Pseudo Random Binary Sequence Signal Research Articles

A Proposal and Analytical Investigation of an All-Optical T-type Flip-Flop Using Semiconductor Optical Amplifier Mach–Zehnder Interferometer Configuration for Differential Phase-Shift Keying Encoding Operation

An all-optical differential phase-shift keying (DPSK) regenerator with a DPSK demodulator and a conventional on–off keying (OOK) regenerator requires a DPSK encoding process that is realized by T-type flip-flop (T-FF) operation. To solve this problem, we propose an all-optical T-FF circuit consisting of counter-faced semiconductor optical amplifier (SOA)-based exclusive OR (XOR) gates for the first time. Numerical investigation with rate equation reveals the possibility of its stable operation in 10 Gbps with 27-1 pseudorandom binary sequence (PRBS) signal. It also reveals that it is necessary to decrease the equivalent turn-off time for high bit-rate operation using push–pull configuration.

Identification of excitation system transfer functions in hydro power plants using a binarypseudo random signal

In several countries, field tests for excitation system model verification have become mandatory. Therefore on-site transfer function identification of hydro units are compulsory. Using excitation system implemented Pseudo-Random Binary Sequence signal instead of a solid state spectrum analyser is a commercial solution. A correct transfer function of a generating unit is of high importance for modelling the power system, since it helps to understand behaviour, predict failures and improve stability and dynamic performance. Especially on hydro units these tests can verify tuning quality of excitation systems and power system stabilisers.

Neural network-based modelling of a two-degrees-of-freedom twin rotor multiple input, multiple output system using conjugate gradient learning algorithms

This paper presents a neural network (NN)-based non-linear dynamic modelling approach for a twin rotor multiple input, multiple output system (TRMS), in terms of its two-degrees-of-freedom dynamics. The TRMS is a highly non-linear system with significant cross-coupling between its horizontal and vertical axes. It is perceived as an aerodynamic test rig, representing the control challenges of modern air vehicles. Accurate dynamic modelling is a prerequisite to address such challenges satisfactorily. A feedforward NN has been trained using the Powell—Beale version of conjugate gradient and scaled conjugate gradient learning algorithms. The data for training comprises sine and square waves with various frequencies and amplitudes, pseudo random binary sequence (PRBS), and composite PRBS signals with different amplitudes. The trained NN-based models have been tested with a set of data that are different from those used for training purposes. For more validation, the power spectral density of the model is compared with that of the real TRMS and also the correlation validations of the test results are presented in order to show the effectiveness of the proposed model. The results show that the developed model can adequately represent the highly non-linear features of the system and can be used for sophisticated controller development.

All-optical format conversion using a periodically poled lithium niobate waveguide and a reflective semiconductor optical amplifier

In the present letter, the authors report on the realization of all-optical format conversion by using the cascaded sum- and difference-frequency generation in a periodically poled lithium niobate waveguide and the active mode locking in a reflective-semiconductor-optical-amplifier-based fiber ring laser. Tunable format conversions from nonreturn-to-zero pseudorandom binary sequence (PRBS) signal to return-to-zero PRBS idler at 10 and 20Gbit∕s are observed in the experiment.

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.

Active Vibration Control of Flexible Structures Using Genetic Optimisation

This paper presents an investigation into the development of an adaptive control mechanism for vibration suppression of flexible beam structures using genetic algorithms (GAs). The global search technique of GA is used to estimate the controller transfer function, based on one-step-ahead prediction. An active vibration control (AVC) system is designed utilizing a single-input single-output (SISO) control structure to yield optimum cancellation of broadband vibration at an observation point along the beam. The mean-squared error of observed deflection is adopted as the fitness function and randomly selected controller parameters are optimised for different, arbitrarily chosen order to fit to the system by applying the working mechanism of GA. The global search technique of the GA is utilised to obtain the best parameters among all attempted orders for the controller. The GA-AVC algorithm thus developed is implemented within the simulation environment of a flexible beam structure and simulation results are presented to assess the performance of the system, with different types of excitation namely pseudo-random binary sequence, uniform random and finite duration step signals. It is noted that, the system performance is satisfactory and significant amount of vibration reduction over a broad range of frequencies of the input signal is achieved.

