Minimum Variance Control Strategy Research Articles

Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost.

Minimum variance control strategy for closed loop linear time invariant system

To design one feedback controller in a closed loop linear time invariant system, the idea of minimum variance control is used to realise this goal. Two explicit forms corresponding to the closed loop system are considered, i.e., its general form and rational transfer function form respectively. Firstly one closed form solution of the minimum variance controller is derived in the general form of the closed loop linear time invariant system, and an optimisation algorithm is proposed to obtain controller in practice. Secondly in the rational transfer function form of the closed loop linear time invariant system, the minimum variance controller is determined, while guaranteeing the modified variances of output and input as small as possible.

Minimum variance control strategy for closed loop linear time invariant system

Minimum variance control strategy for closed loop linear time invariant system

Application of Adaptive Minimum-variance Control Technique for Cyclic Production Machine

In this paper, we consider the control problem of cyclic production machine. There are many mechanical parts produced by cyclic production machine. For example, springs are usually made of long stock wire which is fed into cyclic forming machine. A spring forming machine can force wire into a coiled shape during the cyclic motion, but the length of the produced spring in each cycle is varying according to the internal stress of wire, mechanical uncertainty of the forming machine etc. To improve the accuracy of free length of spring produced by forming machine, we propose an adaptive minimum-variance control strategy for cyclic production system and successfully applied to commercial spring forming machine.

Robust Adaptive Soft Landing Control of an Electromagnetic Valve Actuator for Camless Engines

AbstractThis paper deals with a signal‐based robust adaptive approximation technique for a proportional derivative (PD) regulator which is applied to an electromagnetic valve actuator control for camless internal combustion engines. PD regulators generate very high spikes in the presence of unavoidable noise. These spikes cause high power dissipation and poor dynamic performance with a lack of precision. The presented method allows the reduction of the noise and not robust nonlinear uncertainties effects by using minimum variance analysis. The technique with which the PD regulator is approximated does not depend on the model of the controlled system. Hence, the technique is quite general and can be applied to any type of system. In addition, this paper describes a feasible real‐time self‐tuning of an approximated discrete PD regulator using a backward Euler technique. The main contribution of the paper is the presentation of an approximated PD controller using a minimum variance control strategy together with a weighted least squares method to adapt the parameters of this approximated controller. This control law realises a robust control technique with respect to the noise and nonlinear uncertainties. Moreover, a comparison with the approximate PD controller proposed in MATLAB by Mathworks is provided. The presented technique ensures a good dynamic performance, including low dissipation as well as accurate positioning and soft landing control.

Application of Draw Wire Sensor in the Tracking Control of An Electro Hydraulic Actuator System

A draw wire sensor is considered as a contact measurement method. It is normally used to measure the speed and position of a system. A draw wire sensor is convenient especially when a low cost solution and a small sensor dimension are desired. The objective of this paper is to describe the application of draw wire sensor in control tracking of electro hydraulic actuator system. This research started with the modelling of electro hydraulic actuator system by using system identification approach. During the data taking process, an experiment is conducted using electro hydraulic actuator test bed. A draw wire sensor is attached to the load of electro hydraulic actuator system to measure the output displacement when the system is injected with the desired input signal. Draw wire sensor is measuring the output displacement in millimeters and then the signal is converted to voltage reading regarding to the given input signal. The input and output signal is collected and is used in system identification technique to obtain the best mathematical model that can represent the electro hydraulic actuator system. Once a model is obtained, a Self Tuning Controller (STC) with Generalize Minimum Variance Control (GMVC) strategy is designed to control the tracking performance of the electro hydraulic actuator system. The designed controller is tested in simulation and experiment mode. Then, the output result from both modes is compared. The results show that the output performance from both modes are almost similar. Thus, this research had shown that a draw wire sensor has a significant role in capturing an accurate output data from electro hydraulic actuator system even with or without the controller.

SETTING CONTROLLER PARAMETERS THROUGH A MINIMUM STRATEGY WITH A WEIGHTED LEAST SQUARES METHOD

This paper presents a feasible real time self-tuning of a controller. The main contribution of this paper consists of presenting a minimum variance control strategy together with a weighted least squares method to adapt the parameters of this approximated controller. Robustness in the proposed loop control is achieved. For that the technique is quite general and can be applied to any kind of system.

Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller

It is well known that if we intend to use a minimum variance control strategy, which is designed based on a model obtained from an identification experiment, the best experiment which can be performed on the system to determine such a model (subject to output power constraints, or for some specific model structures) is to use the true minimum variance controller. This result has been derived under several circumstances, first using asymptotic (in model order) variance expressions but also more recently for ARMAX models of finite order. In this paper we re-approach this problem using a recently developed expression for the variance of parametric frequency function estimates. This allows a geometric analysis of the problem and the generalization of the aforementioned finite model order ARMAX results to general linear model structures.

Adaptive control for EDM process with a self-tuning regulator

In order to improve the performance of machines, there is a growing need to develop a highly stable servo control system for electrical discharge machining (EDM). With the perpetual push towards the untended EDM operation, an adaptive control system is and will continue to be a primary option. In this paper, a new EDM adaptive control system which directly and automatically regulates tool-down-time has been developed. Based on the real-time-estimated parameters of the EDM process model, by using minimum-variance control strategy, the process controller, a self-tuning regulator, was designed to control the machining process so that the gap states follow the specified gap state. With a properly selected specified gap states, this adaptive system improves the machining rate by, approximately, 100% and in the meantime achieves a more robust and stable machining than the normal machining without adaptive control. This adaptive control system helps to gain the expected goal of an optimal machining performance.

A kriging interpolation strategy for the optimization of Acidithiobacillus ferrooxidans biomass production using fed-batch bioreactors

In this work, a procedure for the optimization of Acidithiobacillus ferrooxidans biomass production in fed-batch reactors using a model based on optimal spatial interpolation of experimental data is proposed. The approach is useful in those cases where specific growth and substrate consumption rates are unknown. Based on interpolation, the optimal values of biomass and substrate concentrations set points are obtained at the minimum of 2-dimensional cost function. In the fed-batch reactor biomass and substrate concentrations are controlled at their set points by changing the input flow and its concentration. We propose a minimum variance control strategy which improves the classical proportional plus integral control. Since the interpolation is optimal, the proposed linear control strategy allows obtaining a tight minimized upper bound of the variance error in the controlled variables.

Robust Stable Adaptive Control of Uncertain Bilinear Plants and Its Application for Distillation Column

A robust adaptive control design is considered for a class of bilinear plants with unmodeled high-order dynamics and bounded disturbances. A basic optimal control law is first introduced by the generalized minimum variance control strategy, followed by a modification of introducing the modeling error estimate to the control law. Modified least-squares scheme with a relative dead zone is developed to form a novel robust adaptive controller. The resulting closed loop system is proven theoretically to have a zero average tracking error and robust stability. Furthermore, the simulation and an application in controlling of sensitive plate temperature of distillation column demonstrate the effectiveness of the algorithm.

GENERALIZED MINIMUM VARIANCE CONTROL WITH TIME SHIFT MULTIPLICATIONS

This paper describes a Generalized Minimum Variance Control (GMVC) strategy for time varying systems (TVS). If GMVC applies to TVS, be conscious of the time varying multiplication, such as multiplying that comprises of more than two polynomials. In TVS, the time varying multiplication includes the time shift operator. The calculation of time shift multiplication was proposed by Zhen. Li. However, GMVC must be designed for servo systems. Then, the time varying multiplication is composed of double formed time varying multiplication. This paper improves the calculation of the double formed time varying multiplication. Furthermore, the proposed GMVC is verified with the servo characteristic and the noise characteristic. The simulation compares the proposed GMVC with the conventional GMVC for TVS.

Applying the constrained minimum variance control theory on in-line viscosity control in the extrusion molding process

In this study, the constrained minimum variance (CMV) control strategy was applied on the extrusion process. An in-line viscometer was designed and used to measure the viscosity of the polymer melt between the screw end and the die. Under constant temperature, the screw speed was treated as the plant control input, and the viscosity of the polymer melt was treated as the output. The system transfer function and the disturbance dynamic model of the plant were obtained experimentally. Comparing the CMV control strategy with the open loop control method, the variance decreased 39.1% in the simulation, and it also decreased 24.3% in experimental results. This shows that the CMV control method is effective on reducing the fluctuation of viscosity in an extruder. Improved stabilization of the viscosity of the polymer melt in the barrel was thus achieved.

