Search results for "Fuzzy Control"
showing 10 items of 108 documents
Parallel Genetic Algorithms for the Tuning of a Fuzzy AQM Controller
2003
This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workst…
Intelligent Traction Control for Wheeled Space Vehicles
2006
This paper presents the SC-MER, safety control for Mars exploration rover, an innovative traction control scheme for wheeled mobile vehicles. The system is thought to be used on space mission rovers and is based on fuzzy logic and competitive neural networks to achieve optimal navigation on rough terrain with variable morphology. The main goal of this research is to minimize the power consumption needed during the navigation and improve the overall stability and safety of the rover itself
Approximation-based adaptive tracking control of stochastic nonlinear systems with a general form
2014
In this paper, an approximation-based adaptive tracking control scheme is proposed for a class of stochastic nonlinear systems with a more general structure. Fuzzy logical systems are used to approximate unknown nonlinearities in the controller design procedure and the backstepping technique is utilized to construct a state-feedback adaptive controller. The proposed controller can guarantee that all the signals in the closed-loop system are fourth-moment semi-globally uniformly ultimately bounded and the tracking error eventually converges to a small neighborhood around the origin. Simulation results are used to show the effectiveness of the proposed control scheme.
Induced &#x2113;<inf>2</inf> control of discrete-time Takagi-Sugeno fuzzy systems with time-varying delays via dynamic output feedback
2012
This paper is concerned with analyzing a novel model transformation of discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delays and applying it to dynamic output feedback (DOF) controller design. A new auxiliary model is proposed by employing a new approximation for time-varying delay state, and then delay partitioning method is used to analyze the scaled small gain of this auxiliary model. A sufficient condition on discrete-time T-S fuzzy systems with time-varying delays, which guarantees the corresponding closed-loop system to be asymptotically stable and has an induced l 2 disturbance attenuation performance, is derived by employing the scaled small gain theorem. Then the…
Adaptive type-2 fuzzy control of non-linear systems
2009
The paper describes the development of two different type-2 adaptive fuzzy logic controllers and their use for the control of a non linear system that is characterized by the presence of bifurcations and parameter uncertainty. Although a type-2 fuzzy logic controller is able to handle the non linearities and the uncertainties present in a system, its robustness and effectiveness can be increased by the use of an opportune adaptive algorithm. A simulation study was conducted to compare the behavior of adaptive controllers with that of simple type-1 and type-2 fuzzy logic controllers. The system to be controlled, used for the simulation, is a continuous bioreactor for the treatment of mixed w…
Adaptive type-2 fuzzy logic control of a bioreactor
2010
Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI cont…
Nonlinear fuzzy control of a fed-batch reactor for penicillin production
2012
Abstract The process of penicillin production is characterized by nonlinearities and parameter uncertainties that make it difficult to control. In the paper the development and testing of a multivariable fuzzy control system that makes use of type-2 fuzzy sets for the control of pH and temperature are described. The performance of the type-2 fuzzy logic control system (T2FLCS) is compared by simulation with that of a type-1 fuzzy logic control system (T1FLCS) and that of a control system with traditional proportional-integral-derivative (PID) controllers proposed in the literature. The fuzzy controllers are optimized using an ANFIS algorithm. The best results are obtained with the T2FLCS pa…
Experimental Comparison of Type-1 and Type-2 Fuzzy Logic Controllers for the Control of Level and Temperature in a Vessel
2011
Abstract The objective of this experimental study is to compare the performance of type-1 and type-2 fuzzy logic controllers on a real system where the control of liquid level and temperature are considered. By the use of genetic algorithms it is possible to optimize the fuzzy sets of each fuzzy controller assuring high control performance. The experimental results show that a better control in terms of robustness can be achieved by type-2 fuzzy logic controllers.
Development of a predicitive type-2 neurofuzzy controller
2009
A controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership function parameters. The proposed controller is tested by simulation for the control of a bioreactor characterized by bifurcation and parameter uncertainty.