Control of a coupled tank system using PI controller with advanced control methods

The liquid level control in tanks and flow control between cascaded or coupled tanks are the basic control problems exist in process industries nowadays. Liquids are to be pumped, stored or mixed in tanks for various types of chemical processes and all these require essential control and regulation of flow and liquid level. In this paper, different types of tuning methods are proposed for Proportional-Integral (PI) controller and are further improved with integration of Advanced Process Control (APC) method such as feedforward and gain scheduling to essentially control the liquid level in Tank 2 of a coupled tank system. The MATLAB/Simulink tools are used to design PI controller using pole-placement, Ciancone, Cohen Coon and modified Ziegler-Nichols tuning method with Cohen Coon tuning method found to have a better performance. Advanced process control such as feedforward-plus-PI, Gain Scheduling (GS) based PI, Internal Model Control (IMC) based PI, feedforward-plus-GS-based PI and feedforward-plus-IMC-based PI controllers are further tested as improvement version to further compare the significance of the advanced process control outcomes hence GS-PI, improved GI-base PI-plus FF found to have better performance. The GS method is built over five operating points to approximate the system’s nonlinearity and is eventually combined with feedforward control to yield a much better performance

Feedback and Feedforward Control of a Biotrickling Filter for H2S Desulfurization with Nitrite as Electron Acceptor

Biotrickling filters’ control for H2S removal has special challenges because of complexity of the systems. Feedback and feedforward control were implemented in an anoxic biotrickling filter, operated in co-current flow mode and using nitrite as an electron acceptor. The feedback controller was tuned by three methods—two based on Ziegler-Nichols’ rules (step-response and maintained oscillation) and the third using the Approximate M-constrained Integral Gain Optimization (AMIGO). Inlet H2S staircase step perturbations were studied using a feedforward control and the e ect of EBRT considered by feedback control. The tuning method by maintained oscillation shows the lower errors. The selected controller was a PI, because unstable behavior at the lowest H2S inlet loading was found under a PID controller. The PI control was able to maintain an outlet H2S concentration of 14.7 0.45 ppmV at three EBRT, studied at 117 s, 92 s and 67 s. Therefore, desulfurized biogas could be used to feed a fuel cell. Feedforward control enhances BTF performance compared to the system without control. The maximum outlet H2S concentration was reduced by 26.18%, although sulfur selectivity did not exceed 55%, as elemental sulfur was the main oxidation product

Neural Networks in Nonlinear Aircraft Control

Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law

Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks

We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of $n$ output units, given the states of $k\geq1$ input units, can be approximated arbitrarily well by a network with $2^{k-1}(2^{n-1}-1)$ hidden units.Comment: 13 pages, 3 figure

Trapped Modes in Linear Quantum Stochastic Networks with Delays

Networks of open quantum systems with feedback have become an active area of research for applications such as quantum control, quantum communication and coherent information processing. A canonical formalism for the interconnection of open quantum systems using quantum stochastic differential equations (QSDEs) has been developed by Gough, James and co-workers and has been used to develop practical modeling approaches for complex quantum optical, microwave and optomechanical circuits/networks. In this paper we fill a significant gap in existing methodology by showing how trapped modes resulting from feedback via coupled channels with finite propagation delays can be identified systematically in a given passive linear network. Our method is based on the Blaschke-Potapov multiplicative factorization theorem for inner matrix-valued functions, which has been applied in the past to analog electronic networks. Our results provide a basis for extending the Quantum Hardware Description Language (QHDL) framework for automated quantum network model construction (Tezak \textit{et al.} in Philos. Trans. R. Soc. A, Math. Phys. Eng. Sci. 370(1979):5270-5290, to efficiently treat scenarios in which each interconnection of components has an associated signal propagation time delay

On-line nonparametric regression to learn state-dependent disturbances

A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between in- and output can he approximated, even when the structure of this relation is unknown beforehand.\ud This method can adapt its structure on-line while it preserves information offered by previous samples, making it applicable in a control setting. This method has been tested with compntergenerated data, and it b used in a simulation to learn the non-linear state-dependent effects, both with good success

Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid

This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. Different structures of NARMA (Non-linear ARMA) models have been studied. The experimental results have allowed to carry out a comparison between the different neural approaches and a first-principles model. The best neural results are obtained using a parallel model structure based on a recurrent neural network architecture, which guarantees better dynamic approximations than currently employed neural models. The results suggest that parallel models built up with recurrent networks can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits which change from batch installation to installation.Publicad