Two-level controllers for dissolved oxygen reference trajectory tracking for activated sludge processes is proposed and investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological t...
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Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adap...
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Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adap...
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Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adaptive iterative learning control using the command generator tracker (CGT) theory is proposed in this paper. The method can be applied to control a plant with a higher relative degree and it only requires to iteratively adjust n m + 2 parameters for an SISO plant. Therefore, the ILC control system is very simple. The proposed method is in the spirit of simple adaptive control which has received intensive researches during past two decades. Simulation results show the effectiveness and usefulness of the proposed method.
Two-level controllers for dissolved oxygen reference trajectory tracking for activated sludge processes is proposed and investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological t...
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Two-level controllers for dissolved oxygen reference trajectory tracking for activated sludge processes is proposed and investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological treatment using an activated sludge technology are considered. Typically, an aeration system itself is a complicated hybrid nonlinear dynamical system with faster dynamics compared to internal dynamics of the dissolved oxygen at a biological reactor. It is common approach to neglect this dynamics and also important operational limitations of this system such as a limited frequency of allowed switching of the blowers. The paper proposes a two level controller to track prescribed dissolved oxygen trajectory. The upper level control unit produces desired aeration flow set points. For this unit the aeration system is an actuator. The nonlinear model predictive control algorithm is applied to design this controller unit. Also, the predictive control is used to design the lower level control unit based on a linearised hybrid dynamics of the aeration process. The overall controller is validated by simulation using real data sets and ASM2d model of the biological reactor.
Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main feature which makes them distinct from other classes of 2D linear systems is that information propagation in one of the two independent directions only occurs over a finite duration. This, in turn, means that a distinct systems theory must be developed for them, which can then be translated into efficient routinely applicable controller design algorithms for applications domains. In this paper, we give the first significant results on a positive realness based approach to the analysis of these processes.
A causal iterative learning control algorithm based on optimal feedback and feedforward control is derived to provide perfect tracking of selected output values at specified times. Exponential convergence of the algor...
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In this paper we present a new technique to face the so-called constrained Model Predictive control. The main advantage of this new approach with respect to other well-known techniques is represented by the reduced co...
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A causal iterative learning control algorithm based on optimal feedback and feedforward control is derived to provide perfect tracking of selected output values at specified times. Exponential convergence of the algor...
A causal iterative learning control algorithm based on optimal feedback and feedforward control is derived to provide perfect tracking of selected output values at specified times. Exponential convergence of the algorithm is proved and a realization based on Riccati feedback is described. The properties of the algorithm are illustrated by example.
In this paper we present a new technique to face the so-called constrained Model Predictive control. The main advantage of this new approach with respect to other well-known techniques is represented by the reduced co...
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In this paper we present a new technique to face the so-called constrained Model Predictive control. The main advantage of this new approach with respect to other well-known techniques is represented by the reduced conservativeness. More precisely, the technique described in this paper can be applied to polytopic uncertain systems and is based on the use of several Lyapunov functions each one corresponding to a different vertex of the uncertainty's polytope.
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