39,958 research outputs found
Environmental test chamber for the support of learning and teaching in intelligent control
The paper describes the utility of a low cost, 1 m2 by 2 m forced ventilation, micro-climate test chamber, for the support of research and teaching in mechatronics. Initially developed for the evaluation of a new ventilation rate controller, the fully instrumented chamber now provides numerous learning opportunities and individual projects for both undergraduate and postgraduate research students
Time-Varying System Identification Using Modulating Functions and Spline Models With Application to Bio-Processes
Time dependent parameters are frequently encountered in many real processes which need to be monitored for process modeling, control and supervision purposes. Modulating functions methods are especially suitable for this task because they use the original continuous-time differential equations and avoid differentiation of noisy signals. Among the many versions of the method available, Pearson–Lee method offers a computationally efficient alternative. In this paper, Pearson–Lee method is generalized for non-stationary continuous-time systems and the on-line version is developed. The time dependent parameters are modeled as polynomial splines inside a moving data window and recursion formulae using shifting properties of sinusoids are formulated. The simple matrix update relations considerably reduce the number of computations required when compared with repeatedly using FFT. The method is illustrated for estimating the kinetic rates and yield factors as time-varying parameters in a fermentation process. The Monod law along with temperature dependency models were used to simulate the data. The simulation study shows that it is not necessary to assume a growth model in order to estimate the kinetic rate parameters
Identification of continuous-time models for nonlinear dynamic systems from discrete data
A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed
Identification of fractional order systems using modulating functions method
The modulating functions method has been used for the identification of
linear and nonlinear systems. In this paper, we generalize this method to the
on-line identification of fractional order systems based on the
Riemann-Liouville fractional derivatives. First, a new fractional integration
by parts formula involving the fractional derivative of a modulating function
is given. Then, we apply this formula to a fractional order system, for which
the fractional derivatives of the input and the output can be transferred into
the ones of the modulating functions. By choosing a set of modulating
functions, a linear system of algebraic equations is obtained. Hence, the
unknown parameters of a fractional order system can be estimated by solving a
linear system. Using this method, we do not need any initial values which are
usually unknown and not equal to zero. Also we do not need to estimate the
fractional derivatives of noisy output. Moreover, it is shown that the proposed
estimators are robust against high frequency sinusoidal noises and the ones due
to a class of stochastic processes. Finally, the efficiency and the stability
of the proposed method is confirmed by some numerical simulations
Closed-Loop Control of a Piezo-Fluidic Amplifier
Fluidic valves based on the Coand\u{a} effect are increasingly being
considered for use in aerodynamic flow control applications. A limiting factor
is their variation in switching time, which often precludes their use. The
purpose of this paper is to demonstrate the closed-loop control of a recently
developed, novel piezo-fluidic valve that reduces response time uncertainty at
the expense of operating bandwidth. Use is made of the fact that a fluidic jet
responds to a piezo tone by deflecting away from its steady state position. A
control signal used to vary this deflection is amplitude modulated onto the
piezo tone. Using only a pressure measurement from one of the device output
channels, an output-based LQG regulator was designed to follow a desired
reference deflection, achieving control of a 90 m/s jet. Finally, the
controller's performance in terms of disturbance rejection and response time
predictability is demonstrated.Comment: 31 pages, 23 figures. Published in AIAA Journal, 4th May 202
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