A low-noise amplifier operating at 2.4 GHz has been fabricated with MOSFET's in silicon-on-sapphire technology, The amplifier has a 2.8-dB noise figure, IO-dB gain, and 14-dBm output referred IP3 with 14-mW power ...
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A low-noise amplifier operating at 2.4 GHz has been fabricated with MOSFET's in silicon-on-sapphire technology, The amplifier has a 2.8-dB noise figure, IO-dB gain, and 14-dBm output referred IP3 with 14-mW power dissipation, The amplifier was matched for minimum noise with on-chip spiral inductors and capacitors.
Interlaced systems constitute a class of systems only characterized by the zero entries of their matrix configuration and a local stabilizability condition. All these systems are globally stabilizable by a recursive d...
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ISBN:
(纸本)9783952426906
Interlaced systems constitute a class of systems only characterized by the zero entries of their matrix configuration and a local stabilizability condition. All these systems are globally stabilizable by a recursive design procedure which combines steps of backstepping and forwarding. When a nonlinear system misses this structural characterization, other types of conditions (sign, growth) are needed to ensure global stabilization.
Fuzzy logic is a powerful tool in control of systems with ill-defined, inaccurate or unknown mathematical models. In classical applications of fuzzy logic, however, there is a great dependency on proper expert knowled...
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Fuzzy logic is a powerful tool in control of systems with ill-defined, inaccurate or unknown mathematical models. In classical applications of fuzzy logic, however, there is a great dependency on proper expert knowledge acquisition. The authors remove that dependency by using a genetic algorithm (GA) to automatically determine parameters of fuzzy rule sets such as membership functions. This approach differs from conventional applications of GA-fuzzy knowledge development in that expert knowledge is incorporated in creating an initial highly fit population while allowing for randomness among members of the population for diversity. This method is useful for search in GA-hard landscapes and is successfully applied to speed regulation of a DC motor. It is shown that the presented method improves upon the initial fuzzy knowledge-base and significantly outperforms classical PID response.
control of a reusable launch vehicle (RLV) is considered in sliding modes. The sliding mode controllers are designed for the RLV in two operational modes: re-entry (descending) mode and launch (ascending) mode. The de...
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A neuro-fuzzy controller is presented which uses neural networks to modify the parameters of an adaptive fuzzy logic controller. The adaptiveness of the fuzzy controller is derived from a rule generation mechanism and...
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A neuro-fuzzy controller is presented which uses neural networks to modify the parameters of an adaptive fuzzy logic controller. The adaptiveness of the fuzzy controller is derived from a rule generation mechanism and changing the scaling factor or the shape of the membership functions. The neural network functions as a classifier of the system's temporal responses. A multilayer perceptron is used to classify the temporal response of the system into different patterns. Depending on the type of pattern such as "response with overshoot", "damped response", "oscillating response", etc. the scaling factor of the input and output membership functions are adjusted to make the system respond in a desired manner. The rule generation mechanism also utilizes the temporal response of the system to evaluate new fuzzy rules. The non-redundant rules are appended to the existing rule base during the tuning cycles. This controller architecture is used in real-time to control a direct drive motor. The control system hardware utilizes a digital signal processor and a PC to implement the controller architecture. Experimental results are illustrated.
A technique of identifying the dynamics of a robotics system using neural network is presented. The identified model is used by a fuzzy controller to evaluate the range of the control variables and also the performanc...
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A technique of identifying the dynamics of a robotics system using neural network is presented. The identified model is used by a fuzzy controller to evaluate the range of the control variables and also the performance of the adaptive control laws on the identified model. An overview of the neuro-fuzzy control architecture is also discussed. This architecture uses two neural networks, one which identifies the system dynamics and another classifies the temporal response of the robotic system. The information from the neural networks is used to make suitable adjustments in the parameter of the fuzzy controller. This paper however concentrates on the theory and operation of identifying the dynamics of a Adept-Two industrial robot. Simulation results are presented.
Planar photoelastic effect on compound semiconductor structures has been investigated for integrated optical transmitter in rf photonics system. While our prior works emphasized the investigation of low-loss photoelas...
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In recent years, evolution based knowledge optimization has gained a great deal of popularity due to its inherent ability in efficient and parallel search of complex and multi-modal landscapes. Application of Genetic ...
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We present the results of a continuing effort towards the introduction of automatic control to the operation of cupola iron furnaces. The cupola furnace has played an important role in the foundry industry since its i...
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We present the results of a continuing effort towards the introduction of automatic control to the operation of cupola iron furnaces. The cupola furnace has played an important role in the foundry industry since its invention in 1794. The main aim of this research is to improve the operational efficiency and performance of the cupola furnace. A lumped model for the cupola is used for the controller design. The controller is divided into three parts. A feedforward controller is used to decouple the model into delayed and undelayed dynamics. A robust controller is designed for the delayed dynamics of the model based upon a combination of H/sub /spl infin// control and a Smith predictor. The controller for the undelayed portion of the model is designed using an LQR procedure.
In this paper, some recent results on systematic design of model based fuzzy controllers for nonlinear systems with Takagi-Sugeno (T-S) fuzzy models are discussed. These methods are based on finding a common Lyapunov ...
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In this paper, some recent results on systematic design of model based fuzzy controllers for nonlinear systems with Takagi-Sugeno (T-S) fuzzy models are discussed. These methods are based on finding a common Lyapunov matrix and state feedback gains for the closed loop T-S fuzzy model using linear matrix inequalities. However, all of these methods assume that the states are available for measurement, while in reality this is not the case. To solve this problem, the notion of fuzzy observer is introduced and a separation theorem for the observer/controller system is stated and proof sketch is presented.
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