Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.     

Neural identifier for unknown discrete-time nonlinear delayed systems

This work proposes a discrete-time nonlinear neural identifier based on a recurrent high-order neural network trained with an extended Kalman filter-based algorithm for discrete-time deterministic multiple-input multiple-output systems with unknown dynamics and time-delay. To prove the semi-globally uniformly ultimately boundedness of the proposed neural identifier, the stability analysis based on the Lyapunov approach is included. Applicability of the proposed identifier is shown via simulation and experimental results, all of them performed under the presence of unknown external and internal disturbances as well as unknown time-delays.     

The influence of mass transfer on the mechanism of electropolishing of nickel in aqueous sulphuric acid

The effect of mass transfer on the electropolishing of nickel in sulphuric acid solutions has been studied using a rotating disc electrode technique. Two well defined regions for different rotation rates have been observed having (a) pure diffusion control, and (b) mixed kinetic and diffusion control. The presence of a solid layer of a hydrated nickel salt covering a contaminated oxide layer is proposed. The rate of chemical attack of the underlying layer appears to depend on the SO^2?₄ ion concentration for H₂SO₄ concentrations greater than 8 M. When the rate of mass transfer is increased, changes in the morphology of the surface are observed, polishing gives way to levelling, and finally either to metallographic etching or corrosion. The disappearance of electropolishing is observed in general when the rate of mass transfer is sufficient to eliminate from the surface the solid layer of hydrated nickel sulphate, and the results indicate a coupling between diffusional rates in the solution and ionic transport through the oxide or contaminated oxide phases.     

Decentralized discrete-time neural control for a Quanser 2-DOF helicopter

Control design for helicopters is a complicated and challenging problem due to the strong inter-couplings and nonlinear uncertainties in the system model. This paper deals with the decentralized control problem for the output trajectory tracking in a Quanser 2 degree of freedom (DOF) helicopter. High order neural network (HONN) is an important technique to approximate non-linearities in the model. Two different discrete-time schemes with a decentralized structure are used. Neural backstepping and neural sliding mode block control techniques are considered in order to control pitch and yaw positions. On one hand, backstepping control divides the whole system into two subsystems which are used to track the pitch and yaw references respectively. Real and virtual controls are approximated by HONNs. On the other hand, block control technique is applied to HONNs which can identify the system helicopter model. Each discrete-time high order neural network is trained on-line with an extended Kalman filter based algorithm. Without the previous knowledge of the plant parameters neither its model, we show via simulations the good performance of both strategies. The block control technique presents slightly better results than backstepping algorithm.     

OPTIMAL CONDITIONS TO OBTAIN HIGH PURITY SINTERED TeO2 USEFUL AS A THERMAL NEUTRON TARGET FOR THE I-131 INDUSTRIAL PRODUCTION

This paper describes the optimal conditions of temperature, reaction rate, drying and sintering to obtain high purity TeO₂ crystals where the retention of I-131 produced by Te-131 radioactive decay is efficient. The chemical reaction used is oxidation of metallic Te with concentrated HNO₃ in presence of an airflow with heat applied to the reaction chamber. After drying and purifying by further healing, TeO₂ is sintered, i.e., is heated to its melting point and held for a few minutes. This creates cavities inside the crystalline structure, where gaseous I-131 produced by Te-131 radioactive decay must be adsorbed, after the sintered TeO₂ has been bombarded with thermal neutrons in a nuclear reactor. The general procedure of I-131 production is also described. This includes melting of radioactive sintered TeO₂ in a hermetic box, pumping out and retention of I-131 in an alkaline solution keeping the necessary safety conditions     

The Role of Personality in Motivation to use an Affective Feedback System

Programming and Computer Software - Motivation is an essential element in the learning process. In computer-based tutoring environments, motivational components are as significant as cognitive...     

Neural inverse optimal control applied to type 1 diabetes mellitus patients

Inverse optimal trajectory tracking via a control Lyapunov function (CLF) for discrete time non-linear systems is developed and applied to type 1 diabetes mellitus patients control. The control law calculates the insulin delivery rate in order to prevent hyperglycemia and hypoglycemia levels. To synthesize the inverse optimal control law a quadratic candidate CLF is used. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results applied for two different patients illustrate the applicability of the control law and a comparison with inverse optimal neural control via passivity is included.     

Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback

This paper proposes a neural control integrating stereo vision feedback for driving a mobile robot. The proposed approach consists in synthesizing a suitable inverse optimal control to avoid solving the Hamilton Jacobi Bellman equation associated to nonlinear system optimal control. The mobile robot dynamics is approximated by an identifier using a discrete-time recurrent high order neural network, trained with an extended Kalman filter algorithm. The desired trajectory of the robot is computed during navigation using a stereo camera sensor. Simulation and experimental result are presented to illustrate the effectiveness of the proposed control scheme.     

Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms

A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.