A Unified Control Strategy for the Active Reduction of Sound and Vibration

The generalized minimum variance (GMV) control strategy is proposed as an effective strategy for active sound and vibration control systems. The GMV strategy is shown to unify well-known adaptive filtering approaches based on LMS-type algorithms and purely feedback strategies as used in other types of control systems. The short comings of the LMS adaptive filtering systems for active sound control are clearly un covered. Pure adaptive filtering emerges from the GMV strategy merely when the extra neous noise at the error sensor is Gaussian noise, the controller complexity is restricted and no fast tracking of system changes is required. The necessary additional identification of the secondary acoustic path is an extra problem of the adaptive filtering approach. Sim ulation experiments with real and synthetic data verify the wide applicability of the GMV control strategy and the limited efficacy of (LMS) adaptive filters for use in active sound reduction systems.

Control Design Methods for Active Sound Reduction Systems

A review is given of the current adaptive filtering techniques for the active control of sound. In addition, the Generalized minimum Variance (GMV) control strategy is proposed as an important extension of the existing strategies. The adaptive filtering strategy is identified as a simplified realization of GMV control with feedforward. The limitations of the adaptive filtering strategies are discussed comparing this technique with original GMV strategies, which are shown to perform much better both in theory and in simulation experiments on measured time series. Finally. the issue of non-minimum phase processes is addressed. It is shown that the so-called method of weighted minimum variance is very attractive for use in active sound control systems

Real-time multivariable self-tuning controller using a feedforward paradigm with application to a coupled electric-drive pilot plant

A multivariable self-tuning control design based on a feedforward paradigm and the generalized minimum variance (GMV) control strategy is presented. The real-time implementation of this controller on a coupled electric-drive pilot plant is discussed. The results of experiments on the pilot plant demonstrated that good performance can be achieved using the design. >

Distributed Computer System for Adaptive Control of LD Convertor Steel Making

An adaptive algorithm for LD converter steel refining control is presented. Avoiding the traditionally used balance approach, here a real-time process parameters estimation and control is applied. A Recursive Least Squares method is used and a minimum variance control strategy is realized. The original idea is that the converter steel making process| is considered as a continuous process which is sampled and one heat time is considered as one sample time. So, the process model parameters are adjusted after each heat and the algorithm adapts very fast to the technological conditions chanson.A distributed PS/XT computer two-leves control system is realized. Successful industrial results are obtained.

Temperature Self-Tuning Control for a Heat-Vacuum Cabin

This paper introduces a successful example of temperature self-tuning control for a heat-vacuum cabin. To obtain the desired process when the temperature increases and decreases and to fulfil the steady accuracy requirement of the temperature control, a new self-tuning control algorithm is proposed which combines the minimum variance control strategy for reducing noises and the pole assignment strategy for following reference input. The self-tuning controller has been implemented on a single board microprocessor. The experiment results showed the controller has excellent properties.

The role of the interactor matrix in multivariable stochastic adaptive control

This paper is concerned with the extension of the well-known minimum variance control strategy to the multi-input-multi-output case. It is shown that this extension is straightforward when the system interactor matrix is diagonal but presents some unexpected difficulties in the general case. We develop a suitable stochastic controller for the general case as a logical extension of the single input algorithm. We also explore the properties of the algorithm in detail. We also address the question of adaptive control of multivariable stochastic systems and investigate one possible strategy for overcoming the requirement of knowing the system interactor matrix a priori.

Intersample variances in discrete minimum variance control

This note investigates the continuous-time response resulting from digital control of stochastic systems. A method is presented for computing the time-varying covariance between sampling instants. The results are used to examine the performance of the well-known minimum variance control strategy. It is shown that the continuous-time response is cyclo-stationary. This analysis confirms that these algorithms can have poor continuous-time responses especially for lightly damped systems. In view of these results, we suggest that it may be preferable to use alternative design procedures which give suboptimal performance at the sampling instants but which actually result in superior continuous-time results. The theoretical results in the note are substantiated by a computer simulation study.