Model Control Design Research Articles

適応的空力弾性制御モデルの構築と風洞実験による検証 ---圧力制御と翼端変位推定---

GLA (= Gust Load Alleviation) control is an important active safety technology, especially for light-weight flexible wing aircraft with high aspect ratio wings. This paper proposes an adaptive aero-servo-elastic model for control design, including an aerodynamic load distribution estimator using in-flight partial pressure sensing. The GLA control law obtained based on the proposed model consists of pressure control and mode estimation. In general, aero-servo-elastic models require an wing structure dynamics and an aerodynamic model around the wing. However, it is difficult to construct a generic model in advance for complex unsteady aerodynamic phenomena where turbulence and vibration of flexible wings interact. In this case, the optimality and the reliability of a model-based GLA control is significantly reduced. To minimize the uncertainty of the model for unsteady aerodynamics, this method proposes the use of an autoregressive model with time-varying coefficients, which are updated in real-time based on in-flight pressure sensing. The feasibility of the proposed GLA scheme is demonstrated in terms of both pressure control and wing tip displacement estimation through wind tunnel experiments.

Controller considerations for attenuating noise in process variables in presence of uncertainties in plant parameters

This paper analyzes the effect of uncertainties on noise amplification in the internal model control (IMC) and classical control design technique. It is observed that IMC based on integral absolute error (IAE) design criteria (IMC-IAE) boosts noise in the process variables, system output as well as in control input for certain class of systems. Moreover, the resulting control system becomes highly sensitive at high frequency with uncertainty in plant parameters which further boosts noise in process variables. Integral square error (ISE) based IMC (IMC-ISE) is, however, free from the issues of sensitivity and noise amplification at high frequencies and not analyzed in this paper. This issue also exists in well known classical controllers such as PID controller, lead, lag, lead-lag compensators, etc. The solution to this problem is to choose the controller such that the open loop transfer function (OLTF) and hence the closed loop transfer function (CLTF) are strictly proper, and a formal proof to this effect is laid down in this paper. Further, simulation study is carried out to show the impact of noise and the effect of plant uncertainty in output and control input for IMC-IAE control system. The proposed theory is validated experimentally on a DC servo system by comparing noise power of the control input for IMC-IAE and PID controllers.

Modeling and Contouring Control for Cantilever Beam Machine With Structural Flexibility

In biaxial contouring control applications, the inherent structural flexibility of machines can lead to position discrepancies between the manipulator and actuator, and thus deteriorate the manufacturing performance, especially when the controller is designed without available end-effector side feedback. In this work, we focus on the end-effector contouring control problem for industrial machines with position-dependent flexibility to improve the contouring performance while eliminating the effect of mechanical vibration. A model for the widely used cantilever beam machine is developed to describe the dynamics of the end-effector by capturing the rotation and coupled dynamics between axes. The proposed model is validated through experiment and systematically reduced to switched linear time-invariant models for controller design. By adopting the extended state observer, the proposed control architecture decouples the dynamics between the X and Y-axis and simplifies the controller design process. The model predictive control method is utilised for improving the contouring performance while reducing mechanical vibration. The efficacy of the proposed control framework is demonstrated and validated on the designed high-fidelity model. Performance comparisons between the proposed approach with benchmark controllers are presented.

Using SIR Epidemic Modeling and Control to Teach Process Dynamics and Control to Chemical Engineers

The COVID-19 pandemic has brought about unprecedented opportunities to introduce control systems topics in the undergraduate engineering curriculum. This paper describes two computer modeling assignments based on MATLAB with Simulink developed for CHE 461: Process Dynamics and Control taught at Arizona State University during the fall 2020 semester. A myriad of important concepts, among these dynamic modeling using conservation and accounting principles, linearization, state-space system and transfer function model representations, PID feedback control and Internal Model Control design can be applied to the problem and explained to students in the context of a significant world event representing a unique “process” system, notably the COVID-19 pandemic.

