A nonlinear closed loop controller is designed for semicontinuous emulsion terpolymerization systems. The objective of the controller is to simultaneously maintain the production at a maximum rate and to ensure homoge...
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A nonlinear closed loop controller is designed for semicontinuous emulsion terpolymerization systems. The objective of the controller is to simultaneously maintain the production at a maximum rate and to ensure homogeneous composition during the process. A high gain non-linear observer is used to estimate the polymer composition, which is needed to calculate the controller action. A static input/output linearizing state feedback is developed for manipulating the feed rates of monomers to control the concentration of monomer in the reactor. The controller was studied by simulating the butyl acrylate, methyl acrylate and vinyl acetate emulsion terpolymerization processes.
A method is presented to combine multiple model estimation with a neural network to obtain more accurate estimates. The key idea is to use the data from the initial phase of the run for system identification, and then...
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A method is presented to combine multiple model estimation with a neural network to obtain more accurate estimates. The key idea is to use the data from the initial phase of the run for system identification, and then run a single estimator designed for the identified model for the remainder of the run. The use of multiple models and neural networks allows the on-line identification to take place extremely quickly. The method is validated on actual data from an important estimation problem in microelectronics manufacturing which is subject to model uncertainties: determining end-point to an etch step using reflectometry data.
Moving Horizon estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one ...
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Moving Horizon estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one of the applications is to provide the full state information for Model Predictive Controller (MPC) to control the process in either setpoint tracking or economic control purposes. However, the computational burden of MHE could deteriorate the control performance if the feedback delay caused by computation is too long, leading to potential safety issues or process damage. In this paper, we propose a fast moving horizon estimation algorithm to overcome the long computational time of MHE for real-time control applications, especially for fast dynamics or large-scale systems. We exploit the nonlinear programming (NLP) sensitivity and make use of efcient NLP solvers, IPOPT and k_aug , to reduce the on-line computational costs. This new approach is demonstrated on a CSTR process, where results are compared to ideal MHE and advanced-step MHE (asMHE).
Abstract It is well know that, for linear gaussian processes, the Kalman Filter provides an elegant solution to the recursive state estimation problem. However, when the process is nonlinear, then one needs to use var...
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Abstract It is well know that, for linear gaussian processes, the Kalman Filter provides an elegant solution to the recursive state estimation problem. However, when the process is nonlinear, then one needs to use various approximations to the filtering problem. In this paper we describe a new approach to nonlinear filtering based on Minimum Distortion Filtering. At the core of this algorithm is a deterministic on-line griding technique based on Vector Quantization. We explore several practical issues associated with the algorithm which are necessary to ensure that it runs effectively. We also present several examples which illustrate the utility of this class of algorithm in the context of nonlinear state estimation.
This paper presents a new method for estimation of ship hydrodynamic coefficients in harbor maneuvers and its applications to control ships. Described are the ways to achieve very high accuracy in the measurements of ...
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This paper presents a new method for estimation of ship hydrodynamic coefficients in harbor maneuvers and its applications to control ships. Described are the ways to achieve very high accuracy in the measurements of a model ship's position during free-running tests, to detect high frequency external effects, and to estimate the coefficients with high accuracy. Estimated coefficients of the World Mitsubishi ship were used to design a non-linear position and attitude-tracking controller for the ship by applying the Decoupling Control Method. Excellent simulation results prove the effectiveness of the estimation method and designed controller.
Abstract The paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron's firing intensity within the vestibu...
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Abstract The paper deals with implicit and explicit approaches for fractional nonlinear model order estimation using a benchmark model relating applied angular rate and neuron's firing intensity within the vestibular system. The implicit approach is based on an interacting multiple models scheme, where several extended Kalman filters with fixed fractional order nonlinear models are running in parallel. An alternative method based on augmented Unscented Kalman filter is proposed where, where the fractional order of the model is estimated explicitly within the filter state. The performance of the estimators is compared with respect to parameters of estimators and fractional derivative approximation.
The paper describes set-bounded parameters and state estimation component for Model Predictive Control of integrated wastewater treatment plant at medium time scale purposes. This is one of the components within Intel...
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The paper describes set-bounded parameters and state estimation component for Model Predictive Control of integrated wastewater treatment plant at medium time scale purposes. This is one of the components within Intelligent Hierarchical Control Structure designed in order to cope with problems of the controlled system such us: multiple time scale, highly non linear dynamics, small number of on-line measurements etc. The set-bounded joined parameter and state estimation algorithm generates robust estimates of these parameters and state for Model Predictive Control purposes. Both, the estimation algorithm and the Model Predictive Controller utilise a grey-box model of controlled plant dedicated for medium time scale. The set-bounded joined state and grey-box model parameters estimation algorithm is validated by simulation. The data were taken partly from real data records gathered at Kartuzy WWTP in Poland and partly from the Kartuzy plant simulation based on recently developed and calibrated plant model.
We propose a novel estimation method of the rotational velocity of a flying ball as well as its position and translational velocity via aerodynamics model by measuring the ball trajectory using the middle speed camera...
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We propose a novel estimation method of the rotational velocity of a flying ball as well as its position and translational velocity via aerodynamics model by measuring the ball trajectory using the middle speed cameras for table tennis system. The aerodynamics model is complex nonlinear and the measured data of the ball trajectory has quantization errors because the size of the ball is very small in the field of view of the middle speed cameras. Then, we consider the estimation by minimizing the difference between the trajectories which are measured by the cameras and numerically solved by integrating the aerodynamics model respectively. Since the difference is not analytical function, the minimization is solved by the downhill simplex method, where some modification is introduced for improving the converge speed. The effectiveness of the method is verified by numerical simulations.
The cylinder pressure information offers an increasing potential for the control of internal combustion engines. The pressure sensors measure a relative cylinder pressure signal only. For the determination of the abso...
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The cylinder pressure information offers an increasing potential for the control of internal combustion engines. The pressure sensors measure a relative cylinder pressure signal only. For the determination of the absolute cylinder pressure, the measured cylinder pressure data, during the pre-combustion compression, is fitted to a polytropic curve. The polytropic exponent, which defines the polytropic curve, is not known and varies during the compression stroke. Therefore, a potential improvement to using predetermined polytropic exponent could be accomplished by using an estimated value. The estimation of the cylinder pressure offset and the polytropic exponent may be obtained by a nonlinear optimization. For the solution of this optimization problem an Extended Kalman Filter with a Markov-2 Process is proposed.
In this paper we compare two techniques applied to the estimation of discrete-time one-dimensional chaotic orbits: the maximum likelihood estimation and the modified Viterbi algorithm. As the second one performs bette...
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In this paper we compare two techniques applied to the estimation of discrete-time one-dimensional chaotic orbits: the maximum likelihood estimation and the modified Viterbi algorithm. As the second one performs better, it is used in two digital modulation schemes based on identification of chaos generator maps: the Modified Maximum Likelihood Chaos Shift Keying using one and two maps. Both have better symbol error rate characteristics than non-coherent chaos communication schemes.
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