Non-linear generalised predictive control of a jacketed well mixed tank as applied to a batch process—A polymerisation reaction

The purpose of this study is to control the temperature of a well mixed jacketed tank under set-point changes with the use of non-linear generalised predictive control (NLGPC) system. Firstly, perturbations were given to the cooling flow rates so as to investigate the dynamic behaviour of the process. The emphasis was put on the use of polynomial non-linear autoregressive moving average with external input (NARIMAX) model related with tank temperature and heat input for non-linear control. The second part of the study deals with the presentation of identification algorithm for the construction of polynomial NARIMAX and autoregressive moving average with external input (ARIMAX) models. A pseudo random binary sequence (PRBS) signal was utilised as a forcing function in order to determine the parameters of the models. Levenberg–Marquardt algorithm was applied in the estimation of relevant parameters of NARIMAX model. Similar studies were performed for the ARIMAX model using Bierman algorithm. Next linear and non-linear models with generalised predictive control (GPC) algorithm were applied to test the performance of the available control in response to disturbances. Finally the results from NLGPC were compared with linear generalised predictive control (LGPC) to see the efficiency of the batch control system.

Integrated-optic encoder/decoder for time-spreading/wavelength-hopping optical CDMA

We demonstrate an integrated-optic encoder/decoder for time-spreading/wavelength-hopping optical code division multiple access. It is composed of a wavelength multi/demultiplexer and variable delay lines fabricated by using silica-based planar lightwave circuit technology. We evaluated the device characteristics, including those of the key components and the encoding/decoding operation, and confirmed its flexible code assignment ability and good auto/cross correlations. We then tested the performance of the encoder/decoder by undertaking bit error rate measurements with 10-Gb/s pseudorandom binary sequence signals and confirmed its applicability to optical layer multicast routing and its ability to compensate for bit skew caused by fiber chromatic dispersion.

EXPERIMENTAL APPLICATION OF GENERALIZED PREDICTIVE CONTROL OF THE TEMPERATURE IN A POLYSTRENE POLYMERIZATION REACTOR

The performance of generalized predictive control (GPC) was examined and compared with conventional control applied to the temperature of as free radical solution polymerization of styrene in a jacketed batch reactor. Optimal conditions were obtained at different initiator concentrations by applying Lagrange's multiplier to the relevant polymerization reactor. The use of the polynomial ARIMAX model related with reactor temperature and heat input was emphasized. Model parameters were determined using the Kalman algorithm. A pseudo random binary sequence (PRBS) signal was employed in order to operate the system. The GPC control method was based on the ARIMAX model. The performance criteria of GPC in evaluating the temperature control results were the required monomer conversion and molecular weight.

Generalized Minimum Variance Control of Growth Medium Temperature of Baker's Yeast Production

The control of optimum growth medium temperature of a batch bioreactor in which S.cerevisiae was grown under aerobic conditions has been studied. The generalized minimum variance (GMV) algorithm was applied for on-line computer control. A controlled autoregressive moving average model relating the bioreactor temperature and heat input was used to show the dynamic behaviour of the system. The heat input to the bioreactor was chosen as a manipulated variable. A pseudo-random binary sequence signal was applied to the system and the model parameters were determined using the Bierman algorithm. More suitable values of the GMV controller parameters were determined using a total simulation program. These control parameters were used in experimental and theoretical work under several disturbances.

Pseudo-random binary sequence design for finite impulse response identification

The design of pseudo-random binary sequence (PRBS) signals for finite impulse response identification is investigated. We derive a relationship between the PRBS design parameters and the maximum eigenvalue of the parameter error covariance matrix, which yields some useful insights and design guidelines. From the perspective of minimizing the “worst-direction” error represented by the maximum eigenvalue, PRBSs are not the best test signals in the case that disturbances are colored noises. We show that by linearly combining several shifted versions of a PRBS according to the noise model, one can improve the estimation result.