The in situ approach to model identification and control design for pressure regulation in Water Distribution Networks: An in silico evaluation

In the context of Water Distribution Networks, service pressure regulation is an important technique that allows reductions in leakage, risk of pipe bursts and mechanical stress to the infrastructure. Recent works demonstrated in silico, i.e. numerically, that linear control systems can be effectively adopted for this task, provided that a careful tuning is performed. Specifically, the tuning should be based on high order, linear models, which describe the system dynamics around a nominal working point. These models can be straightforwardly derived in a simulated environment, but their in situ identification may be challenging due to the presence of non-measurable, exogenous disturbances. This work moves a step forward towards the application of service pressure regulation in situ, by proposing an effective model identification approach for the linear models, based on spectral analysis. The novel approach can cope with exogenous, non-measured disturbances acting during the identification experiments, and considers possible constraints limiting the experimental design. Moreover, the models identified in the in situ conditions are exploited to synthesise linear regulators and assess the closed-loop performances of the overall control methodology. Though being presented and tested in silico, this work assumes a strong practical relevance in view of the results achieved. It in fact demonstrates that novel control schemes, previously designed in nominal conditions only, can be actually designed and implemented in a real scenario, thus making pressure control safer, more reliable and more effective. Finally, the numerical analysis allows for a comparison of both identification and control results with to those obtained in nominal conditions, to provide further insight and stress the reliability of the proposed methodology.

Estimation of reliable vehicle dynamic model using IMU/GNSS data fusion for stability controller design

This study deals with the enhancement of reliability of vehicle dynamic model for the stability controller design by estimating the effect of uncertainties in both tire forces and vehicle dynamics. In this respect, any selected initial kinetic model of vehicle is updated at each instant by adding some complementary terms using a novel prediction approach. The proposed estimation method uses the information fusion between the inertial measurement unit (IMU) and the aided systems including the global navigation satellite system (GNSS) and the calibrated compass system. The bias of acceleration sensors and the drift of gyroscope are compensated using a novel adaptive algorithm by which the estimator weights are tuned automatically. Therefore, the vehicle longitudinal and lateral velocities together with the yaw rate are accurately estimated with a high frequency. The proposed estimation algorithm is mathematically analyzed for the stochastic stability and experimentally evaluated through real-world vehicular tests. Accordingly, a nonlinear controller for vehicle directional stability is designed based on the updated dynamic model within the CarSim software environment as a virtual vehicle experiment platform. The obtained results reveal that, the designed data fusion algorithm remarkably improves the estimation performance in the presence of system and measurement uncertainties. Also, the controller designed by the estimated vehicle dynamic model will be reliable and robust in the presence of uncertainties.

유전 알고리즘을 이용한 최적 서보 제어 기반의 다입 · 출력 가변속 오일쿨러 시스템의 정밀한 온도제어

A variable speed refrigeration system (VSRS) has been applied to various industrial fields because of its excellent energy saving performance and partial load response capability. In this paper, an optimal servo controller for a variable speed oil cooler system was investigated to obtain precise temperature control performance against disturbance and model uncertainty. A state space model for controller design was derived from empirical transfer function. The controller was designed as a servo type to follow the step command strictly while minimizing an evaluation function of the secondary form for state variable and input energy. Especially, the weighting matrix, a key parameter in an optimal servo controller design, was automatically designed to meet design specifications using genetic algorithm (GA). The validity of the suggested controller was confirmed through MATLAB-based simulations and actual experiments. Moreover, the optimality and ease of design of the proposed optimal servo controller were demonstrated by comparing results with a conventional PI controller.

