Controlled Autoregressive Moving Average Research Articles

Identification of Multivariable Hammerstein CARMA System Using Special Test Signals

Abstract The multivariable Hammerstein controlled autoregressive moving average (CARMA) system contains the sum of bilinear parameter vectors in the identification model, which is difficult to be transformed into a regression form for direct identification. A two-stage identification technique is developed in this article. By using multiple sets of special test signals, this chapter achieves separable identification for the linear subsystem and the nonlinear subsystem. Then, using the property of binary signal input to nonlinear part, parameters of noise model and the linear subsystem can be calculated by recursive extended least squares (RELS) method. To implement the off-line identification, the RELS is used to identify the nonlinear subsystem parameters. Finally, simulation examples illustrate the validity of proposed approach in identifying multivariable Hammerstein CARMA system.

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

Statistical process monitoring, maintenance policy, and production have commonly been studied separately in the literature, whereas their integration can lead to more favorable conditions for the entire production system. Among all studies on integrated models, the underlying process is assumed to generate independent data. However, there are practical examples in which this assumption is violated because of the extraction of correlation patterns. Autocorrelation causes numerous false alarms when the process is in the in-control state or makes the traditional control charts react slowly to the detection of an out-of-control state. The auto-regressive moving average (ARMA) control chart is selected as an effective tool for monitoring autocorrelated data. Therefore, an integrated model subject to some constraints is proposed to determine the optimal decision variables of the ARMA control chart, economic production quantity, and maintenance policy in the presence of autocorrelated data. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to search for optimal decision variables. An industrial example and some comparisons are provided for more investigations. Moreover, sensitivity analysis is carried out to study the effects of model parameters on the solution of the economic-statistical design.

Design of a Nonlinear Predictive Controller for a Fractional-Order Hydraulic Turbine Governing System with Mechanical Time Delay

A nonlinear predictive control method for a fractional-order hydraulic turbine governing system (HTGS) with a time delay is studied in this paper. First, a fractional-order model of a time-delay hydraulic turbine governing system is presented. Second, the fractional-order hydraulic servo subsystem is transformed into a standard controlled autoregressive moving average (CARMA) model according to the Grünwald-Letnikov (G-L) definition of fractional calculus. Third, based on the delayed Takagi-Sugeno fuzzy model, the fuzzy prediction model of the integer-order part of the HTGS is given. Then, by introducing a fourth-order Runge-Kutta algorithm, the fuzzy prediction model can be easily transformed into the CARMA model. Furthermore, a nonlinear predictive controller is proposed to stabilize the time-delay HTGS. Finally, the experiment results are consistent with the theoretical analysis.

Trajectory planning and diagonal recurrent neural network vibration control of a flexible manipulator using structural light sensor

Abstract A new vibration measurement method and active control algorithm for a flexible manipulator are investigated. A non-contact vibration measurement method based on structural light sensor is proposed. During and after the point-to-point motion, the locally controlled autoregressive moving average (CARMA) model of the flexible manipulator is determined through experiments. Based on the model, the particle swarm optimization (PSO) algorithm is adopted to obtain the optimal vibration suppression trajectory. Moreover, considering the time-varying and nonlinear characteristics of the flexible manipulator, a diagonal recurrent neural network (DRNN) control algorithm is designed to suppress residual vibration, which consists of an on-line identifier and a vibration control signal generator. The experiment setup is constructed. Compared with trapezoidal trajectory and classic smooth trajectories, planning an optimal trajectory can cause less vibration under the same motion requirements. Experimental results demonstrated that the DRNN controller is superior to the classical PD controller on vibration suppression, especially for the small amplitude residual vibration. Furthermore, the hybrid control strategy of optimal trajectory planning and DRNN control has the advantages of ensuring smoother motion and faster residual vibration suppression speed.

Maximum likelihood estimation method for dual-rate Hammerstein systems

For a dual-rate sampled Hammerstein controlled autoregressive moving average (CARMA) system, this paper uses the polynomial transformation technology to obtain its dual-rate bilinear identification model which is suitable for the available dual-rate sampled-data, uses the maximum likelihood principle to construct a unified parameter vector of all parameters and an information vector formed by the derivative of the noise variable to the unified parameter vector, and directly identifies the parameters of the linear block and the nonlinear block for the dual-rate Hammerstein CARMA system. The unified parameter vector contains the minimum number of the unknown parameters, and the proposed maximum likelihood estimation algorithm has higher computational efficiency than the over-parameterization model based least squares algorithm.

Economic statistical design of ARMA control chart through a Modified Fitness-based Self-Adaptive Differential Evolution

In this paper the economic statistical design of an Auto-Regressive Moving Average (ARMA) control chart for autocorrelated data has been investigated. In particular, the total cost of the chart subject to a constraint on the minimum in-control average run length has been taken into account. Due to the autocorrelation of the data, a simulation approach was adopted to assess both in-control and out-of-control average run lengths as the charting parameters change. In order to select the optimal parameters of the ARMA chart a Modified Fitness-based Self-Adaptive Differential Evolution algorithm, named MF-SADE, has been developed. To validate the effectiveness of MF-SADE in solving the proposed control chart design problem, an extensive comparison campaign involving four different metaheuristics from the relevant literature was arranged. Once the obtained numerical results confirmed the outperformance of SADE, it was used to carry out a sensitivity analysis including parameters of the cost model and those concerned with the underlying process, denoted as u and v. Among the numerous findings, the sensitivity analysis put in evidence the relationship between u, v and the variables of the economical statistical issue under investigation.

Optimal design and experimental analyses of a new micro-vibration control payload-platform

This paper presents a new payload-platform, for precision devices, which possesses the capability of isolating the complex space micro-vibration in low frequency range below 5Hz. The novel payload-platform equipped with smart material actuators is investigated and designed through optimization strategy based on the minimum energy loss rate, for the aim of achieving high drive efficiency and reducing the effect of the magnetic circuit nonlinearity. Then, the dynamic model of the driving element is established by using the Lagrange method and the performance of the designed payload-platform is further discussed through the combination of the controlled auto regressive moving average (CARMA) model with modified generalized prediction control (MGPC) algorithm. Finally, an experimental prototype is developed and tested. The experimental results demonstrate that the payload-platform has an impressive potential of micro-vibration isolation.

Parameter estimation algorithms for multivariable Hammerstein CARMA systems

This paper considers parameter identification problems for multivariable Hammerstein controlled autoregressive moving average (CARMA) systems. We transform a multivariable Hammerstein CARMA system into a new identification model which contains a set of bilinear parameter vectors, i.e., two sets of parameter vectors. To solve the difficulty of estimating the two sets of parameter vectors, this paper constructs two different forms of the identification model in each of which the output is linear about one set of parameter vectors, and presents an extended stochastic gradient algorithm to interactively estimate the two sets of parameter vectors by using the hierarchical identification principle. The proposed hierarchical identification algorithm has a higher computational efficiency than the over-parametrization model based stochastic gradient algorithm. The simulation results indicate that the proposed algorithm is effective, and an example of modeling a photovoltaic array system with the multivariable Hammerstein model is given.

Fuzzy Generalized Predictive Control for Nonlinear Brushless Direct Current Motor

Abstract In the study, a novel fuzzy generalized predictive control (GPC) scheme is proposed for the stability control of nonlinear brushless DC motor (BLDCM). First, the fuzzy predictive model of BLDCM is presented via Takagi–Sugeno fuzzy model. Then, based on the controlled autoregressive moving average (CARMA) model transformed by Takagi–Sugeno fuzzy model of BLDCM, a new fuzzy GPC scheme for the nonlinear BLDCM is designed by combining fuzzy techniques and GPC theory, and the rigorous mathematical derivation is given. Finally, numerical simulations are implemented to verify the effectiveness and superiority of the proposed scheme. It also provides reference for other nonlinear even chaos control in actual project.

Recursive maximum likelihood identification method for a multivariable controlled autoregressive moving average system

The identification model of the multivariable controlled autoregressive moving average (CARMA)-like system contains a parameter matrix and a parameter vector, and cannot be performed by a standard least squares method. In order to solve this identification problem, we transform the CARMA-like system into |$m$| regression identification models (⁠|$m$| is the number of outputs) each of which has only a parameter vector, and use a recursive maximum likelihood identification algorithm to estimate the parameter vectors of these submodels. The proposed algorithm is simple and effective, and has high estimation accuracy.

