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This paper presents a non-linear, data driven Adaptive Network based Fuzzy Inference System (ANFIS) modeling of a Two Tanks Hydraulic System (TTHS). The paper also addresses the design of a Type 1 Fuzzy Logic Controller optimized with Genetic Algorithms (GA). The controller was designed and tested in simulation with the obtained ANFIS model and validated in real-time with the actual TTHS. Obtained...
Mental stress is an important aspect that can, more often than not, affect the humans' performance when they attempt tasks of varying levels of complexity. Indeed, exposing the human to high levels of mental stress which he/she cannot tolerate, may affect the successful completion of the task to be accomplished. Therefore, the need for a system which can monitor the levels of the applied mental stress...
In this paper the analysis, design and simulation of a Takagi-Sugeno model for DC-DC converters, the buck-boost as a particular case study, are presented in order to analyze their performance compared to classical models of this converter. The model used for the Takagi-Sugeno fuzzy representation is the reduced nonlinear model. the Takagi-Sugeno fuzzy model is defined by eight rules and three parameters:...
In this paper the design and simulation of a residual generator based on a Luenberger observer is presented. The residual generator is applied to a DC-DC converter in order to indicate disturbances or failures during the system operation. As a particular case study a Buck converter is analyzed, according to its model characteristics a linear observer is selected to generate residuals, Labview simulations...
In this paper, the design and real-time implementation of a compensator (controller-observer) for a nonlinear two-tank liquid level system, linearized at a specific operating point, is presented. The controller uses feedback of this estimated state by means of a Luenberger observer which, in turn, is used to generate a residual signal by comparing the estimated state with the real state. The evaluation...
This paper proposes a robust control scheme for the Antilock Braking System (ABS). The controller is based on the Super-Twisting (ST) control algorithm, this is a high-orden sliding mode controller. The ABS is nonlinear and uncertain system; therefore, a robust control method needs to be employed. The ST control algorithm is developed and applied for a quarter vehicle model via simulations. The simulation...
This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) Control design for a Two Tanks Hydraulic System (TTHS) model. The ANFIS control algorithm was trained to perform as a successful Mamdani Fuzzy Logic Control (FLC) algorithm designed previously. Its performance was tested for different references as well as perturbation scenarios and compared with the FLC obtaining similar results...
This paper presents a graphic interface developed to control the operation of a commercial adjustable speed drive. The interface developed in LabVIEW allows specifying and modifying data of the drive, data of operation parameters of the induction motor controlled by the drive, and real time data actualizations. The interface is a tool easy to handle without having knowledge of the driver programming...
In this work, a fault detection method based on a neural-network models bank to residual generation and a residual evaluation scheme using a fuzzy rules type is developed. The case of study is a nonlinear hydraulic system consisting of two interconnected tanks which is simulated, in normal conditions and fault conditions. In this case we use also its equivalent Takagi-Sugeno Model in discreet time...
In this paper, we present some results obtained from the application of a class of sliding mode observers to the model-based fault diagnosis problem in non-linear dynamic systems. A Takagi-Sugeno fuzzy model is used to describe the system and then sliding mode observers are designed to estimate the system state vector, from this the diagnostic signal-residual, is generated by the comparison of measured...
A technique for design of unknown inputs observers is presented, applied to the solution of fault detection problem. The proposed technique is mainly based on observation of error signals known as residuals, which are obtained by taking away actual input from estimated input. An unknown inputs observer has the estimation error vector in asymptomatic cero tendency as its special feature, without considering...
In this paper, the problem of sensor faults detection (SFD) to non-linear systems is addressed using a modified dedicated observers approach originally proposed by Clark [1], The new approach use sliding mode fuzzy dedicates observers for the Takagi-Sugeno fuzzy model of the non-linear dynamic system to generate the signal residuals that indicate a fault condition in the system. This approach provides...
In this paper, we present some results obtained from the application of a class of sliding mode observers to the model-based fault diagnosis problem in non-linear dynamic systems. A Takagi-Sugeno fuzzy model is used to describe the system and then sliding mode observers are designed to estimate the system state vector, from this the diagnostic signal-residual is generated by the comparison of measured...
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