An accurate simulation of a heating coil is used to compare the performance of a proportional plus integral (PI) controller, a neural network trained to predict the steady-state output of the PI controller, a neural network trained to minimize the n-step ahead error between the coil output and the set point, and a reinforcement learning agent trained to minimize the sum of the squared error ove...

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural ...

This paper proposes a neural controller to guide a nonholonomic mobile robot during trajectory tracking. The structure of the controller used consists of two models that describe the kinematical mobile robot system. These models are modified Elman neural networks (MENN) and feed forward multi-layer perceptron (MLP). The modified Elman neural networks model is trained with two stages; off-line a...

In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed a...

A model-free "teaching by showing" methodology is developed to train a fuzzy-neural controller for an autonomous robot helicopter. The controller is generated and tuned using training data gathered while a teacher operates the helicopter. A hierarchical behavior-based control architecture is used, with each behavior implemented as a hybrid fuzzy logic controller (FLC) and general regression neu...

This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based on Recurrent Hybrid Networks (RHNs). The NIMC, an alternative to the basic inverse control scheme, consists of a forward internal neural model of robot, a neural controller and a conventional feedback controller. An Alopex Learning Algorithm (ALA) was used to adjust weights of the proposed neural ...

This paper presents the design of a Neural Network based approach to improve efficiency in a vector control of Permanent Magnet synchronous Motors (PMSM). The conventional Proportional-Integral (PI) controller is largely used in industry because of the robustness this regulator procures. But in some case, when the dynamics of the system vary over time or with operating conditions, the performan...

A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integralderivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. Th...

This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanentmagnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed c...

In this paper, a nonlinear controller is proposed to handle some nonlinear control problems. In this scheme, the controller uses the system error, the integral of the system error, and the derivative of the system error as its inputs but the mapping from the inputs to the output is nonlinear. The corresponding nonlinear mapping may be specified based on the control requirement. Therefore, the p...