Generalized Predictive Control for superheated steam temperature regulation in a supercritical coal-fired power plant

The design and implementation of a Generalized Predictive Control (GPC) strategy for the superheated steam temperature regulation in a supercritical (SC) coal-fired power plant is presented. A Controlled Auto-Regressive MovingAverage (CARMA) model of the plant is derived from using the experimental data to approximately predict the plant's future behavior. This model is required by the GPC algorithm to calculate the future control inputs. A new GPC controller is designed and its performance is tested through extensive simulation studies. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the GPC controller is found to be more stable. The stable steam temperature leads to more efficient plant operation and energy saving, as demonstrated by the simulation results. Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.

Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems

In the present study, single and two-stage least mean square (LMS) adaptive strategies based on fractional signal processing are developed for parameter estimation of controlled autoregressive moving average (CARMA) systems. The main idea is to use fractional LMS identification (FLMSI) and two-stage FLMSI (TS-FLMSI) algorithms for CARMA model that is decomposed into a system and noise models. The performance analyses for both proposed FLMSI and TS-FLMSI schemes are conducted based on adapting the prior known design parameters of the system and comparing the results with standard adaptive algorithms. The accuracy and convergence of the design schemes are verified and validated through the results of statistical analyses based on sufficient number of independent runs to adapt CARMA system. Comparative studies established the dominance of single and two-stage fractional adaptive algorithms over other counterpart in term of model accuracy and reliability in case of different scenarios based on variant signal to noise ratios and step size parameters.

Minimum variance control of organic Rankine cycle based waste heat recovery

Abstract In this paper, an online self-tuning generalized minimum variance (GMV) controller is proposed for a 100 KW waste heat recovery system with organic Rankine cycle (ORC). The ORC process model is formulated by the controlled autoregressive moving average (CARMA) model whose parameters are identified using the recursive least squares (RLS) algorithm with forgetting factor. The generalized minimum variance algorithm is applied to regulate ORC based waste heat recovery system. The contributions of this work are twofold: (1) the proposed control strategy is formulated under the data-driven framework, which does not need the precise mathematic model; (2) this proposed method is applied to handle tracking set-point variations and process disturbances by improved minimum objective GMV function. The performance of GMV controller is compared with the PID controller. The simulation results show that the proposed strategy can achieve satisfactory set-point tracking and disturbance rejection performance.

A Control Strategy for Continuously Adjusting Attack Angle in Wind Tunnel Experiments

In wind tunnel experiments, in order to adjust the attack angle continuously, the support mechanism movement should be steady and smooth. However, the electro-hydraulic servo system is a typical nonlinear, time-varying and uncertainty system, and the wind tunnel environment is very complicated. To address these problems, an on-line identification and generalized predictive control (GPC) strategy is proposed in this study. Firstly, the Labview and AMESim are integrated to build an electro-hydraulic system simulation model. Secondly, the controlled auto-regressive moving average (CARIMA) model of the electro-hydraulic system is developed. Thirdly, the influence on the system performance owing to the control parameters, model parameters, and external disturbance are widely discussed and deeply analyzed. At last, a test platform is constructed with the National Instruments (NI) embedded real time technology. The proposed control strategy is tested and verified on this test platform. The experimental results show that the angular velocity control precision reaches 0.01°/s. It implies that this control strategy has a good performance for nonlinear velocity control. Thus it satisfies the requirement of the continuously adjusting attack angle in wind tunnel experiments.

A Direct Control Parameters Tuning Method Using CARMA Models Based on Minimum Variance Evaluation

The paper proposes a direct control parameter tuning method which employs the variance evaluation for the performance criterion to solve the disturbance attenuation problem for the CARMA(Controlled Auto-Regressive Moving Average) models. The proposed method tunes the control parameters so that the variance of the plant output approaches the minimum variance value, which can be estimated from the output prediction using the CARMA model. The method can avoid the disturbance reference model, which is indispensable for our earlier research. The efficiency of the proposed method is demonstrated from a numerical example.

