Neural network control

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.

We derive a linear neural network model of the chemotaxis control circuit in the nematode Caenorhabditis elegans and demonstrate that this model is capable of producing nematodelike chemotaxis. By expanding the analytic solution for the network output in time-derivatives of the network input, we extract simple computational rules that reveal how the model network controls chemotaxis. Based on these rules we find that optimized linear networks typically control chemotaxis by computing the first time-derivative of the chemical concentration and modulating the body turning rate in response to this derivative. We argue that this is consistent with behavioral studies and a plausible mechanism for at least one component of chemotaxis in real nematodes.

COMPARISON OF TWO NEURAL NETWORK METHODS APPLIED TO A TRACKING CONTROL SYSTEM ... Cliston Lcfr;iric, Scirior ML'III~CT, IEEE, ririd Bcdcr Cistcratis ... Department of Electrical Engineering, Catholic University of Valparaiso, Chile Fax 56-32-212746. E-mail: ...

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV

This paper investigates an efficient and robust control method for the UPFC in order to improve the stability of the power system, thus providing the security for the increased power flow. With neural networks, uncertainty or unknown variations in plant parameters and structure can be dealt with more effectively and hence improving the robustness of the control system. On the other hand control theory allows transient and steady state characteristics of the closed loop system to be specified. The effectiveness of this control structure is demonstrated under different operating conditions of the UPFC system.

This paper introduces a new technique to control spacecraft maneuvers. The new technique is based upon using neuro-fuzzy approach to predict the required control torque, using a modelless-strategy, for attitude and rate tracking subjected to torque constraints. The Neuro-Fuzzy Controller (NFC) is built up using the Adaptive Neuro-Fuzzy Inference System (ANFIS) which transforms a fuzzy controller into an adaptive network to take the advantage of all the neural network control techniques proposed in the literature. First, the inverse dynamics of the spacecraft is developed by training the ANFIS with specified states such as Euler angles and the angular velocities. These data can be collected via direct measurements, estimators, or simulation using attitude propagators. Second, three types of controllers are developed, started with a Single Level NFC (SLNFC) to a Multi Level NFC (MLNFC) and ended by a Hybrid Controller. The configuration of the first and second controllers depends on t...

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC

In this paper, the locomotion of an autonomously navigated undersea vehicle that uses vorticity control propulsion is computationally simulated. The navigation procedure employs a set of vehicle geometric and state variables to predict the needed vehicle body deformations in order to pass through a set of predefined path-points. To simulate the movement of the vehicle, a two-dimensional unsteady potential flow solver was developed based on the unsteady panel method coupled with the vehicle dynamics. The developed flow solver was validated against published computational results of unsteady flow standard test cases. Then, another set of properly planned test cases to cover the range of possible conditions were processed with the simulation and the output data was subsequently used to train a Multi-Layer Perceptron (MLP) neural network. The trained network can predict what body deflection time history is necessary for the vehicle to pass through the given path-points. Several autonomo...

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV

COMPARISON OF TWO NEURAL NETWORK METHODS APPLIED TO A TRACKING CONTROL SYSTEM ... Cliston Lcfr;iric, Scirior ML'III~CT, IEEE, ririd Bcdcr Cistcratis ... Department of Electrical Engineering, Catholic University of Valparaiso, Chile Fax 56-32-212746. E-mail: ...

In this paper, a neural network control based on optimal quadratic regulators is developed for the stabilization of constrained nonlinear robotic systems. This method of robotic control is performed by adding an optimal control, generated from the dynamics of the position error, to a neural control, estimated through a three layers neural network. Solving an algebraic Riccati equation, solutions of

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC