System Output Response Research Articles

Learning control of process systems with hard input constraints

Abstract In this paper, a novel and simple learning control strategy based on using a bounded nonlinear controller for process systems with hard input constraints is proposed. To enable the bounded nonlinear controller to learn to control a changing plant by merely observing the process output errors, a simple learning algorithm for parameter updating is derived based on the Lyapunov stability theorem. The learning scheme is easy to implement, and does not require any a priori process knowledge except the system output response direction. For demonstrating the effectiveness and applicability of the learning control strategy, the control of a once-through boiler, as well as an open-loop unstable continuously stirred tank reactor (CSTR), were investigated. Furthermore, extensive comparisons of the proposed scheme with the conventional PI controller and with some existing model-free intelligent controllers were also performed. Due to significant features of simple structure, efficient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligent control of process systems subject to hard input constraints.

Multicriteria fuzzy control using evolutionary programming

In this paper, we propose a multicriteria fuzzy control (MFC) using evolutionary programming (EP) by which its λ-fuzzy measure is optimized. Basically the MFC is an extended version of the existing fuzzy logic controller to obtain the proper weights of the fuzzy rules for the fine tuning control. The MFC is designed by three steps: first, the attributes of the system output response are selected, second, the partial evaluations from the viewpoints of the attributes are assigned to the fuzzy rules, and third, the fuzzy integral is employed to aggregate the partial evaluation and the user's fuzzy measure about the consideration degree of each attribute for the proper weight value. EP is used to optimize the λ-fuzzy measure for the linear tuning property of the MFC. Computer simulations indicate that the proposed scheme has excellent performance.

Comparison of traditional and neural network approaches to stochastic nonlinear system identification

A comparison between neural network and traditional approaches to nonlinear system identification is investigated with respect to aspects of model performance. Two neural network models, the state space and input-output model structures, are considered. A global recurrent RMLP and a teacher forcing RMLP are categorized as the state space models, and a global feedback FMLP and a teacher forcing FMLP are considered as the input-output models. In the traditional methods an AutoRegressive eXogeneous (ARX) Input model and a Nonlinear AutoRegressive eXogeneous (NARX) Input model are considered. Basic algorithms of models are described, and simulation results are also presented through the system output response. Performance of models is compared based on the Mean-Square-Errors (MSE). Noise-added sinusoidal, pulse and step signals are chosen as the test inputs for the validation of the obtained models. Two different noise levels are augmented to the chosen input signals.

Multicriteria Fuzzy Control

In this article, we propose a multicriteria fuzzy control MFC based on fuzzy measures and fuzzy integrals. The basic idea underlying this approach is based on analyzing the source of attributes of the system output response and applying the fuzzy measure and integral theory to the existing fuzzy control. With this scheme, we can tune the three attributes of rise time, overshoot, and settling time of the output response. We demonstrate that MFC has excellent control performance compared to conventional fuzzy control by computer simulations as well as via experiments performed on a DC servomotor angular position control. Moreover, our scheme can be easily implemented in practice simply by adding weight terms to existing fuzzy rules and assigning the weight to each rule by employing the fuzzy measure and integral.

Servo-drive systems for milling machine retrofit application

The objective of this paper is to describe how to retrofit a traditional milling machine, which has the hand wheel-manipulated ladder lead screw transmission system, with a d.c. servo-drive system. In this research, a d.c. servomotor is used as the actuator instead of the hand wheel to operate a milling machine table. A PC/AT is used to handle the data acquisition and the control signal calculation for various types of motion condition. The control laws are designed and written as C language software programs. In order to reduce the cost of the approach given in this study, the new type of low-cost PWM motor driver, and position and velocity feedback circuits are all designed and implemented. Additionally, a classical PID control method with relay adjustment is used to design the controller for this converted system in order to verify the approach availability. Finally, the system output response and the controller performance are investigated based upon the experimental data of this milling machine system operating in different specified motion situations.

Neural ensembles: Simultaneous recognition of multiple 2-D visual objects

Presented is a hierarchical neural network that using some highly simplified ideas from the neurophysiology of the visual cortex can simultaneously recognise multiple objects within the same field of view without recourse to feedback or a competitive learning type algorithm. It employs ensembles of neurons that within each layer are isolated from one another and can adapt itself either with or without supervision, requiring only one forward pass for learning or recognition. The adaption algorithm for each ensemble or neuron is purely local and is independent from the system's output response. Experiments verified the network's ability for simultaneous multiple-object recognition and also gave an indication of its performance with noisy images. A method of automatic neuron threshold dropoff was found to give increased performance in the recognition of noisy images.

Static response of sparsely probabilistic systems

A formal matrix inversion procedure is developed which enables the evaluation of the static response of mechanical and structural systems composed of sparsely nondeterministic component parts. The methodology provides the determination of the mean, standard deviation as well as the various higher order statistical moments of the system output response, namely of the deflections, forces, stresses, strain, etc. This includes the capability to handle any form of statistical component variation. Due to the generality of the formalism, solution conditioning can be established via a priori qualitative and quantitative convergence criteria. Because of the development of efficient recursive algorithms, the computational effort associated with the calculation of the various statistical moments is ordered by the inverse of the traditional system stiffness martrix. In this context, the scheme can handle large scale lumped, finite element and difference type simulations. To demonstrate the methodology, the results of several benchmark problems are presented.

A functional prediction method on the response probability for linear sound systems on energy scale based on the stochastic information and its application to room acoustics.

As is well-known, the conventional standard identification methods for stochastic systems are usually employed under the optimum selection of the system order and system parameters, by use of the linear correlation technique and the usual least squares error method. In this paper, a new trial of finding some object-oriented system identification method matched to the prediction of output response probability distribution has been proposed in the functional and hierarchical forms. More explicitly, the indoor sound system on an energy scale is firstly described by a linear time series model supported by the well-known Sabine's equation or Statistical Energy Analysis method. Then, a generalized series expansion type probability density expression matched to an energy state variable has been proposed for the system output response in the form matched to the variety of fluctuation form, under the introduction of statistical information reflected hierarchically in expansion coefficients. Furthermore, instead of the usual system order and system parameters in the time series type system model, several kinds of functionally introduced system orders and system parameters have been estimated hierarchically in the matched form to the above hierarchical structure in the probability density expression. Finally, the validity and the effectiveness of the proposed theory have been experimentally confirmed too by applying it to the actual data observed in a reverberation room.

Sensitivity analysis of a batch polymerization reactor

AbstractThe sensitivity of model output variables for a batch polymerization reactor to uncertainties in the kinetic parameters and initial conditions is studied. Differential equations that describe the time variation of sensitivity coefficients for the batch reactor are derived. Numerical integration of the sensitivity equations reveals that the system output responses are very sensitive to parameter variations especially when the polymerization exhibits an autoacceleration of the reaction rate.

Pole determinations with complex-zero inputs

A method for the determination of the characteristic pole locations in simple systems and components is discussed. The techniques introduced are extended to include more complex systems. The method consists of applying a signal to the system from a "complex-zero-generator" and then observing the system output response on an oscillograph. The system pole locations are determined when the system output goes through a null, which occurs when the zeros generated by the "complex-zero-generator" cancel the system poles. Tests on experimental systems indicate that the pole locations can be determined accurately and rapidly for a wide variety of systems. For testing purposes, a "complex-zero-generator" was built using two square-wave generators with a timed delay between them. This generator gives a signal which can contain complex zeros anywhere in the s-plane. This signal is then applied to a number of systems containing several different relative pole orientations. The system outputs, as photographed on the oscillograph, are studied to develop rules and procedures to determine the pole locations. The rules are then applied to a number of "unknown" systems in order to determine their applicability and a study is made of the errors and limitations involved. The results indicate that the method described is readily applicable to many systems and that, in many instances, the pole locations are determined more accurately and more rapidly than can be done through the use of a steady-state frequency analysis.