Designs for an adaptive tuned vibration absorber with variable shape stiffness element

An adaptive tuned vibration absorber (ATVA) with a smart variable stiffness element is capable of retuning itself in response to a time-varying excitation frequency., enabling effective vibration control over a range of frequencies. This paper discusses novel methods of achieving variable stiffness in an ATVA by changing shape, as inspired by biological paradigms. It is shown that considerable variation in the tuned frequency can be achieved by actuating a shape change, provided that this is within the limits of the actuator. A feasible design for such an ATVA is one in which the device offers low resistance to the required shape change actuation while not being restricted to low values of the effective stiffness of the vibration absorber. Three such original designs are identified: (i) A pinned-pinned arch beam with fixed profile of slight curvature and variable preload through an adjustable natural curvature; (ii) a vibration absorber with a stiffness element formed from parallel curved beams of adjustable curvature vibrating longitudinally; (iii) a vibration absorber with a variable geometry linkage as stiffness element. The experimental results from demonstrators based on two of these designs show good correlation with the theory.

Preliminary results from a real time control system using optical information

Successful results from training an adaptive controller to use optical information to balance an inverted pendulum are presented in comparison to the training requirements using traditional controller inputs. Results from research into the psychology of the sense of balance in humans are presented as the motivation for the investigation of this new type of controller. The simulated model of the inverted pendulum and the virtual reality environments used to provide the optical input are described The successful introduction of optical information is found to require the preservation of at least two of the traditional input types and entail increased training time for the adaptive controller and reduced performance (measured as the time the pendulum remains upright).

Results of applying sensor fusion to a control system using optic flow

The results from applying a sensor fusion process to an adaptive controller used to balance all inverted pendulum axe presented. The goal of the sensor fusion process was to replace some of the four mechanical measurements, which are known to be sufficient inputs for a linear state feedback controller to balance the system, with optic flow variables. Results from research into the psychology of the sense of balance in humans were the motivation for the investigation of this new type of controller input. The simulated model of the inverted pendulum and the virtual reality environments used to provide the optical input are described. The successful introduction of optical information is found to require the preservation of at least two of the traditional input types and entail increased training till-le for the adaptive controller and reduced performance (measured as the time the pendulum remains upright)

Parallelism in control

A parallel structure is suggested for feedback control systems. Such a technique can be applied to either single or multi-sensor environments and is ideally suited for parallel processor implementation. The control input actually applied is based on a weighted summation of the different parallel controller values, the weightings being either fixed values or chosen by an adaptive decision-making mechanism. The effect of different controller combinations is a field now open to study.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

A critique of neural networks for discrete-time linear control

This paper discusses the use of multi-layer perceptron networks for linear or linearizable, adaptive feedback.control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parametrization. A comparison is made with standard, non-perceptron algorithms, e.g. self-tuning control, and it is shown how gross over-parametrization can occur in the neural network case. Because of the resultant heavy computational burden and poor controller convergence, a strong case is made against the use of neural networks for discrete-time linear control.

Adaptive general predictive controller for nonlinear systems

A nonlinear general predictive controller (NLGPC) is described which is based on the use of a Hammerstein model within a recursive control algorithm. A key contribution of the paper is the use of a novel, one-step simple root solving procedure for the Hammerstein model, this being a fundamental part of the overall tuning algorithm. A comparison is made between NLGPC and nonlinear deadbeat control (NLDBC) using the same one-step nonlinear components, in order to investigate NLGPC advantages and disadvantages.

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

Practical neural networks in a computer control package

This paper deals with the integration of radial basis function (RBF) networks into the industrial software control package Connoisseur. The paper shows the improved modelling capabilities offered by RBF networks within the Connoisseur environment compared to linear modelling techniques such as recursive least squares. The paper also goes on to mention the way this improved modelling capability, obtained through the RBF networks will be utilised within Connoisseur.

