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ABSTRACT: Adsorption plays an important role in water and wastewater treatment. The analysis and design of processes that involve adsorption rely on the availability of isotherms that describe these adsorption processes. Adsorption isotherms are usually estimated empirically from measurements of the adsorption process variables. Unfortunately, these measurements are usually contaminated with errors that degrade the accuracy of estimated isotherms. Therefore, these errors need to be filtered for improved isotherm estimation accuracy. Multiscale wavelet based filtering has been shown to be a powerful filtering tool. In this work, multiscale filtering is utilized to improve the estimation accuracy of the Freundlich adsorption isotherm in the presence of measurement noise in the data by developing a multiscale algorithm for the estimation of Freundlich isotherm parameters. The idea behind the algorithm is to use multiscale filtering to filter the data at different scales, use the filtered data from all scales to construct multiple isotherms and then select among all scales the isotherm that best represents the data based on a cross validation mean squares error criterion. The developed multiscale isotherm estimation algorithm is shown to outperform the conventional time-domain estimation method through a simulated example.
International Journal of Environmental Science and Technology. 01/2010;
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ABSTRACT: In this article, resilient delay-dependent adaptive control algorithms are developed for closed-loop stabilization of a class of uncertain time-delay systems with time-varying state delay, nonlinear dynamical perturbation, and controller gain perturbation. The norm of the nonlinear perturbation is assumed to be bounded by a weighted norm of the state, and the norm of the uncertainty of the state feedback gain is assumed to be bounded by a positive constant. Two main problems are investigated. In the first problem, an adaptive control scheme is developed to guarantee asymptotic stabilization of the closed-loop system when the value of the upper bound of the norm of the state feedback gain perturbation is known. In the second problem, asymptotically stabilizing adaptive controller is derived when the value of the upper bound of the norm of the state feedback gain perturbation is assumed to be unknown. A numerical simulation example, that illustrates the two design approaches, is presented.
Decision and Control, 2007 46th IEEE Conference on; 01/2008
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ABSTRACT: The decoupled control of torque and flux has made field oriented control an attractive choice for high performance induction motor drives. However, changes in the speed tracking trajectory and external disturbances make it difficult to achieve an acceptable closed-loop tracking performance, especially when traditional linear controllers are used. This paper addresses this issue by applying direct and indirect adaptive fuzzy controllers for performance enhancement of variable speed control of induction machines. Theoretical background of these schemes is outlined, and then a simulation test bench has been established for performance evaluation under a variety of operating conditions. Such conditions include changes in the speed reference trajectory and presence of external disturbances, such as load changes. A comparison has been made among direct adaptive, indirect adaptive and direct fuzzy controllers to show the potential of applying adaptive fuzzy control techniques to induction machines.
Electric Machines & Drives Conference, 2007. IEMDC '07. IEEE International; 06/2007
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H.N. Nounou
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ABSTRACT: In this article, delay-dependent adaptive control algorithms are developed for closed-loop stabilization of a class of uncertain time-delay systems with time-varying delay and nonlinear dynamical perturbation. The norm of the nonlinear perturbation is assumed to be bounded by a weighted norm of the state. Two main problems are investigated. In the first problem, an adaptive control scheme is developed to guarantee asymptotic stabilization of the closed-loop system when the weight of the state norm is assumed to be known. In the second problem, asymptotically stabilizing adaptive controller is derived when the weight of the state norm is assumed to be unknown. A numerical simulation example, that illustrates the two design approaches, is presented
Decision and Control, 2006 45th IEEE Conference on; 01/2007
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ABSTRACT: The presence of measurement noise in the data used in empirical modeling can have a drastic effect on the accuracy of estimated models, and thus need to be removed for improved model accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this ability is exploited to improve the prediction accuracy of the Takagi-Sugeno (TS) fuzzy model by developing a multiscale fuzzy (MSF) system identification algorithm. The algorithm relies on constructing multiple fuzzy models at multiple scales using the scaled signal approximations of the input-output data, and then selecting the optimum multiscale model which maximizes the prediction signal-to-noise ratio. The developed algorithm is shown to outperform its time domain counterpart through a simulated example.
