The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

A comprehensive review for industrial applicability of artificial neural networks

Bayesian Data Analysis (2nd ed.) (Book)

Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

https://www.mdpi.com/1996-1073/11/7/1820/pdf

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a /spl sigma/-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Neural network-based model reference adaptive control system

In this paper, an adaptive controller for robot manipulators which uses neural networks is presented. The proposed control scheme is based on PD feedback plus a feedforward compensation of full robot dynamics. The feedforward signal is obtained by summing up the weighted outputs of a set of fixed multilayer neural nets. The controller is adaptive to robot dynamics and payload uncertainties. A stability analysis which takes into account neural network learning errors is included. Simulation results showing the feasibility and performance of the approach are given. >

A neural network based feedforward adaptive controller for robots

In the present work, a design of an automatic irrigation neuro-controller for precision agriculture is presented. The irrigation neuro-controller regulates the level of moisture in agricultural soils, specifically in the root zone, using an on-off control-type that opens and closes the valves of the irrigation system (IS). The changes in the moisture levels in the roots area can be modeled as a non-linear differential function depending mainly on the amount of water supplied by the IS, the crop consumption, and the soil characteristics. This dynamic model is identified by a neural network (NN). After the NN is trained, it is used as a prediction model within the control algorithm, which determines the irrigation time necessary to take the moisture level up to a user desired level. At the same time, the NN is re-trained in order to get a new and improved model of the moisture's soils, giving to the IS the capability of adapt to the changing soil characteristics and water crop needs. In this work, it is also presented the main advantages of using this irrigation closed-loop adaptive controller instead of traditional systems that operates to open-loop, such as timed irrigation control.

Neural Network-Based Irrigation Control for Precision Agriculture

Maximum battery runtime and its transients behaviors are crucial in many applications. With accurate battery models in hand, circuit designers can evaluate the performance of its developments considering the influence of a finite source of energy which has a particular dynamics; as well as the energy storage systems can be optimized. First, this work describes a complete dynamic model of a lithium polymer battery. In the sequel a simple and novel procedure is used to obtain the electric parameters of adopted model with the advantage of using only one resistor to represent the battery load and a pc-connected multimeter. The methodology used to identify the parameters of the battery model is simple, clearly explained and can be applied to various types of batteries. Simulation and experimental results are presented and discussed, demonstrating the good performance of the proposed identification methodology.

Dynamic model of lithium polymer battery – Load resistor method for electric parameters identification

In this work, a trajectory tracking control design is proposed for the planar vertical takeoff and landing (PVTOL) aircraft using linear algebra theory. The resulting control law is implemented easily since the equation to be solved is not complex. The tracking is achieved providing convergence of the tracking errors to zero, and simulation results show the good performance of the proposed controller.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/asjc.819

Trajectory Tracking Control of a PVTOL Aircraft Based on Linear Algebra Theory

This paper proposes a new control law based on linear algebra. This technique allows nonlinear path tracking in multivariable and complex systems. This new methodology consists in finding the control action to make the system follow predefined concentration profiles solving a system of linear equations. The controller parameters are selected with a Monte Carlo algorithm so as to minimize a previously defined cost index. The control scheme is applied to a fed-batch penicillin production process. Different tests are shown to prove the controller effectiveness, such as adding parametric uncertainty, perturbations in the control action and in the initial conditions. Moreover, a comparison with other controllers from the literature is made, showing the better performance of the present approach.

Daniel Patino

Papers

A neural network based feedforward adaptive controller for robots

Neural Network-Based Irrigation Control for Precision Agriculture

Dynamic model of lithium polymer battery – Load resistor method for electric parameters identification

Trajectory Tracking Control of a PVTOL Aircraft Based on Linear Algebra Theory

A New Approach for Nonlinear Multivariable Fed-Batch Bioprocess Trajectory Tracking Control