A method to weight three categories of adaptive thermal comfort

The adaptive thermal comfort theory considers people as active rather than passive recipients in response to ambient physical thermal stimuli, in contrast with conventional, heat-balance-based, thermal comfort theory. Occupants actively interact with the environments they occupy by means of utilizing adaptations in terms of physiological, behavioural and psychological dimensions to achieve ‘real world’ thermal comfort. This paper introduces a method of quantifying the physiological, behavioural and psychological portions of the adaptation process by using the analytic hierarchy process (AHP) based on the case studies conducted in the UK and China. Apart from three categories of adaptations which are viewed as criteria, six possible alternatives are considered: physiological indices/health status, the indoor environment, the outdoor environment, personal physical factors, environmental control and thermal expectation. With the AHP technique, all the above-mentioned criteria, factors and corresponding elements are arranged in a hierarchy tree and quantified by using a series of pair-wise judgements. A sensitivity analysis is carried out to improve the quality of these results. The proposed quantitative weighting method provides researchers with opportunities to better understand the adaptive mechanisms and reveal the significance of each category for the achievement of adaptive thermal comfort.

Results of a DSP based adaptive cancellation filter system for the chirp, pulsar and localiser sirens

Sirens’ used by police, fire and paramedic vehicles generate noise that propagates inside the vehicle cab that subsequently corrupts intelligibility of voice communications from the emergency vehicle to the control room. It is even common for the siren to be turned off to enable the control room to hear what is being said. Both fixed filter and adaptive filter systems have previously been developed to help cancel the transmission of the siren noise over the radio. Previous cancellation systems have only concentrated on the traditional 2-tone, wail and yelp sirens. This paper discusses an improvement to a previous adaptive filter system and presents the cancellation results to three new types of sirens; being chirp pulsar and localiser. A siren noise filter system has the capability to improve the response time for an emergency vehicle and thus help save lives. To date, this system has been tested using live recordings taken from a nonemergency situation with good results.

Gradient recovery in adaptive finite-element methods for parabolic problems

We derive energy-norm a posteriori error bounds, using gradient recovery (ZZ) estimators to control the spatial error, for fully discrete schemes for the linear heat equation. This appears to be the �rst completely rigorous derivation of ZZ estimators for fully discrete schemes for evolution problems, without any restrictive assumption on the timestep size. An essential tool for the analysis is the elliptic reconstruction technique.Our theoretical results are backed with extensive numerical experimentation aimed at (a) testing the practical sharpness and asymptotic behaviour of the error estimator against the error, and (b) deriving an adaptive method based on our estimators. An extra novelty provided is an implementation of a coarsening error "preindicator", with a complete implementation guide in ALBERTA in the appendix.

Switching control of sympathetic activity from forebrain to hindbrain in chronic dehydration

We investigated the mechanisms responsible for increased blood pressure and sympathetic nerve activity (SNA) caused by 2-3 days dehydration (DH) both in vivo and in situ preparations. In euhydrated (EH) rats, systemic application of the AT(1) receptor antagonist Losartan and subsequent pre-collicular transection (to remove the hypothalamus) significantly reduced thoracic (t) SNA. In contrast, in DH rats, Losartan, followed by pre-collicular and pontine transections, failed to reduce tSNA, whereas transection at the medulla-spinal cord junction massively reduced tSNA. In DH but not EH rats, selective inhibition of the commissural nucleus tractus solitarii (cNTS) significantly reduced tSNA. Comparable data were obtained in both in situ and in vivo (anaesthetized/conscious) rats and suggest that following chronic dehydration, the control of tSNA transfers from supra-brainstem structures (e. g. hypothalamus) to the medulla oblongata, particularly the cNTS. As microarray analysis revealed up-regulation of AP1 transcription factor JunD in the dehydrated cNTS, we tested the hypothesis that AP1 transcription factor activity is responsible for dehydration-induced functional plasticity. When AP1 activity was blocked in the cNTS using a viral vector expressing a dominant negative FosB, cNTS inactivation was ineffective. However, tSNA was decreased after pre-collicular transection, a response similar to that seen in EHrats. Thus, the dehydration-induced switch in control of tSNA from hypothalamus to cNTS seems to be mediated via activation of AP1 transcription factors in the cNTS. If AP1 activity is blocked in the cNTS during dehydration, sympathetic activity control reverts back to forebrain regions. This unique reciprocating neural structure-switching plasticity between brain centres emphasizes the multiple mechanisms available for the adaptive response to dehydration.