCO2 Brayton cycle.

Modeling and Simulation of a Double-Stage Single-Phase Grid-Connected PV System

This paper presents the design model and simulation of a double-stage single-phase grid-connected PV system. This system includes an MPPT DC-DC boost power converter and a transformer-less single-phase DC-AC power inverter, which is connected to the power grid with an LCL filter. In the DC-DC boost converter, the Perturb and Observe algorithms are combined. The output voltage of the DC-DC converter is regulated, and the DC/AC output voltage is synchronized with the grid. The MATLAB-Simulink environment is used for the closed-loop simulation of the proposed setup. The simulation results are presented for various solar irradiance conditions and show that photovoltaic current is generated and matches the phase of grid voltage.

Beyond pull-in angle control of a dual axis torsional micro-actuator considering bending effects

Torsional micro-actuators are fundamental elements in micro-system technologies. They've found several applications in optical cross connects, endoscopic optical coherence tomography and miniaturized projection displays. Effective angle control in these actuators is necessary for their acceptable performance. So, the objective of the current paper is to develop a novel fuzzy controller to extend the travel range of these actuators based on an accurate dynamic model. The degrees of freedom (DoF) of the system includes two rotational angles resulted from twist of the torsion micro-beams and two translational ones originating from their bending towards the electrodes. Using these four DoFs, Lagrange equations are employed to develop a novel dynamic model for the dual axis torsional micro-actuator. Comparing the response of the system obtained from 4-DoF and 2-DoF models with those resulted from nonlinear recursive finite element simulations reveals that the use of the 4-DoF model is critical for having accurate predictions of the system's behavior. Then dynamic simulations to step input voltages are used for proposing an expert fuzzy rule base which describes the dependence of the rotation angles to input voltages. Using this rule base along with a singleton fuzzifier, product inference engine and center average defuzzifier, an incremental fuzzy controller is designed for smooth control of the rotation angles of the actuator about the desired angles. To maximize the performance of the closed-loop system, the centers of the fuzzy sets of the THEN parts of the rules are adaptively tuned using a higher-level fuzzy system. Closed loop simulations shows that the presented novel fuzzy controller is capable of stabilizing the angles of the actuator even beyond their inherently unstable pull-in range. The proposed 4-DoF model can be used for design optimization of dual axis torsional micro-actuators. Additionally, the developed incremental fuzzy controller can be employed for controlling the response of other types of electrostatically actuated micro/nano-systems.

A predictive neural hierarchical framework for on-line time-optimal motion planning and control of black-box vehicle models

This paper addresses the on-line minimum-time motion planning and control of a black-box racing vehicle model. We present a hierarchical control framework, composed of a high-level non-linear model predictive controller (NMPC) based on an advanced kineto-dynamical vehicle model, a low-level neural network to compute the inverse steering dynamics and a longitudinal controller for the low-level tracking of speed profiles. An off-line identification procedure, consisting of simulated manoeuvres, is defined to learn the high-level and low-level models. A closed-loop simulation is setup to control the black-box vehicle near the limits of handling along a racetrack. Simulation results are compared with the off-line solution of a minimum-time-optimal control problem.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept.

Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

A novel power transformer on-line setting impedance protection

As the backup protection of adjacent lines, transformer impedance protection needs to be strictly coordinated with line distance protection in impedance setting and time setting. The traditional transformer impedance protection has the problems of heavy workload and difficult coordination in setting and coordination. An on-line self-setting transformer impedance protection scheme was proposed, which impedance setting values and time setting values were no longer required, and can automatically cooperate with the line protection of the same scheme with the subordinate line. The algorithm proposed in this paper was incorporated into the actual protection device, and a closed-loop simulation environment was built together with RTDS. The applicability and reliability of the proposed scheme are verified by the closed-loop simulation experiment of the protection device.