An iterative identification algorithm and its convergence analysis of closed-loop power system based on ambient signals

Identification and control are important problems of closed-loop power system. At present, most studies are separate identification methods. This paper studies an online and real-time integrated identification method, which can solve the problems of model set and controller design of closed-loop power system. This paper investigates a new iterative identification algorithm and its convergence problem of closed-loop power system based on ambient signals. Firstly, the whole algorithm procedure is given. This algorithm uses the iterative process under the closed-loop condition, which combines system model identification with controller design. Then the complementary of model identification and control design has been realized. Secondly, because of the dynamic performance of the iterative identification algorithm, it has characteristics described from the perspective of a partitioned dynamic system. Regard each iterative identification step as a state node. In this situation, the algorithm guarantees all the state nodes converge to the Lyapunov stable equilibrium. Finally, the simulation results show the correctness and effectiveness of the proposed method through the simulation of a power system with four-machine-two-region.

Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations

Recently, solar power generation systems are more and more popular and widely used in grid connected power generation, intelligent buildings, and power supply in remote areas. For photovoltaic panels, due to the influence of factors such as light intensity and ambient temperature, their output voltage and current become uns, and the output voltage of a single photovoltaic panel is considerably low. As a result, Boost circuits are needed for voltage boosting. PID controller is commonly used for Boost converter because it can effectively control the controlled object according to the characteristics of the controlled object. However, when the controlled object is complex and variable, the appropriate parameters are hardly to be selected by experience, and the fixed controller parameters may lead to unexpected performances under different working conditions. Here, a genetic algorithm combined with BP neural network PID control (GA-BPPID) is proposed to improve both dynamic and anti-interference performances of Boost circuit by introducing the global optimization ability of genetic algorithm and the adaptive adjustment characteristics of BP neural network. System modelling and detailed controller design procedures are provided. Finally, the theoretical analysis and controller design are validated by simulation results.

Design and In Silico Evaluation of a Closed-Loop Hemorrhage Resuscitation Algorithm With Blood Pressure as Controlled Variable

Abstract This paper concerns the design and rigorous in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure (BP) as controlled variable. A lumped-parameter control design model relating volume resuscitation input to blood volume (BV) and BP responses was developed and experimentally validated. Then, three alternative adaptive control algorithms were developed using the control design model: (i) model reference adaptive control (MRAC) with BP feedback, (ii) composite adaptive control (CAC) with BP feedback, and (iii) CAC with BV and BP feedback. To the best of our knowledge, this is the first work to demonstrate model-based control design for hemorrhage resuscitation with readily available BP as feedback. The efficacy of these closed-loop control algorithms was comparatively evaluated as well as compared with an empiric expert knowledge-based algorithm based on 100 realistic virtual patients created using a well-established physiological model of cardiovascular (CV) hemodynamics. The in silico evaluation results suggested that the adaptive control algorithms outperformed the knowledge-based algorithm in terms of both accuracy and robustness in BP set point tracking: the average median performance error (MDPE) and median absolute performance error (MDAPE) were significantly smaller by >99% and >91%, and as well, their interindividual variability was significantly smaller by >88% and >94%. Pending in vivo evaluation, model-based control design may advance the medical autonomy in closed-loop hemorrhage resuscitation.

Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation

Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e., taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design.

Comparison of Optimal Control Designs for a 5 MW Wind Turbine

Optimal controllers, namely Model Predictive Control (MPC), H∞ Control (H∞), and Linear Quadratic Gaussian control (LQG), are designed for a 5 MW horizontal-axis variable-speed wind turbine. The control design models required as part of the optimal control design are obtained by using a high fidelity aeroelastic model (i.e., DNV Bladed). The optimal controllers are eventually designed in three operating modes: below-rated, just below-rated, and above rated-wind speeds, based on linearized control design models. The linearized models are reduced by using a model reduction technique to facilitate the design of optimal controllers. The controllers are analyzed not only in the time domain but also in the frequency domain and on the torque/speed plane. Simulation results demonstrated that optimal controllers perform better than the standard proportional-integral-derivative (PID) controller, particularly for removing oscillation due to the drive-train mode without incorporating a drive-train damper.