Application of generalized predictive control to baker's yeast production

Generalized Predictive Control (GPC) was applied in the production of baker's yeast. The bioreactor was modeled with the autoregressive integrated moving average exogenous (ARIMAX) parametric difference equation model.A 2 L bioreactor with a cooling jacket was used for collecting input-output data. In order to measure pH, temperature, and dissolved oxygen in the bioreactor growth medium, suitable sensors were placed in the bioreactor. Medium temperature and the heat of the immersed heater were selected as output and manipulated variable, respectively. Square wave and a pseudo-random binary sequence (PRBS) signal were used as disturbance. Model parameters were calculated by using the recursive least square parameter estimation method. Bioreactor temperature was controlled theoretically using the GPC algorithm. The control performance was investigated by giving positive and negative step responses to the set point. The GPC algorithm holds the bioreactor temperature succesfully at the optimal set point. Optimum values of the maximum costing horizon ( N 2 ), control horizon ( N U ), and control weighting ( u ) were found to be 10, 1, and 0.005, respectively.

Transmission experiment using silica waveguide based encoder∕decoders for time-spreading∕wavelength-hopping CDMA

The performance of silica waveguide based encoder/decoders for time-spreading/wavelength-hopping optical CDMA with 10 Gbit/s pseudorandom binary sequence signals is evaluated. Their encoding/decoding ability is demonstrated and it is confirmed that the devices can compensate flexibly for bit skew caused by fibre chromatic dispersion, and can be used for optical layer multicast routing.

Application of optimal adaptive generalized predictive control to a packed distillation column

In this work, optimal operating conditions for a packed distillation column and optimal adaptive generalized predictive control (OA-GPC) were investigated. Thus, the dynamic and steady-state properties of the packed distillation column distilling methanol–water mixture were observed experimentally and theoretically. Mathematical models for the packed distillation column were solved with orthogonal collocation on finite elements. Optimal operating conditions of the system were found by using Box–Wilson optimization method and “Experimental Design” technique. Two types of control algorithm were utilized for controlling the packed distillation column, viz. conventional proportional integral derivative (PID) and generalized predictive control (GPC) at optimal operating conditions. Overhead temperature control was examined experimentally and theoretically. Pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of the polynomial ARIMAX model. Generally theoretical and experimental control results were in accord with each other and it was observed that OA-GPC produced better performance than PID for the packed distillation column.

Psuedo-random binary sequence design for finite impulse response identification

Design of Pseudo-Random Binary Sequence (PRBS) signals for finite impulse response identification is investigated. We derive a relationship between a PRBS design parameters and the maximum eigenvalue of the parameter error covariance matrix, which yields useful guidelines for the design. PRBSs are not the best signals to use in the presence of colored noise. We show that by linearly combining several shifted versions of a PRBS in a specific manner, one can bring substantial improvements to test results.

Nonlinear control of polymerization reactor

In this work, nonlinear model based control was applied to the free radical solution polymerization of styrene in a jacketted batch reactor and its performance was examined to reach the required monomer conversion and molecular weight. Optimal temperature profiles for the properties of polymer quality were evaluated using the Hamiltonian optimization method. Total simulation program having mass and energy balances of the jacketed polymerization reactor was used to calculate the optimal trajectories. For control purposes, several experimental and theoretical dynamic studies have been made to observe the validity of simulation program. Experimental and theoretical nonlinear model based control have been investigated to track the temperature at the optimal trajectory Two types of parametric and nonparametric models were evaluated to achieve the temperature control. For this purpose, reaction curve was obtained to calculate the system dynamic matrix as a nonparametric model. In all control work, heat input to the reactor was chosen as a manipulated variable. Nonlinear auto regressive moving average exogenous (NARMAX) giving a relation between heat input and reactor temperature was chosen to represent the system dynamic and this model was used to describe the related control system as a parametric model. NARMAX model parameters were determined by using Levenberg Marquard algorithm. A pseudo random binary sequence (P.R.B.S.) signal was employed to disturb the system. Total simulation program was used to calculate the system and control parameters. Several types and orders were used to construct the NARMAX models. The efficiency and the performance of the nonlinear model based control with the NARMAX model and dynamic matrix were tested to calculate the best model. Nonlinear model based control system was used to control the reactor temperature at desired temperature trajectory experimentally and theoretically. Theoretical simulation results were compared with experimental control data. It was concluded that the control simulation program represents the behavior of the controlled reactor temperature well. In addition, nonlinear model based control keeps the reactor temperature of optimal trajectory satisfactorily.

