New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Design of a simplified neuro-fuzzy system based on CMAC Architectures

This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.

An Intelligent Controller Design for Magnetorheological Damper Based on a Quarter-car Model

This paper presents the control strategies of nonlinear vehicle suspension using a magnetorheological (MR) damper. We used two different approaches for modeling and control of the mechanical and electrical parts of the suspension systems with the MR damper. First, we have formulated and resolved the control problem in order to design the linear feedback dumping force controller for a nonlinear suspension system. Then the values of the control dumping force functions were transformed into electrical control signals by the application of a fuzzy logic control method. The numerical simulations were provided in order to show the effectiveness of this method for the semi-active control of the quarter-car suspension.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Stabilizing controller design for uncertain nonlinear systems using fuzzy models

A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state-feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

Cerebellar Cognitive Affective Syndrome in Machado Joseph Disease: Core Clinical Features

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Variable structure controller and observer applied to induction machine

This work will propose the control of an induction machine in field coordinates with imposed stator current based on theory of variable structure control and sliding mode. We describe the model of an induction machine in field coordinates with imposed stator current and we show the design of variable structure control and sliding mode to get a desirable dynamic performance of that plant. To estimate the inaccessible states we will use a state observer (estimator) based on field coordinates induction machine. We will present the results of simulations in any operation condition (start, speed reversal and load) and with parameters variation of the machine compared to a PI control scheme.

MODEL-BASED COMPENSATION FOR BURST MESSAGE LOSS IN WIRELESS NETWORKED CONTROL SYSTEMS: EXPERIMENTAL RESULTS

A recent trend in networked control systems (NCSs) is the use of wireless networks enabling interoperability between existing wired and wireless systems. One of the major challenges in these wireless NCSs (WNCSs) is to overcome the impact of the message loss that degrades the performance and stability of these systems. Moreover, this impact is greater when dealing with burst or successive message losses. This paper discusses and presents the experimental results of a compensation strategy to deal with this burst message loss problem in which a NCS mathematical model runs in parallel with the physical process, providing sensor virtual data in case of packet losses. Running in real-time inside the controller, the mathematical model is updated online with real control signals sent to the actuator, which provides better reliability for the estimated sensor feedback (virtual data) transmitted to the controller each time a message loss occurs. In order to verify the advantages of applying this model-based compensation strategy for burst message losses in WNCSs, the control performance of a motor control system using CAN and ZigBee networks is analyzed. Experimental results led to the conclusion that the developed compensation strategy provided robustness and could maintain the control performance of the WNCS against different message loss scenarios.

Digital controller design considering hardware constraints: application in a paraplegic patient

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Robust controller design of a wheelchair mobile via LMI approach to SPR systems with feedback output

This article discusses the design of robust controller applied to Wheelchair Furniture via Linear Matrix Inequalities (LMI), to obtain Strictly Positive Real (SPR) systems. The contributions of this work were the choice of a mathematical model for wheelchair: mobile with uncertainty about the position of the center of gravity (CG), the decoupling of the kinematic and dynamical systems, linearization of the models, the headquarters building of parametric uncertainties, the proposal of the control loop and control law with a specified decay rate.

Ethanol intake-induced apoptosis in glial cells and axonal disorders in the cerebellar white matter of UChA rats (voluntary ethanol consumers)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)