Multivariable control of hybrid powertrain dynamics at GM

AbstractThis article illustrates the control design process, from the requirements determination phase to the vehicle validation phase, as applied to GM's first two‐mode hybrid electric powertrains. Customer requirements are first translated into metrics that must be met with the closed loop control design. Physics‐based modeling tools developed in‐house are utilized to simplify the development of control design and simulation models for the multi‐input multi‐output hybrid driveline system. Frequency sweep methods and tools are then employed to validate the models and later to verify the control implementation with vehicle tests. Modern control tools are used to develop a multi‐input multi‐output feedback control design. Finally, vehicle test results show how this design approach is successful in dramatically changing the open loop behavior of the dynamic driveline response, turning an unsalable product into a world class leader in drive quality.

A constraint multi-step prediction method for identification of a water-jet vessel in 3DOF planar motion

In this research work, a Water-Jet catamaran HUSTER-30 is used as a case study to investigate the modeling and system identification of water-jet vessels. There is a natural phenomenon of the water-jet vessels, which is that the vessel cannot go straight with zero steering angle even under absolutely calm water conditions. Identifying the parameters of the water-jet vessel model and guaranteeing the identified parameters cover a wide range of maneuvering is investigated in this paper. The nonlinear asymmetric 3 Degree of Freedom (DOF) model considering water-jet system assembly error and the external force generated by the effect of outlet water flows is established. Moreover, a constrained multi-step prediction (CMSP) method is proposed in this paper to identify parameters of the nonlinear model. A series of experiments are conducted to identify the parameters, and the prediction results demonstrate that the asymmetric model has better performance than the symmetric model. Also, the identified results demonstrate that the parameters identified by the CMSP method have a more accurate prediction than the LS method in a wide range of maneuvering. The future work will focus on optimizing the proposed method and using the identified model for controller design and simulation study.

Robust Fractional-Order Perfect Control for Non-Full Rank Plants Described in the Grünwald-Letnikov IMC Framework

Abstract The advanced analytical study in the field of fractional-order non-full rank inverse model control design is presented in the paper. Following the recent results in this matter it is certain, that the inverse model control-oriented perfect control law can be established for the non-full rank integer-order systems being under the discrete-time state-space reference with zero value. It is shown here, that the perfect control paradigm can be extended to cover the multivariable non-full rank plants governed by the more general Grünwald-Letnikov discrete-time state-space model. Indeed, the postulated approach significantly reduces both iterative and non-iterative computational effort, mainly derived from the approximation of the Moore-Penrose inverse of the non-full rank matrices to finally be inverted. A prevention provided by the new method excludes the detrimental matrix behavior in the form of singularity, often avoided due to the observed ill-conditioned sensitivity. Thus, the new defined robust fractional-order non-full rank instance of such control strategy, supported by the pole-free mechanism, gives rise to the introduction of the general unified non-full rank perfect control-originated theory. Numerical algorithms with simulation investigation clearly confirm the innovative peculiarities provided by the manuscript.

Adaptive model predictive control of a six-rotor electric vertical take-off and landing urban air mobility aircraft subject to motor failure during hovering

In this article, motor failure control of a six-rotor electric vertical take-off and landing (eVTOL) urban air mobility aircraft is investigated using adaptive model predictive control (MPC) based on the linear parameter-varying (LPV) model developed using the nonlinear rigid-body aircraft model. For capturing the aircraft dynamics under motor failure conditions, a family of linearized models are obtained by trimming the nonlinear aircraft model at multiple equilibrium conditions and the LPV model is obtained by linking the linear models using the failed rotor speed, where the system transition from healthy to failure is modeled by a scheduling parameter calculated based on failed rotor speed caused by available motor peak power after failure. The proposed adaptive MPC is developed to optimize the system output performance, including the rigid-body aircraft velocity and altitude, by using quadratic programming optimization with reference compensation subject to a set of time-varying constraints representing the current available propeller acceleration calculated based on the motor power. Simulation study is conducted based on the developed LPV control design and original nonlinear rigid-body model, and the simulation results demonstrate that the designed adaptive MPC controller is able to recover and maintain the aircraft at desired stable condition after motor failure.