Decision and Control, 2004. CDC. 43rd IEEE Conference on; 01/2005
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ABSTRACT: In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, "approximators" such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present algorithms to tune some of the parameters (e.g., the adaptation gain and the direction of descent) for a gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in both direct and indirect cases. In our proposed algorithms, the adaptation gain and the direction of descent are obtained by minimizing the instantaneous control energy. We will show that updating the adaptation gain can be viewed as a special case of updating the direction of descent. We will also compare the direct and indirect adaptive control schemes and illustrate their performance via a simple surge tank example.
IEEE Transactions on Fuzzy Systems 03/2004; · 4.26 Impact Factor
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H.N. Nounou
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ABSTRACT: In adaptive control algorithms, the adaptation routine (e.g. least squares or gradient) is usually used to adjust the controller parameters to approximate the ideal controller that is assumed to exist. Searching for the ideal parameter vector, a gradient-based hybrid adaptive routine is used here for continuous-time nonlinear systems. The adjustment of the parameter vector is usually based on minimizing the squared error. For direct adaptive control, in this paper an algorithm is presented to adapt the direction of the search vector so that the instantaneous control energy is minimized. Hence, the overall adaptive routine minimizes not only the squares error but also the instantaneous control energy. Stability results of the presented algorithm show that boundedness of the error is dependent on the length of the search vector.
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on; 01/2004
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ABSTRACT: In direct adaptive control, the adaptation mechanism attempts to
adjust a parameterized nonlinear controller to approximate an ideal
controller. In the indirect case, however, we approximate parts of the
plant dynamics that are used by a feedback controller to cancel the
system nonlinearities. In both cases, "approximators" such as linear
mappings, polynomials, fuzzy systems, or neural networks can be used as
either the parameterized nonlinear controller or identifier model. We
present an algorithm to tune the adaptation gain for a gradient-based
hybrid update law used for a class of nonlinear continuous-time systems
in both direct and indirect cases. In our proposed algorithm, the
adaptation gain is obtained by minimizing the instantaneous control
energy
Decision and Control, 2001. Proceedings of the 40th IEEE Conference on; 02/2001
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[show abstract]
[hide abstract]
ABSTRACT: In direct adaptive control, the adaptation mechanism attempts to
adjust a parameterized nonlinear controller to approximate an ideal
controller. In the indirect case, however, we approximate parts of the
plant dynamics that are used by a feedback controller to cancel the
system nonlinearities. In both cases, approximators such as linear
mappings, polynomials, fuzzy systems, or neural networks can be used as
either the parameterized nonlinear controller or identifier model. We
present an algorithm to tune the direction of descent for a
gradient-based approximator parameter update law used for a class of
nonlinear discrete-time systems in both direct and indirect cases. In
our proposed algorithm, the direction of descent is obtained by
minimizing the instantaneous control energy. We show that updating the
adaptation gain can be viewed as a special case of updating the
direction of descent. Finally, we illustrate the performance of the
proposed algorithm via a simple surge tank example
American Control Conference, 2001. Proceedings of the 2001; 02/2001
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ABSTRACT: Direct adaptive control is a widely known control scheme in which
the controller attempts to approximate the ideal controller directly.
“Approximators” such as linear mappings, polynomials, fuzzy
systems, or neural networks can be used. Here, we present two algorithms
to tune the adaptation gain for a gradient based approximator parameter
update law used for a class of nonlinear discrete-time systems
American Control Conference, 2000. Proceedings of the 2000; 02/2000
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ABSTRACT: Fuzzy model predictive control (FMPC) algorithms presented here
are model-based control schemes in which the models used for prediction
are Takagi-Sugeno fuzzy systems (TSFS). Three approaches to FMPC design
are discussed. The fuzzy model in the first approach can be represented
as a time-varying affine model that is used for control. In the second
approach, the fuzzy system is a convex combination of multiple affine
models, where the control is a convex combination of multiple
controllers. Lastly, the control of the third algorithm is obtained when
only the model with the highest certainty is used in the design. Also,
we extend the idea to have an adaptive controller for the first
algorithm, where the parameters of the fuzzy model are updated online
Intelligent Control/Intelligent Systems and Semiotics, 1999. Proceedings of the 1999 IEEE International Symposium on; 02/1999