Hysteresis identification and adaptive vibration control for a smart cantilever beam by a piezoelectric actuator

This paper presents some experiments not only for depiction of hysteresis property but also for vibration suppression by designing an adaptive controller to a smart system consisted of a cantilever beam bonded with a piezoelectric actuator. The adaptive controller is composed of an identifier and the minimum variance direct control. With the identifier by employing the recursive extended least square method, an online controlled autoregressive moving average (CARMA) model for the smart beam is proposed to characterize the hysteresis phenomena between the output strain near the fixed end of the cantilever beam and the input voltage applied on the piezoelectric actuator. Based on the CARMA model, an adaptive controller is designed to control the vibration of the nonlinear smart system by using the minimum variance self-tuning direct regulator (MVSTDR). The MVSTDR has a significant advantage of trying to obtain an optimal control results for vibration suppression over the control process. The experiment results demonstrate that it is feasible to suppress vibration by the MVSTDR for the smart beam with the hysteresis property and show that the amplitude reduction quantity of the strain in the frequency spectrum analysis is up to about 83.67% with the adaptive controller at the first natural frequency when the smart beam is subjected to a free vibration.

Modeling and energy management control design for a fuel cell hybrid passenger bus

This paper presents the modeling and supervisory energy management design of a hybrid fuel cell/battery-powered passenger bus. With growing concerns about petroleum usage and greenhouse gas emissions in the transportation sector, finding alternative methods for vehicle propulsion is necessary. Proton Exchange Membrane (PEM) fuel cell systems are viable possibilities for energy converters due to their high efficiencies and zero emissions. It has been shown that the benefits of PEM fuel cell systems can be greatly improved through hybridization. In this work, the challenge of developing an on-board energy management strategy with near-optimal performance is addressed by a two-step process. First, an optimal control based on Pontryagin's Minimum Principle (PMP) is implemented to find the global optimal solution which minimizes fuel consumption, for different drive cycles, with and without grade. The optimal solutions are analyzed in order to aid in development of a practical controller suitable for on-board implementation, in the form of an Auto-Regressive Moving Average (ARMA) regulator. Simulation results show that the ARMA controller is capable of achieving fuel economy within 3% of the PMP controller while being able to limit the transient demand on the fuel cell system.

Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems

A two-stage least squares based iterative (two-stage LSI) identification algorithm is derived for controlled autoregressive moving average (CARMA) systems. The basic idea is to decompose a CARMA system into two subsystems and to identify each subsystem, respectively. Because the dimensions of the involved covariance matrices in each subsystem become small, the proposed algorithm has a high computational efficiency. The simulation results indicate that the proposed algorithm is effective.

Economic design of autoregressive moving average control chart using genetic algorithms

When designing control charts, it is usually assumed that the observations from the process at different time points are independent. However, this assumption may not be true for some production processes, e.g., the continuous chemical processes. The presence of autocorrelation in the process data can result in significant effect on the statistical performance of control charts. Jiang, Tsui, and Woodall (2000) developed a control chart, called the autoregressive moving average (ARMA) control chart, which has been shown suitable for monitoring a series of autocorrelated data. In the present paper, we develop the economic design of ARMA control chart to determine the optimal values of the test and chart parameters of the chart such that the expected total cost per hour is minimized. An illustrative example is provided and the genetic algorithm is applied to obtain the optimal solution of the economic design. A sensitivity analysis shows that the expected total cost associated with the control chart operation is positively affected by the occurrence frequency of the assignable cause, the time required to discover the assignable cause or to correct the process, and the quality cost per hour while producing in control or out of control, and is negatively influenced by the shift magnitude in process mean.

Discrete system identification and self-tuning control of dissolved oxygen concentration in a stirred reactor

This work presents the applications of discrete-time system identification and generalized minimum variance (GMV) control of dissolved oxygen (DO) level in a batch bioreactor in which Saccharomyces cerevisiae is produced at aerobic condition. Air flow rate and mixing rate were varied to determine the maximum local liquid phase volumetric mass transfer coefficient (KLa). Maximum KLa value was determined at a mixing rate of 600 rpm and air flow rate of 3.4 Lmin−1. For control purpose, manipulated variable was selected as air flow rate due to its effectiveness on the KLa. To examine the dynamic behavior of the bioreactor, various input signals were utilized as a forcing function and three different model orders were tested. A second0order controlled auto regressive moving average (CARMA) model was used as the process model in the control algorithm and in the system identification step. It is concluded that the ternary input is more suitable than the other input types used in this work for system identification. Recursive least squares method (RLS) was used to determine the model parameters. GMV control results were compared with the traditional PID control results by using performance criteria of IAE and ITAE for different types of DO set point trajectories. DO concentration in the batch bioreactor was controlled more successfully with an adaptive controller structure of GMV than the PID controller with fixed parameters.