PENERAPAN WIRELESS COMUNICATION HC-05 PADA MODEL KENDALI ECOSYSTEM (EVAPORATIVE COOLER SYSTEM)

Abstract The interconnectedness of the times includes all aspects, one of which is the temperature of the earth which is getting warmer, the annual prediction of increasing by 1 ° to 1.5 ° Celsius. In addition, there are technological updates with ideas that are a daily necessity. Evaporative Cooler is a conventional air conditioner throughout the world with various types and types, DEC (Direct evaporative Cooler), for example works by converting sensible heat into latent heat, whose main circuit consists of an electric motor, swing cooler motor and 24V DC water pump, a actuator module used in the implementation of this system. The idea that came up was that not many people paid attention to the system its use. This research will apply one of the wireless communication, namely the Bluetooth HC-05 Module in the control model design using ECOSYSTEM (Evaporative Cooler System) where the input is integrated with an artificial application installed on a smartphone with Bluetooth devices in it with the android operating system so that it can communicate between systems. Focusing on the communication performance applied by the HC-05 with a 2.4GHz frequency as a receiver or sender of commands with a range of signal distances that can be given, equipped with an Arduino UNO ATMega328 microcontroller for core system controllers. Over-the-air communication model that does not require other media devices in the process of working. Measurement of the performance of the signal coverage is proven horizontally and vertically, intended because there is a difference in the capture power of the transceiver and receiver signals as well as the conditions when bluetooth is controlled, it is connected and not yet connected. In the test, bluetooth only captures two conditions when it is working, when it is still affordable, bluetooth will connect and remain connected with a delay of not more than 0.02 seconds or not connected at all when the range is getting farther.

Nonlinear Model Predictive Velocity Control of a VTOL Tiltwing UAV

This letter presents the modeling, system identification and nonlinear model predictive control (NMPC) design for longitudinal, full envelope velocity control of a small tiltwing hybrid unmanned aerial vehicle (H-UAV). A first-principles based dynamics model is derived and identified from flight data. It captures important aerodynamic effects including propeller-wing interaction and stalled airfoils, but is still simple enough for on-board online trajectory optimization. Based on this model, a high-level NMPC is formulated which optimizes throttle, tilt-rate and pitch-angle setpoints in order to track longitudinal velocity trajectories. We propose and investigate different references suitable to regularize the optimization problem, including both offline generated trims as well as preceding NMPC solutions. In simulation, we compare the NMPC with a frequently reported dynamic inversion approach for H-UAV velocity control. Finally, the NMPC is validated in flight experiments through a series of transition maneuvers, demonstrating good tracking capabilities in the full flight envelope.

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control technique is also proposed by combining the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques.

Adaptive optics for dynamic aberration compensation using parallel model-based controllers based on a field programmable gate array.

Adaptive optics (AO) is an effective technique for compensating the aberrations in optical systems and restoring their performance for various applications such as image formation, laser processing, and beam shaping. To reduce the controller complexity and extend the compensation capacity from static aberrations to dynamic disturbances, the present study proposes an AO system consisting of a self-built Shack-Hartmann wavefront sensor (SHWS), a deformable mirror (DM), and field programmable gate array (FPGA)-based controllers. This AO system is developed for tracking static and dynamic disturbances and tuning the controller parameters as required to achieve rapid compensation of the incoming wavefront. In the proposed system, the FPGA estimates the coefficients of the eight Zernike modes based on the SHWS with CameraLink operated at 200 Hz. The estimated coefficients are then processed by eight parallel independent discrete controllers to generate the voltage vectors to drive the DM to compensate the aberrations. To have the DM model for controller design, the voltage vectors are identified offline and are optimized by closed-loop controllers. Furthermore, the controller parameters are tuned dynamically in accordance with the main frequency of the aberration as determined by a fast Fourier transform (FFT) process. The experimental results show that the AO system provides a low complexity and effective means of compensating both static aberrations and dynamic disturbance up to 20 Hz.