In this paper we develop linear, fixed-order (i.e., full- and reduced-order) pressure rise feedback dynamic compensators for axial flow compressors. Unlike the nonlinear static controllers proposed in the literature p...
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In this paper we develop linear, fixed-order (i.e., full- and reduced-order) pressure rise feedback dynamic compensators for axial flow compressors. Unlike the nonlinear static controllers proposed in the literature possessing gain at all frequencies, the proposed dynamic compensators explicitly account for compressor performance versus sensor accuracy, compressor performance versus processor throughput, and compressor performance versus disturbance rejection. Furthermore, the proposed controller is predicated on only pressure rise measurements, providing a considerable simplification in the sensing architecture over the bifurcation-based and backstepping controllers proposed in the literature.
In this paper an automotive MacPherson suspension unit is identified and modeled using a radial basis function network with local linear weighting functions (LOLIMOT). The output of the network represents the nonlinea...
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In this paper an automotive MacPherson suspension unit is identified and modeled using a radial basis function network with local linear weighting functions (LOLIMOT). The output of the network represents the nonlinear force characteristic of the spring-and-damper unit in dependency of the measured spring travels at the front axle and the corresponding spring travel velocities. The network parameters are interpreted physically by comparing the five local linear models identified with the network to a physical second-order model of the wheel suspension unit. Implemented in an overall vehicle model the network or its look-up table representation represents an adaptable model of the nonlinear vehicle suspension characteristics.
In this study, we focus on the error of estimated frequency of disturbance and present a new adaptive frequency tracking and new modification law after examining relation between the error of frequency and output leve...
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In this study, we focus on the error of estimated frequency of disturbance and present a new adaptive frequency tracking and new modification law after examining relation between the error of frequency and output level in detail. We also develop a multiple frequency estimation algorithm, which is insensitive to observation noise and prove the asymptotic stability for adaptive nonlinear algorithm and adaptive frequency tracking method theoretically. The results of simulation show that when estimated frequency by difference equation method approach to the true value, then the output error converges to zero (or equilibrium point) asymptotically. This corresponds to the asymptotic stability condition. The effectiveness of this method and the theoretical proof are verified by simulation. The experimental results show that the proposed algorithm is effective for achieving unbalance vibration suppression.
Magnetic levitation systems have recently become the focus of many research interests not only because they are most suitable for high precision engineering applications but also due to the fact that they represent a ...
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Magnetic levitation systems have recently become the focus of many research interests not only because they are most suitable for high precision engineering applications but also due to the fact that they represent a difficult challenge to control engineers. As a result, most previous studies have focused on the control stabilization problem. In this paper, we address the issue of performance with respect to uncertainty in order to achieve a desired rigidity. The proposed controller is an adaptive backstepping controller. The adaptive backstepping controller provides system stability under model uncertainty, and achieves the desired servo performance. The experiments show that the proposed control achieves a superior behavior than other control.
The theory of Markov Decision Process (MDP) has been widely applied to the networking management such as routing and admission control. However, the traditional MDP approach is mainly hindered by prohibitive computati...
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The theory of Markov Decision Process (MDP) has been widely applied to the networking management such as routing and admission control. However, the traditional MDP approach is mainly hindered by prohibitive computational complexity. The performance potential theory offers an efficient solution to alleviate such difficulties in infinite-horizon MDP problems. The concept `potential' leads to some important properties that allow it to be measured on a single sample path, thereby adapting to the dynamic characteristics in realistic applications such as high speed networks. In this paper, we investigate the application of single-sample-path-based potential theory to the admission control in the network with multiple classes of traffic. Optimal policies under different traffic characteristics are obtained with a fast convergence. Some simple and efficient algorithms are developed for online implementation.
In this paper, we present a phenomenological nonlinear dynamic model for direct injection stratified charge (DISC) gasoline engines and discuss several key control problems for this advanced technology powertrain. The...
