Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Automatic tuning of PID controllers using model reference adaptive control techniques

This paper discusses the application of model reference adaptive control concepts to the automatic tuning of PID controllers. The effectiveness of the proposed method is shown through simulated applications. The gradient approach and simulated examples are provided.

PID autotuning using neural networks and model reference adaptive control

This paper describes the application of artificial neural networks for automatic tuning of PID controllers using the Model Reference Adaptive Control approach. The effectiveness of the proposed method is shown through a simulated application.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Bifurcation Analysis of Eigenstructure Assignment Control in a Simple Nonlinear Aircraft Model

Aircraft systems are highly nonlinear and time varying. High-performance aircraft at high angles of incidence experience undesired coupling of the lateral and longitudinal variables, resulting in departure from normal controlled � ight. The construction of a robust closed-loop control that extends the stable and decoupled � ight envelope as far as possible is pursued. For the study of these systems, nonlinear analysis methods are needed. Previously, bifurcation techniques have been used mainly to analyze open-loop nonlinear aircraft models and to investigate control effects on dynamic behavior. Linear feedback control designs constructed by eigenstructure assignment methods at a � xed � ight condition are investigated for a simple nonlinear aircraft model. Bifurcation analysis, in conjunction with linear control design methods, is shown to aid control law design for the nonlinear system.

Applying robust control theory to solve problems in bio-medical sciences: study of an apoptotic model

Biological models of an apoptotic process are studied using models describing a system of differential equations derived from reaction kinetics information. The mathematical model is re-formulated in a state-space robust control theory framework where parametric and dynamic uncertainty can be modelled to account for variations naturally occurring in biological processes. We propose to handle the nonlinearities using neural networks.

Exercise improves cardiovascular control in a model of dislipidemia and menopause

The present study investigated the effects of exercise training on arterial pressure, baroreflex sensitivity, cardiovascular autonomic control and metabolic parameters on female LDL-receptor knockout ovariectomized mice. Mice were divided into two groups: sedentary and trained. Trained group was submitted to an exercise training protocol. Blood cholesterol was measured. Arterial pressure (AP) signals were directly recorded in conscious mice. Baroreflex sensitivity was evaluated by tachycardic and bradycardic responses to AP changes. Cardiovascular autonomic modulation was measured in frequency (FFT) and time domains. Maximal exercise capacity was increased in trained as compared to sedentary group. Blood cholesterol was diminished in trained mice (191 +/- 8 mg/dL) when compared to sedentary mice (250 +/- 9 mg/dL, p<0.05). Mean AP and HR were reduced in trained group (101 +/- 3 mmHg and 535 +/- 14 bpm, p<0.05) when compared with sedentary group (125 +/- 3 mmHg and 600 +/- 12 bpm). Exercise training induced improvement in bradycardic reflex response in trained animals (-4.24 +/- 0.62 bpm/mmHg) in relation to sedentary animals (-1.49 +/- 0.15 bpm/mmHg, p<0.01); tachycardic reflex responses were similar between studied groups. Exercise training increased the variance (34 +/- 8 vs. 6.6 +/- 1.5 ms(2) in sedentary, p<0.005) and the high-frequency band (HF) of the pulse interval (IP) (53 +/- 7% vs. 26 +/- 6% in sedentary, p<0.01). It is tempting to speculate that results of this experimental study might represent a rationale for this non-pharmacological intervention in the management of cardiovascular risk factors in dyslipidemic post-menopause women. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Bifurcation analysis of a model for biological control

In this paper we study the Lyapunov stability and Hopf bifurcation in a biological system which models the biological control of parasites of orange plantations. (c) 2007 Elsevier Ltd. All rights reserved.

An economic model of gun control

Guns stolen from law-abiding households provide the principal source of guns for criminals. The lethality of crime instruments increases with the availability of guns, so the gun market is subject to externalities that generate excessive ownership and inadequate spending on protective measures to deter gun theft. One motive for gun ownership is self defense, and the gun market is subject to coordination failure: the more guns purchased lawfully, the more will be stolen by criminals, so the greater the incentive for lawful . consumers to purchase guns for self defense. As a result, there may be multiple equilibria in the gun market and more than one equilibrium crime rate. We show that a simple refundable deposit for guns will internalize the externalities in the gun market and may cause large downward jumps in gun ownership, the lethality of crime instruments, and the social costs of crime.

Double sampling (x)over-bar control chart for a first-order autoregressive moving average process model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)