A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

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.

Tuning the self-assembly of the bioactive dipeptide L-carnosine by incorporation of a bulky aromatic substituent

The dipeptide L-carnosine has a number of important biological properties. Here, we explore the effect of attachment of a bulky hydrophobic aromatic unit, Fmoc [N-(fluorenyl-9-methoxycarbonyl)] on the self-assembly of Fmoc-L-carnosine, i.e., Fmoc-Beta-alanine-histidine (Fmoc-BetaAH). It is shown that Fmoc-BetaAH forms well-defined amyloid fibril containing Beta sheets above a critical aggregation concentration, which is determined from pyrene and ThT fluorescence experiments. Twisted fibrils were imaged by cryogenic transmission electron microscopy. The zinc-binding properties of Fmoc-BetaAH were investigated by FTIR and Raman spectroscopy since the formation of metal ion complexes with the histidine residue in carnosine is well-known, and important to its biological roles. Observed changes in the spectra may reflect differences in the packing of the Fmoc-dipeptides due to electrostatic interactions. Cryo-TEM shows that this leads to changes in the fibril morphology. Hydrogelation is also induced by addition of an appropriate concentration of zinc ions. Our work shows that the Fmoc motif can be employed to drive the self-assembly of carnosine into amyloid fibrils.

Simplified PID self-tuning controller for robotic manipulators

The paper describes a self-tuning adaptive PID controller suitable for use in the control of robotic manipulators. The scheme employs a simple recursive estimator which reduces the computational effort to an acceptable level for many applications in robotics.

Self-tuning property of state-space self tuners

The basic assumption from implicit self-tuning theory is that, for self tuning to occur, the control input obtained from the estimated system model converges to the value whic would be obtained if the system parameters were known. As as direct result of this, only certain control strategies are acceptable. Here a general rule for the self-tuning property of pole-placement self tuners is obtained, and previous strategies are shown to be special cases of this.

Decentralized PID self-tuning control of industrial robots

A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

Reduced complexity set-point following in self-tuning control

An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.

Implementation of microprocessor based self-tuning control on a heating system

This paper describes the implementation, using a microprocessor, of a self-tuning control algorithm on a heating system. The algorithm is based on recursive least squares parameter estimation with a state-space, pole placement design criterion and shows how the controller behaves when applied to an actual system.

Simulating the effects of grassland management and grass ensiling on methane emission from lactating cows

A dynamic, mechanistic model of enteric fermentation was used to investigate the effect of type and quality of grass forage, dry matter intake (DMI) and proportion of concentrates in dietary dry matter (DM) on variation in methane (CH(4)) emission from enteric fermentation in dairy cows. The model represents substrate degradation and microbial fermentation processes in rumen and hindgut and, in particular, the effects of type of substrate fermented and of pH oil the production of individual volatile fatty acids and CH, as end-products of fermentation. Effects of type and quality of fresh and ensiled grass were evaluated by distinguishing two N fertilization rates of grassland and two stages of grass maturity. Simulation results indicated a strong impact of the amount and type of grass consumed oil CH(4) emission, with a maximum difference (across all forage types and all levels of DM 1) of 49 and 77% in g CH(4)/kg fat and protein corrected milk (FCM) for diets with a proportion of concentrates in dietary DM of 0.1 and 0.4, respectively (values ranging from 10.2 to 19.5 g CH(4)/kg FCM). The lowest emission was established for early Cut, high fertilized grass silage (GS) and high fertilized grass herbage (GH). The highest emission was found for late cut, low-fertilized GS. The N fertilization rate had the largest impact, followed by stage of grass maturity at harvesting and by the distinction between GH and GS. Emission expressed in g CH(4)/kg FCM declined oil average 14% with an increase of DMI from 14 to 18 kg/day for grass forage diets with a proportion of concentrates of 0.1, and on average 29% with an increase of DMI from 14 to 23 kg/day for diets with a proportion of concentrates of 0.4. Simulation results indicated that a high proportion of concentrates in dietary DM may lead to a further reduction of CH, emission per kg FCM mainly as a result of a higher DM I and milk yield, in comparison to low concentrate diets. Simulation results were evaluated against independent data obtained at three different laboratories in indirect calorimetry trials with COWS consuming GH mainly. The model predicted the average of observed values reasonably, but systematic deviations remained between individual laboratories and root mean squared prediction error was a proportion of 0.12 of the observed mean. Both observed and predicted emission expressed in g CH(4)/kg DM intake decreased upon an increase in dietary N:organic matter (OM) ratio. The model reproduced reasonably well the variation in measured CH, emission in cattle sheds oil Dutch dairy farms and indicated that oil average a fraction of 0.28 of the total emissions must have originated from manure under these circumstances.

Outgoing longwave radiation due to directly transmitted surface emission

A frequently used diagram summarizing the annual- and global-mean energy budget of the earth and atmosphere indicates that the irradiance reaching the top of the atmosphere from the surface, through the midinfrared atmospheric window, is 40 W m−2; this can be compared to the total outgoing longwave radiation (OLR) of about 235 W m−2. The value of 40 W m−2 was estimated in an ad hoc manner. A more detailed calculation of this component, termed here the surface transmitted irradiance (STI), is presented, using a line-by-line radiation code and 3D climatologies of temperature, humidity, cloudiness, etc. No assumption is made as to the wavelengths at which radiation from the surface can reach the top of the atmosphere. The role of the water vapor continuum is highlighted. In clear skies, if the continuum is excluded, the global- and annual-mean STI is calculated to be about 100 W m−2 with a broad maximum throughout the tropics and subtropics. When the continuum is included, the clear-sky STI is reduced to 66 W m−2, with a distinctly different geographic distribution, with a minimum in the tropics and local peaks over subtropical deserts. The inclusion of clouds reduces the STI to about 22 W m−2. The actual value is likely somewhat smaller due to processes neglected here, and an STI value of 20 W m−2 (with an estimated uncertainty of about ±20%) is suggested to be much more realistic than the previous estimate of 40 W m−2. This indicates that less than one-tenth of the OLR originates directly from the surface.