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In this paper, we present a phenomenological nonlinear dynamic model for direct injection stratified charge (DISC) gasoline engines and discuss several key control problems for this advanced technology powertrain. The model is developed and validated using dynamometer engine mapping data obtained from a 4-cylinder DISC engine. It captures the static behavior of the key components of a DISC engine, such as the torque and emissions generation and volumetric efficiency, as well as the essential dynamics for the intake manifold and engine rotational inertia. It is shown that the multi-mode operation of a DISC engine dictates a hybrid model structure and also requires a coordinated multivariable control strategy to achieve expected performance.
Continuous venovenous hemofiltration (CVVH) is a lifesaving renal replacement therapy used in clinical intensive care settings. Patients undergoing a life threatening illness often develop renal failure, and CVVH perf...
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Continuous venovenous hemofiltration (CVVH) is a lifesaving renal replacement therapy used in clinical intensive care settings. Patients undergoing a life threatening illness often develop renal failure, and CVVH performs the blood filtering process while the kidneys recover. The open-loop fluid flow induced by the peristaltic pumps utilized in CVVH cannot provide the fluid balance accuracy necessary to treat neonates. This paper presents a hierarchical control architecture for CVVH. The control methodology uses a direct adaptive control scheme for the peristaltic pumps and a supervisory control algorithm for high-level decisions on the safe operation of the system. Adaptive control of the pumps results in improved accuracy of fluid flow, while the supervisory controller provides greater autonomy and reduces the burden on clinical personnel. The performance of the proposed hierarchical controller is illustrated by experiments on a hemofiltration machine using a simulated patient.
The problem of robust stabilization of nonlinear systems with partially known uncertainties is considered. A class of continuous adaptive robust state feedback controllers with simpler structure is proposed. It is sho...
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The problem of robust stabilization of nonlinear systems with partially known uncertainties is considered. A class of continuous adaptive robust state feedback controllers with simpler structure is proposed. It is shown that the resulting closed-loop nonlinear systems with uncertainties are stable in the sense of uniform ultimate boundedhess. In contrast with some results presented in the control literature, the proposed adaptive law for updating the estimate values of the unknown parameters is continuous, and the existence of the solutions to the resulting closed-loop systems in the usual sense can well be guaranteed. Moreover, due to the continuity of state feedback controller and adaptive law, the proposed adaptive robust state feedback controllers is easily implemented in practical robust control problems. Finally, an illustrative example is given to demonstrate the utilization of the results.
In this paper we investigate the interaction between window based flow control and a recently proposed packet scheduling discipline designed for real-time services. The scheduling discipline, called the Dual Queue dis...
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In this paper we investigate the interaction between window based flow control and a recently proposed packet scheduling discipline designed for real-time services. The scheduling discipline, called the Dual Queue discipline, has been shown to provide greater flexibility than other scheduling disciplines such as fair queueing. However, its performance on non real-time services has not been previously investigated. We show that the Dual Queue's performance for non real-time services is again better than that of alternative approaches, which indicates that it will perform well in an environment of mixed real-time and non real-time traffic, such as the current internet.
We describe an innovative hybrid controller that uses neural networks and Multivariable Predictive control (MPC) to handle abnormal events in process applications. The controller detects abnormal situations, such as g...
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We describe an innovative hybrid controller that uses neural networks and Multivariable Predictive control (MPC) to handle abnormal events in process applications. The controller detects abnormal situations, such as grinding mill spills or mill power excursions in mineral processing, or incipient flooding in separation columns and then reconfigures the multivariable controller to stabilize the operations. Neural networks are typically used to detect and classify the abnormal situation and knowledge of process dynamics and interactions is used to reconfigure the multivariable predictive controller parameters to stabilize the operations. Thus the MPC can be configured and tuned to provide good control around the `normal' operating range, and when an upset occurs and is detected a new set of tuning parameters are used.
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