Confusion in differential piston measurement with the pyramid wavefront sensor

Context. The pyramid was proven to be a highly sensitive and versatile wave-front sensor (WFS) and has been selected to be installed on the single conjugate adaptive optics (AO) systems of the extremely large telescope (ELT). The pupil of the ELT is fragmented by the secondary support spider arms, which are larger than the spatial coherence length of the atmospheric turbulence. This causes a rupture of the incoming wavefront continuity, which means that we need to be able to measure the differential pistons across the spider arms to achieve full wavefront reconstruction. Aims. We investigate the reaction of the modulated pyramid WFS to discontinuous aberrations in presence of phase residuals after AO compensation for a range of expected observing conditions at the location of the ELT. We then explore some parameters of the sensor in order to improve its sensitivity to the wavefront discontinuities, including optical gain compensation, specific sensor modulation paths, and the number of faces of the pyramidal prism. Methods. We derived sensitivity loss and modal cross-talk strength coefficients around static post-AO residual phases using the COMPASS end-to-end AO simulation software. Moreover, extensive closed-loop AO simulations let us derive two wavefront error criteria that are appropriate for a fragmented pupil geometry. We used these to assess the wavefront reconstruction performance. Results. We show that on the ELT, the pyramid experiences a drastic loss in sensitivity and also non-linear modal cross-talks. Added to the limited capture range, this makes it poorly suited for phase discontinuity measurements at visible wavelengths. The strategies we studied to increase the sensitivity and reduce the modal cross-talk provide an improvement of the reconstruction for low D/r0(λWFS) values, that is, for a K-band pyramid. In presence of a large residual wavefront variance, however, a similar sensor in visible light fails to provide the necessary trade-off to measure both the continuous modes and the wavefront discontinuities. Conclusions. The ELT instruments, designed with only visible-band pyramid WFSs, will not be able to perform a direct measurement of the wavefront discontinuities. They will have to rely on Kolmogorov statistics to restore the continuity of the atmospheric corrugated wavefront. If any other source of discontinuities arises on the ELT, instruments will need an additional, dedicated WFS.

Tracking inter-annual terrestrial water storage variations over Lake Baikal basin from GRACE and GRACE Follow-On missions

The Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on mission (GRACE Follow-On) can accurately track the Earth’s mass variations, which have been successfully used in various geoscience researches related to global and regional hydrological process. In this study, satellite gravimetry (GRACE and GRACR Follow-On) and hydrological model (GLDAS) are used to estimate the TWS variations over LBB in 2003–2020. As the world’s largest freshwater lake by volume, the terrestrial water storage (TWS) variations in Lake Baikal basin (LBB) significantly affect the surrounding ecological environment. However, the spatial scale of LBB is relatively small when compared to approximately 300 km spatial resolution of GRACE and GRACE Follow-On mission. Therefore, the forward modeling method is used to alleviate the leakage errors during satellite gravimetry data processing. To assess the performance of forward modeling method, a close-loop simulation study is designed in noise-contaminated condition. The simulation results demonstrate the feasibility of forward modeling method in accurately estimating TWS variations over LBB. After appropriate leakage correction, the inter-annual TWS from satellite gravimetry agrees well with GLDAS. The TWS variations over LBB demonstrate that (1) there are drastic variations and periodic characteristics around 4–5 years, and the drought events are observed in 2003, 2007, 2011 and 2015 as relatively smaller TWSs. (2) The soil moisture and groundwater are the major contributors to TWS variations (with the correlation coefficient of 0.88 and 0.89), and the surface water is the main contributor to the seasonal variations over LBB. (3) The TWS presents about 6 moths delay to precipitation variations, while the precipitation is still the major driver to affect the hydrological processes over this region.

Dynamic control allocation between onboard and delayed remote control for unmanned aircraft system detect-and-avoid

This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

Simulation platform of an industrial propylene-propane splitter integrated to Advanced Process Control for Real Time Optimization experiments

The present paper shows the development of a dynamic rigorous model of a propylene-propane splitter connected to an industrial MPC controller via OPC-UA. The model includes the material and energy balances, thermodynamic equilibrium, and constitutive equations. Some of the PI controllers presented in the real plant have also been modeled. A dynamic model requires further information as the sizing of the equipments, heat transfer coefficients, thermodynamics data and a good initial value for the state and algebraic variables. The validation of the model was performed in open loop and in closed loop simulations. The simulation results were compared to the historical data of the process. As future work, the simulation platform created will be used to study new algorithms of RTO with Modifier Adaptation methodology.

Predictive Control of Batch Crystallization Process Using Machine Learning

This work develops a framework for building machine learning models and machine learning-based predictive control schemes for batch crystallization processes. We consider a seeded fesoterodine fumarate cooling crystallization and dissolution in a batch reactor and present the methodology and implementation of simulation, modeling, and controller design. Specifically, to address the experimental data scarcity problem, we first develop a population balance model (PBM) based on published kinetic parameters to describe the formation of crystals via nucleation, growth, and agglomeration. Then, recurrent neural network (RNN) models are developed using data from extensive simulations of the semi-empirical PBM under various operating conditions to capture the process dynamic behavior. The model predictive control (MPC) scheme using RNN models is developed to optimize the crystallization process in terms of product yield, crystal size, and energy consumption, while accounting for the constraints on the manipulated inputs. Through open- and closed-loop simulations, it is demonstrated that the RNN models well capture the process dynamics, and the RNN-based MPC achieves desired product yield and crystal size with significantly improved computational efficiency.

