System Output Variables Research Articles

Decentralized control synthesis for bilinear systems using orthogonal functions

Abstract In this paper, we propose the development of a new technique of decentralized control for bilinear systems, through using orthogonal functions. The use of this tool allows the conversion of differential state equations to a set of algebraic ones by projecting the system input and output variables into orthogonal functions basis and then using the operational properties of these orthogonal functions. The optimum decentralized control parameters for the interconnected bilinear system can then be determined through comparison of the response behaviour with the chosen reference model.

IDENTIFICATION OF NONLINEAR CONTINUOUS DYNAMIC SYSTEMS WITH CLOSED CYCLE

Structural and parametric identification of nonlinear continuous dynamic systems with a closed cycle on a set of continuous block-oriented models with feedback is considered. The method of structural identification in the steady state based on the observation of the system's input and output variables at the input periodic influences is proposed. The solution of the parameter identification problems, which can be immediately connected with the structural identification problem, is carried out in the steady and transient states by the method of least squares. The structural and parametric identification algorithms are investigated by means of both theoretical analysis and computer modeling.

Quantitative Indexes of System Identifiability: An Approach Based on Consistent Measures of Dependence

The paper is concerned with the problem of quantitative characterizing the identifiability of systems. Such a characterization is based on applying dependence properties of two random values, one of which represents a component of the system input variable, the second one represents a component of the system output variable. In turn, the dependence properties are described by a measure of stochastic dependence. A measure of heterogeneity of the impact of input variables of a system into the output one, based on measures of dependence meeting the Rényi's axioms, is proposed. Applying the Cauchy-Schwarz divergence, an information theoretic measure of dependence meeting “almost all” Rényi's axioms is also proposed. The paper has been supported by a grant of the Russian Foundation for Basic Researches (RFBR): project 12-08-01205-a.

Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems

Most system identification approaches and statistical inference methods rely on the availability of the analytic knowledge of the probability distribution function of the system output variables. In the case of dynamic systems modelled by hidden Markov chains or stochastic nonlinear state-space models, these distributions as well as that of the state variables themselves, can be unknown or untractable. In that situation, the usual particle Monte Carlo filters for system identification or likelihood-based inference and model selection methods have to rely, whenever possible, on some hazardous approximations and are often at risk. This review shows how a recent nonparametric particle filtering approach can be efficiently used in that context, not only for consistent filtering of these systems but also to restore these statistical inference methods, allowing, for example, consistent particle estimation of Bayes factors or the generalisation of model parameter change detection sequential tests. Real-life applications of these particle approaches to a microbiological growth model are proposed as illustrations.

Comparative investigation on the dynamic behaviours of SRD operated under PI control strategies

In this paper, by making use of Fourier series, mathematic description of a four-phase switched reluctance drive (SRD) system with proportional-integral (PI) control is presented based on the equivalent non-linear inductance function. The PI control strategy is carried out under digital and analogous modes, respectively. After addressing the waveforms of system output variables, the bifurcation characteristics of digitally controlled SRD are discussed comparatively with its analogously controlled counterpart to highlight the differences between digital and analogous control strategies. Lastly, the experimental results are provided to further verify the correctness of the theoretical analysis.

Research on Kinematics of High Speed Linear Feed System

The feed unit is modeled with Solidworks entity, then the combined mechatronic simulation is studied with MATLAB and ADAMS, In this paper，control of information exchange was completed by defining the system input and output variables, it build a mechatronic integrated simulation platform of high speed linear feed system of type GD-3. The acceleration, velocity and displacement curve of linear feed system are simulated and compared with experimental results on optimization of parameters. The results indicate that the mechatronic integrated simulation of virtual prototyping can better analyze and evaluate the velocity and displacement Kinematics characteristics of high-speed linear feed system, the acceleration different between the simulation and experimental is 10.71 percent and the virtual prototyping simulation is verified to be a new approach for analysis of the kinematics characteristics of the high-speed linear feed system.

Identification of One Class Nonlinear Systems with Closed Cycle

AbstractStructural and parametric identification of the nonlinear systems with a closed cycle on the set of continuous block-oriented models with feedback is considered. The method of the structural identification in the steady state based on the observation of the system's input and output variables at the input periodic influences is proposed. The solution of the parameter identification problems, which can be immediately connected with the structural identification problem, is carried out in the steady and transient states by the method of least squares. The structural and parametric identification algorithms are investigated by means of both the theoretical analysis and the computer modeling.

Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.

Car assembly line fault diagnosis based on triangular fuzzy support vector classifier machine and particle swarm optimization

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory and v-support vector classifier machine, the triangular fuzzy v-support vector regression machine (TF v-SVCM) is proposed. To seek the optimal parameters of TF v-SVCM, particle swarm optimization (PSO) is also applied to optimize parameters of TF v-SVCM. A diagnosing method based on TF v-SVCM and PSO are put forward. The results of the application in fault system diagnosis confirm the feasibility and the validity of the diagnosing method. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on TF v-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than standard v-SVCM.

Optimality of CUSUM Rule Approximations in Change-Point Detection Problems: Application to Nonlinear State–Space Systems

The well-known cumulative sum (CUSUM) sequential rule for abrupt model change detection in stochastic dynamic systems relies on the knowledge of the probability density functions of the system output variables conditional on their past values and on the system functioning mode at each time step. This paper shows how to build an asymptotically optimal detection rule under the common average run length (ARL) constraint when these densities are not available but can be consistently estimated. This is the case for nonlinear state-space systems observed through output variables: for such systems, a new class of particle filters based on convolution kernels allows to get consistent estimates of the conditional densities, leading to an optimal CUSUM-like filter detection rule (FDR).

Distributed Structures, Sequential Optimization, and Quantization for Detection

In the design of distributed quantization systems, one inevitably confronts two types of constraints - those imposed by a distributed system's structure and those imposed by how the distributed system is optimized. Structural constraints are inherent properties of any distributed quantization system and are normally summarized by functional relationships defining the inputs and outputs of their component quantizers. The use of suboptimal optimization methods are often necessitated by the computational complexity encountered in distributed problems. This correspondence briefly explores the impact and interplay of these two types of constraints in the context of distributed quantization for detection. We introduce two structures that exploit interquantizer communications and that represent extremes in terms of their structural constraints. We then develop a sequential optimization scheme that maximizes the Kullback-Leibler divergence, takes advantage of statistical dependencies in the distributed system's output variables, and leads to simple parameterizations of the component quantization rules. We present an illustrative example from which we draw insights into how these constraints influence the quantization boundaries and affect performance relative to lower and upper bounds.

Ti6Al4V laser alloying process monitoring by using adaptive neural network controller

Since the surface alloying process has multi-input and multi-output, non-linear coupling and time varying dynamic characteristics, it is very difficult to establish an accurate process model for designing model-based controller. Hence an adaptive neural network controller is developed in this paper to tackle the variation and disturbance of the laser alloying control system. The proposed control strategy is based on a neural network structure combined with sliding-mode control scheme. An adaptive rule is derived based on the reaching condition of a specified sliding surface to on-line adjust the weights of radial basis functions neural network (RBFNN). It has on-line learning ability in response to the system's non-linear and time-varying behaviors. Two adaptive neural network controllers are designed for regulating the laser power control and traverse speed, respectively, to achieve the desired temperature distribution on the alloying surface. The control performance is evaluated based on numerical simulation results. Different disturbances are introduced into the system output variables to investigate the robustness of the proposed controller with respect to practical process variation.

An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework

In this study an expert system of a crude oil distillation column is designed to predict the unknown values of required product flow and temperature in required input feed characteristics. The system is also capable to optimize the distillation process with minimizing the model output error and maximizing the required oil production rate with respect to control parameter values. The designed expert system uses the practical data of an operating refinery located in Abadan. The input operating variables of the column were operational parameters of crude oil such as flow and temperature, while the system output variables were defined as oil product qualities. We can make the knowledge database of these input–output values of plant with the aid of a neural networks model (NNM) to organize and collect all data related to this process and also to predict the unknown output values of required inputs. In addition we have made the ability of system’s optimization with the use of genetic algorithm (GA) with the aim of error minimizing of expert system’s output and also maximizing the required product rate with respect to its industrial importance. The built expert system can be used by operators and engineers to calculate and get some unknown data for operational values of this distillation column.

System Identification of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics

Homogeneous Charge Compression Ignition (HCCI) combustion lacks direct ignition timing control, instead the auto ignition depends on the operating condition. Since auto ignition of a homogeneous mixture is very sensitive to operating condition a fast combustion timing control is necessary for reliable operation, the ignition timing control design requiring appropriate models and system output variables for its feedback design. This paper demonstrates the use of system modeling and identification as a means to find models relevant to the engine control. The identification methods used were various subspace-based methods. An LQG controller was designed based on the estimated models and tested on a six-cylinder heavy duty engine running in HCCI operation.

