Generalized Minimum Variance Control Research Articles

Multiagent-Based Nonlinear Generalized Minimum Variance Control for Islanded AC Microgrids

Voltage regulation, frequency restoration, and reactive/active power sharing are the crucial tasks of the microgrid's secondary control, especially in the islanding operating mode. Because sensors and communication links in a microgrid are subject to noise, it is of paramount value to design a noise-resilient secondary voltage and frequency control. This paper proposes a minimum variance control approach for the secondary control of AC microgrids that can effectively perform noise attenuation, voltage/frequency restoration, and reactive/active power sharing. To this end, the nonlinear generalized minimum variance (NGMV) control approach is introduced to the islanded microgrid's secondary control system. The effectiveness of the proposed control approach is verified by simulating two microgrid test systems in MATLAB.

High-Order Neural-Network-Based Multi-Model Nonlinear Adaptive Decoupling Control for Microclimate Environment of Plant Factory.

Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.

Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control

Closed-loop transcranial ultrasound stimulation technology is based on real-time feedback signals, and has the potential for precise regulation of neural activity. In this paper, firstly the local field potential (LFP) and electromyogram (EMG) signals of mice under different intensities of ultrasound stimulation were recorded, then the mathematical model of ultrasound intensity and mouse LFP peak/EMG mean was established offline based on the data, and the closed-loop control system of LFP peak and EMG mean based on PID neural network control algorithm was simulated and built to realize closed-loop control of LFP peak and EMG mean of mice. In addition, using the generalized minimum variance control algorithm, the closed-loop control of theta oscillation power was realized. There was no significant difference between the LFP peak, EMG mean and theta power under closed-loop ultrasound control and the given value, indicating a significant control effect on the LFP peak, EMG mean and theta power of mice. Transcranial ultrasound stimulation based on closed-loop control algorithms provides a direct tool for precise modulation of electrophysiological signals in mice.

Data-Driven Generalized Minimum Variance Control with Autoencoder based Dimensionality Reduction of Input Signals

This paper considers data-driven type generalized minimum variance control (GMVC) for p-inputs/q-outputs (p > q) multivariable systems with static nonlinearity. In the proposed approach, an autoencoder, which can extract the feature of input data, is used. First, an encoder converts input data with p dimensions into that with q dimensions. Then, a GMV controller is designed by using the dimension-reduced input data. Finally, the nonlinearity of a plant is compensated by a decoder, which reconstructs the input data with p dimensions. The effectiveness of the presented approach is evaluated using a numerical example.

A Generalized Minimum Variance Controller Based on a Modified Full-Order Observer Equivalent to Polynomial Approach

A Generalized Minimum Variance Controller Based on a Modified Full-Order Observer Equivalent to Polynomial Approach

Data‐driven generalized minimum variance regulatory control using routine operation data

AbstractThis paper provides a new generalized minimum variance (GMV) control using routine operation data. The proposed method achieves disturbance rejection without additional experiments and reference models, which is different from other data‐driven techniques. In this paper, a new data‐driven criterion is proposed for the Box and Jenkins (BJ) model, which is a more general description including the Auto‐Regressive and Moving Average eXogeneous (ARMAX) model. The paper proves that the optimization of the proposed criterion can achieve GMV control. Numerical examples for two different model structures show the validity of the proposed method. In particular, the application to datasets obtained from a continuous stirred tank reactor (CSTR) demonstrates the efficiency of the proposed method.

Parametric Robust Generalized Minimum Variance Control

This work presents a hybridization of the Generalized Minimum Variance control (GMVC) with Parametric Robust Control (RPC) for Linear Time Invariant (LTI) Uncertain Systems. A linear parametric uncertainties model is used to describe the system dynamic behavior. The controller synthesis is based on PID controller with a low-pass filter and formulated as a convex optimization problem which takes in account the desired closed-loop performance and the uncertainties on the model parameters. Then, the robust controller parameters, that represent the best solution for the all possible models and assign the closed-loop poles within the desired region in the s-plane, are transferred to GMVC by Tustin’s method, resulting in a parametric robust generalized minimum variance (PRGMV) controller, which was compared to two other approaches, carrying out several simulation essays in Matlab environment. In order to emphasize its performances and its efficiency, the performance index and sensitivity analysis were assessed. The results confirmed that the proposed controller ensures better robustness and performance for reference tracking and disturbance rejection.

Design of an Optimized GMV Controller Based on Data-Driven Approach

This paper presents a data-driven scheme that can obtain the optimized Generalized Minimum Variance (GMV) control parameters by applying the Nelder—Mead (NM) method based on Proportional-Integral Derivative (PID) controller for linear system. An adjustable λ is included in the GMV-PID controller. In the existing GMV-PID controller, the PID parameters are calculated by simply changing λ manually. Therefore, it is hard to get desirable control performance. The NM method is introduced to improve the control performance. The application of NM method can optimize the calculation of λ. However, the objective function in NM method needs the output calculation. In other words, the model information is inevitably to obtain output. To achieve model-free design scheme, the estimation of closed-loop response method is introduced in database-driven approach. Using obtained closed-loop data can predict the output and then substitute it into the objective function without any model information of the process. The effectiveness is verified by experiment.

Design of nonlinear predictive generalized minimum variance control for performance monitoring of nonlinear control systems

In this paper, a nonlinear predictive generalized minimum variance (NPGMV) controller is proposed and explicitly formulated for a class of nonlinear systems modeled by autoregressive second-order Volterra series, applying the polynomial approach. Hence, a new benchmark controller for performance assessment is introduced to improve the achievable control performance. Furthermore, to have an efficient control assessment, a data-driven algorithm based on the NPGMV control is presented that uses only the closed-loop operating data. In the design procedure, a multi-step cost function is defined to incorporate predictive action. Exploiting the predictive control concept enables the control scheme to handle constrained problems. Also, the proposed control algorithm utilizes an inherent integrating effect, which is essential for practical purposes. Volterra series are employed for modeling and identification of the nonlinear processes, using conventional least-squares methods. To show the effectiveness of the proposed methodology, simulation results and comparison studies are provided on a cascade Wiener model and a continuous stirred tank reactor (CSTR) chemical pilot plant. Finally, an experimental study on a pressure pilot plant is used to demonstrate the applicability of the proposed control scheme. The simulation and experimental results indicate satisfactory performance of the proposed controller.

Dynamic data reconciliation to improve the result of controller performance assessment based on GMVC

Due to the complexity of the industrial working environment, controllers are susceptible to various disturbance signals, resulting in unsatisfactory control performance. Therefore, it is especially important to assess the controller performance. Considering the harmful effect of measurement noise on controller performance assessment (CPA) based on generalized minimum variance control (GMVC), this paper proposes dynamic data reconciliation (DDR) to improve the accuracy of CPA based on GMVC. The paper first introduces CPA based on GMVC, and then analyzes the influence of measurement noise on GMVC based CPA index. DDR combined with GMVC based CPA is then proposed and analyzed in both SISO and MIMO systems to weaken the impact of measurement noise on CPA index. For both Gaussian distributed noise and non-Gaussian distributed noise, the formulation of DDR is derived from the Bayesian formula and maximum likelihood estimate. The effectiveness of the proposed method is verified in different case studies (involving both SISO and MIMO systems), and further verified by the control process of DC–AC converter. The simulation and experiment results demonstrate that the results of CPA based on GMVC can be obviously improved by using DDR.

Design and real-time implementation of a wireless autopilot using multivariable predictive generalized minimum variance control in the state-space

The contribution of this work is the numerical simulation and the experimental assessment of a network distributed control system using an unmanned aerial vehicle and a remote master controller connected by a wireless network. A novel multi-input multi-output long-range predictive horizon minimum variance control approach is developed and implemented in altitude, heading, lateral and longitudinal velocities control problems under the influence of process noise, measurement noise and time-delay. The proposed autopilots exhibited damped and fast response speeds with optimal control and output variance minimization, outperforming a classic model predictive control approach in convincing experimental field tests in real-world environments and scenarios.

Resilient Nonlinear Control for Attacked Cyber-Physical Systems

In this paper, the problem of resilient nonlinear control for cyber-physical systems (CPSs) over attacked networks is studied. The motivation for this paper comes from growing applications that demand the secure control of CPSs in industry 4.0. The nonlinear physical system considered can be attacked by changing the temporal characteristics of the network, causing fixed time or time-varying delays and changing the orders of received packets. The systems under attack can be destabilized if the controller is not designed to be robust with an adversarial attack. In order to cope with nonlinearity of the physical system, a nonlinear generalized minimum variance controller and a modified Kalman estimator are derived. A worst-case controller is presented for fixed-time delay. In the situations of time-varying delays and out-of-order transmissions, an opportunistic estimator and a resilient controller are designed through an on-line algorithm in the sense that it is calculated by using the information in the received packets immediately. The ability to use the received information immediately leads to the improvement of the controller’s performance. Simulation results are provided to show the applicability and performance of control law developed.

Asymptotic Tracking for an Osprey Fixed-Wing Aircraft via Nonlinear Generalized Minimum Variance Control

AbstractA nonlinear generalized minimum variance (NGMV) optimal controller is presented in this paper, which achieves tracking control of an Osprey fixed-wing aircraft with parameter uncertainties ...