New Tuning Method for Generalized Predictive Control of the Production of S. cerevisiae

In this study, S. cerevisiae was produced in a batch bioreactor in aerobic conditions and the growth medium temperature was controlled at its optimal value. In order to control the growth medium temperature, the Generalized Predictive Control (GPC) method was used. The process was described using an Auto Regressive Integrated Moving Average eXogenous (ARIMAX) model. Model parameters were determined by applying Pseudo Random Binary Sequence (PRBS) signals to the process and using Bierman's U-D Factorization Algorithm. By using statistical experimental design and Box-Wilson optimization methods, optimal values of the sampling time and weighting factor were determined. A two-level factorial experimental design technique was used to identify a statistical model. The predicted maximum, minimum and the base levels of sampling time and weighting factor were determined on the basis of the previous knowledge about the plant. It was proposed to determine the values of sampling time and weighting factor giving the best control performance. Integral Square Error (ISE) was selected as the optimization criterion. Growth medium temperature was controlled with very small levels of oscillation around the set point by using optimal controller parameters with GPC.

Bit error rate measurement of a high-speed small-voltage signal using a superconducting transmission line

We developed a measuring system that has a magnetically shielded wide-band test fixture mounted on a closed-cycle cryo-cooler, and used this system to investigate the feasibility of cryo-packaging technology for high-Tc superconducting digital electronics. In this work, we evaluated the bit-error-rate (BER) performance of a system in which was installed a 50-/spl Omega/ standard microstrip line (MSTL) and a YBa/sub 2/Cu/sub 3/Ox microstrip line to a coplanar waveguide (CPW) transmission line converter (MCC). In the experiment, we used 3-Gbps, 2/sup 15/-1 pseudo-random binary sequence (PRBS) signals. The temperature of the test fixture was 20 K. Results showed that the BER was 3.74/spl times/10/sup -9/ for the 50-/spl Omega/ MSTL when the signal amplitude at the sample was 13.3 mV, and 1.52/spl times/10/sup -9/ for the MCC when the amplitude was 30.8 mV. The effect of the signal loss and impedance mismatch on the BER is also discussed.

OPTIMAL TEMPERATURE CONTROL IN A BATCH POLYMERIZATION REACTOR USING NONLINEAR GENERALIZED PREDICTIVE CONTROL

The application of Non-Linear Generalized Predictive Control (NLGPC) to the free radical solution polymerization of styrene in a jacketed batch reactor has been realized. The dynamic behavior of polymerization reactor is modelled and simulated for control purposes. The optimal temperature policies for minimum time, desired conversion and molecular chain length were obtained at different initiator concentrations by applying the optimal control theory which is based on the Hamiltonian principle. The polynomial Nonlinear auto Regressive Integrated Moving Average with external input (NARIMAX) model is used to relate the reactor temperature with heat input for nonlinear control algorithm. The linear (ARIMAX) and nonlinear (NARIMAX) models are utilized in the GPC algorithm for comparison. A Pseudo Random Binary Sequence (PRBS) signal was employed to operate the system. The model parameters are evaluated by using Levenberg Marquart Method. The NLGPC, Linear Generalized Predictive Control (LGPC) and standard PID controllers are applied experimentally to the polymerization reactor by using on-line computer control system. The performance of NLGPC control system was compared with LGPC and standard PID controller. It is concluded that the NLGPC control gives much better performance than the other.

Multi-tone testing of quantisers using PRBS signals

This paper compares the advantages and disadvantages of multi-tone testing with single-tone (sine-wave) testing and proposes the use of pseudo random binary sequence (PRBS) based multi-tone test signals. Single-tone testing is convenient as a pure test signal as it enables the practical performance of analogue to digital converter (ADC) to be compared with its theoretical value at one frequency and amplitude. ADCs exhibit non-linearity that depends on the rate of change and amplitude of the signal and on the amplitude probability density function (APDF) of the test signal. Tests that are sensitive to these effects will therefore assist manufacturers in improving their designs, and users in selecting the best device for their application. A test procedure that enables conventional ADC dynamic test parameters to be measured with the PRBS test signal is described.