Improved offset-free model predictive control utilizing learned model-plant mismatch map

The requirement for a framework that effectively overcomes the limitation of model-based and data-driven control strategies by combining both methods continues to grow. In this study, we propose an approach that learns the model-plant mismatch map and utilizes it based on the offset-free model predictive control (MPC). Specifically, the mismatch map is learned via general regression neural network (GRNN) that has been applied in broad range of fields based on the data from the process, and then the learned mismatch information is provided to the MPC system. In addition, since the approximated mismatch map via GRNN cannot be perfect, an additional supplementary disturbance estimator is utilize to ensure the zero-offset tracking property. Finally, the learned and supplementary disturbance signals are applied to the target problem and the optimal control problem based on the offset-free MPC framework. The effectiveness of the proposed combined model-based and data driven framework is demonstrated by closed-loop simulation. The result shows that the proposed framework can improve the closed-loop tracking performance by utilizing both the learned mismatch information from GRNN and the stabilizing property of the supplementary disturbance estimator.

Estudio de simuladores dinámicos con ADAMS y Matlab/Simulink para sistemas de control

ADAMS-MATLAB co-simulation is widely used in complex system development to control models of closed-loop mechanisms. An advantage of ADAMS is to facilitate the configuration of parameters such as friction. However, its mathematical friction model is unknown. There are alternatives such as Simscape Multibody that facilitate the configuration of any friction model. This paper presents a case study of dynamic simulation using the mathematical model of a mechanism in order to compare it with ADAMS and Simscape Multibody. The state-space model uses the parameters b and fc – viscous and Coulomb friction coefficients. However, ADAMS uses only one dynamic friction parameter µ. Since both friction parameters do not appear in ADAMS, a parametric identification procedure was used to determine the equivalency between the friction coefficients b and fc compared to µ. To identify the friction model of ADAMS, open-loop and closed-loop simulations were proposed. To compare the simulation results numerically, the L2 norm was used. The reference was the model solved with the ODE45 function.

Learning Nonlinear Model Predictive Controllers and Virtual Sensors with Koopman Operators

Model Predictive Control is an industry-standard technique used to drive systems based on their internal dynamics. When not all states are available for feedback, a state estimator, such as an Extended Kalman Filter, is employed to achieve control over the complete system state. Nevertheless, when the system under control is nonlinear, these two combined methods can result in a computationally heavy control strategy, raising significantly the cost of implementing it online. In this paper, a data-driven strategy based on the Koopman Operator theory is presented to identify and replicate the dynamics of the Kalman Filter plus Model Predictive Controller pair in a resource-efficient scheme. First, a closed-loop operation data-set is generated from a pre-calibrated reference controller; then, a finite-dimensional approximation is derived for the Koopman Operator of the filter plus controller dynamics in the lifted space of observables; finally, the stability of the identified controller is evaluated through closed-loop simulations; in case the desired response has not been achieved, the identification process is performed iteratively with a progressively increasing regularization coefficient. A simulated example applied to the Van der Pol oscillator is presented to illustrate the effectiveness of the approach.

Model-Predictive Quantum Control via Hamiltonian Learning

This work proposes an end-to-end framework for the learning-enabled control of closed quantum systems. The proposed learning technique is the first of its kind to utilize a hierarchical design which layers probing control, quantum state tomography, quantum process tomography, and Hamiltonian learning to identify both the internal and control Hamiltonians. Within this context, a novel quantum process tomography algorithm is presented which involves optimization on the unitary group, i.e., the space of unitary operators, to ensure physically meaningful predictions. Our scalable Hamiltonian learning algorithms have low memory requirements and tunable computational complexity. Once the Hamiltonians are learned, we formalize data-driven model-predictive quantum control (MPQC). This technique utilizes the learned model to compute quantum control parameters in a closed-loop simulation. Then, the optimized control input is given to a physical quantum system in an open-loop fashion. Simulations show model-predictive quantum control to be more efficient than the current state-of-the-art, quantum optimal control, when sequential quadratic programming (SQP) is used to solve each control problem.