Stochastic Control for DC–DC Power Converters: A Generalized Minimum Variance Control Approach

This paper proposes a Generalized Minimum Variance (GMV) controller to regulate the output of the DC-DC power converters in order to decrease the impact of the noise in the system performance. The Dual Active Bridge and the Buck converters are used to illustrate the proposed methodology. The proposed controller is designed using the stochastic augmentation methodology that provides a Proportional-Integral-Derivative (PID) controller, in the Reference Signal Tracking (RST) structure, the ability to adequately treat noise of a stochastic nature. The GMV control reduces the variance of the control signal and in turn leads to a better output characteristic. The simulation results show that the GMV controller achieve better performance in the sense of minimum variance and energy consumption in comparison with a robust PID controller.

Design of Servo Type Single Input Multi Outputs Generalized Minimum Variance Control Including Two Opposite Reference Signal

Design of Servo Type Single Input Multi Outputs Generalized Minimum Variance Control Including Two Opposite Reference Signal

Enhancing performance of generalized minimum variance control via dynamic data reconciliation

The design, tuning, and implementation of controllers are crucial for the solutions to control problems. Generalized minimum variance control (GMVC) has attractive properties and it is widely used for controller performance enhancement. The measured signals of process output variables, which are used as feedback signals, are generally subject to measurement noise. However, the GMVC theory assumes the feedback signals are the process outputs, which rarely consider the unavoidable measurement noise. By additionally considering the measurement noise, the control performance of GMVC with the measurement noise is analyzed in this paper. The dynamic data reconciliation (DDR) method, which uses the information of both the process model and the measurement data to reconcile the measured signals, is introduced. It is combined with GMVC to reduce the effect of the measurement noise on the results of GMVC. The effectiveness of GMVC combined with DDR is illustrated in two case studies, where the proposed method is compared with the original GMVC and the GMVC with the conventional digital filter. The results in both SISO and MIMO control systems show that the proposed GMVC combined with DDR can reduce the effect of the measurement noise and achieve better control performance.

An efficient dark fermentative hydrogen production by GMV control of pH

This study proposes that the on-line pH control via a model-based adaptive controller markedly improves the dark fermentative hydrogen production. According to the dynamic behavior of the dark fermentation process, pH, which rapidly declines with the beginning of the biogas production, should be precisely controlled around its optimal value in a narrow range. The success of on-line pH control was guaranteed by performing the preliminary simulation studies by experimental data obtained from dynamic analysis to determine ARMAX model order with Recursive Least Squares parameter estimation method and then to control the pH with Generalized Minimum Variance (GMV) controller. On-line control of pH at the optimal value of 6.0 during the 25 h dark fermentation process resulted in 5.4 times higher biogas production, 6.2 times higher biogas production potential, nearly doubled the duration of fermentation, and 18.4% biogas production rate increment in comparison with the uncontrolled pH case.

A Comparison of the Control Schemes of Human Response to a Dynamic Virtual 3D Face

This paper introduces the application of predictor-based control principles for the control of human response to a virtual 3D face. A dynamic woman 3D face is observed in virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimulus are observed using EEG-based excitement signals – output signal. The technique of dynamic systems identification which ensure stability and possible higher gain of the model for building a predictive input-output model of control plant is applied. Three predictor-based control schemes with a minimum variance or a generalized minimum variance control quality and constrained control signal magnitude and change rate are developed. High prediction accuracies and control quality are demonstrated by modelling results.

Design of a Data-Oriented Performance Driven Control System Based on the Generalized Minimum Variance Control Law

In process industries, it is necessary to maintain the user-specified control performance in order to achieve desired productivity. This paper describes a design scheme for control performance eval...

Stabilization of artificial gas-lift process using nonlinear predictive generalized minimum variance control

Artificial gas-lift (AGL) is one of the most widely used methods in oil production to maintain acceptable oil flow to the processing equipment and sales when the reservoir pressure is not high enough. In spite of its popularity, the AGL process is prone to casing-heading instability, which is revealed as significant flow oscillation. This is undesirable as it results in production losses and unstable behavior that has negative impact on the downstream equipment. Controller design for such a process is very challenging as it exhibits highly nonlinear dynamics. In this work, the predictive generalized minimum variance control (NPGMV) is employed to derive a robust controller based on the state estimation to stabilize AGL process when casing-heading phenomenon occurs. A closed-form optimal control law is obtained based on the Taylor series approximation. Further, a nonlinear state observer is produced and combined with the controller to ensure closed-loop control through variables that are most beneficial to the system performance, which are unmeasurable and can be obtained only via estimation. Through simulation studies, the effectiveness of the proposed controller is demonstrated.