Expert system of a crude oil distillation unit for process optimization using neural networks

An expert system of crude oil distillation unit (CDU) was developed to carry out the process optimization on maximizing oil production rate under the required oil product qualities. The expert system was established using the expertise of a practical CDU operating system provided by a group of experienced engineers. The input operating variables of the CDU system were properties of crude oil and manipulated variables; while the system output variables were defined as oil product qualities. The knowledge database of the CDU operating model can be built using the input–output data with an approach of artificial neural networks (ANN). The built ANN model can be applied on predicting the oil product qualities with respect to the system input variables. In addition, a design of experiment was implemented to analyze the effect of the system input variables on the oil product qualities. Optimal operating conditions were then found using the knowledge database with an optimization method according to a defined objective function. The built expert system can provide on-line optimal operating information of the CDU process to the operators corresponding to the change of crude oil properties.

Application of fuzzy control to industrial bioprocesses in Japan

Applications of fuzzy control to industrial biological processes in Japan were summarized, compared and discussed in terms of the system features, control purpose, input and output variables, development of fuzzy rules and its effectiveness. Fuzzy control was mainly applied to the fed-batch cultures of microorganisms and used for the on-line control of feeding rate of substrate. Most of the control systems were developed based on the knowledge of experienced. Fuzzy control is regarded as a promising method for automating the bioprocesses where the experienced operators play significant roles for their successful operation.

Elementwise stablewise tracking with finite reachability time: Linear time-invariant continuous-time MIMO systems

Three new qualitative control concepts are introduced and developed in the framework of general MIMO linear, time-invariant, continuous time systems to be controlled: natural trackability, elementwise and stablewise tracking, and natural tracking control. The study of these concepts opens new problems in control science, which are solved in the form of their necessary and sufficient conditions. Different physical natures and/or purposes of the system output variables, as well as their mutual independence, may require that each output be assigned a specific settling time and/or a reachability time. Such times compose corresponding vector times. The natural tracking control ensures elementwise output tracking which obeys a prespecified vector settling time and/or vector reachability time. A controller should use information only about the system output error and the realized control itself in order for the control to be natural tracking control. But the controller does not use any information about the system internal dynamics description, system state and external disturbances. The necessary and sufficient conditions are proved for the stability of systems that exhibit tracking. They permit possible independent and separate synthesis of a stabilizing control and of a tracking control. A natural tracking control synthesis and natural tracking controller design for a fourth-order MIMO system and simulation results illustrate and verify the new control theory avenues.

Identification of Nonlinear Systems with Missing Data by the EM Algorithm

In this paper, identification of the joint probability dentisity function (PDF) from missing data is considered. The model of PDF is Gaussian mixture. It is well known that the expectation-maximization (EM) algorithm is useful for the identification of Gaussian mixture. Here it is extended to the case of missing elements of the observations. It will be shown that, after identifying the PDF model, it is easy to estimate the missing elements as well as the system output variable.

A Recursive Method for Transfer-Function Matrix Identification in MIMO System

A general algorithm is presented for the identification of the parameters in the transfer-function matrix of a multi-input-multi-output (MIMO) system. The approach adopted is that of expanding the system input and output variables in Laguerre polynomial series. The feasibility of the method lies in the generation of linear algebraic equations in the unknown parameters and initial conditions by means of an elegant operational matrix which relates Laguerre polynomials to their integrals. An example is included to illustrate the applicability of the proposed method.

A DESIGN PROCEDURE FOR PI OUTPUT-FEEDBACK MODAL CONTROLLERS

In this paper the problem of pole-assignment in linear time invariant multi-variable systems is generalized to include the case of systems incorporating integral control action. In some control system design poroblems, integral feedback is introduced in conjunction with proportional one in order to ele-minate steady-state errors arising from sustained disturbances. Most published work deals with the introduction of integral as well as pro-portional feedback of some or all of the state variables of the system under control. This paper establishes a new design procedure of proportional plus-integral controllers (PI-controllers) based on the feedback of system output variables. The controllability of the compensated system is investigated and a necessary and sufficient condition for the augmented system to be controllable is deri-ved. The existance problem of such type of modal-controllers is discussed and a necessary condition for complete pole-assignment of the closed-loop system is derived. A pratical design algroithm of this type of controllers is developed, and it is used to design a PI-controller for a VSTOL aircraft.