Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Free-roaming dogs control activities in one Italian province (2000-2013): Is the implemented approach effective?

In Italy, standards for the management of free-roaming dogs (FRDs) are defined by regional norms, generating a high variability of approaches around the country. Despite efforts carried out by the competent authorities, FRDs are still a reality impacting upon animal health and welfare and public costs. A similar scenario can be found in many other Mediterranean and Balkan counties. Here we present 14 years of data (2000–2013) retrieved from the admission dog registry of a public shelter (PS) responsible for the collection of stray dogs from one Italian province. The aim of this retrospective study was to describe the local FRD population, identifying its source and to evaluate the effectiveness of the actions implemented by the local authorities. In the investigated period, 7,475 dogs were admitted to the PS. Despite the intense sterilisation plan (mean 381.7 sterilisations per year), the overall number of dogs entering PS did not decrease consistently across the years. Results highlighted a lack of responsibility of owners by failing to sterilise and identify their dogs and allowing intact animals to roam free, therefore producing uncontrolled and unwanted litters. The current dog population management strategy, based on both sheltering and capture-neuter-release programmes, is insufficient to tackle the straying phenomenon. Educational and sterilisation programmes should be an integral part of a successfully implemented FRD control plan. Our results provide further insight on free-roaming dog population dynamics and control systems, and may have important implications for many other local contexts across Europe trying to overcome the straying phenomenon.

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements.

In this brief, a hybrid filter algorithm is developed to deal with the state estimation (SE) problem for power systems by taking into account the impact from the phasor measurement units (PMUs). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control center, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed SE problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved using the particle swarm optimization algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.

On the application of optimal control techniques in lossy fieldbuses

The performance of real-time networks is under continuous improvement as a result of several trends in the digital world. However, these tendencies not only cause improvements, but also exacerbates a series of unideal aspects of real-time networks such as communication latency, jitter of the latency and packet drop rate. This Thesis focuses on the communication errors that appear on such realtime networks, from the point-of-view of automatic control. Specifically, it investigates the effects of packet drops in automatic control over fieldbuses, as well as the architectures and optimal techniques for their compensation. Firstly, a new approach to address the problems that rise in virtue of such packet drops, is proposed. This novel approach is based on the simultaneous transmission of several values in a single message. Such messages can be from sensor to controller, in which case they are comprised of several past sensor readings, or from controller to actuator in which case they are comprised of estimates of several future control values. A series of tests reveal the advantages of this approach. The above-explained approach is then expanded as to accommodate the techniques of contemporary optimal control. However, unlike the aforementioned approach, that deliberately does not send certain messages in order to make a more efficient use of network resources; in the second case, the techniques are used to reduce the effects of packet losses. After these two approaches that are based on data aggregation, it is also studied the optimal control in packet dropping fieldbuses, using generalized actuator output functions. This study ends with the development of a new optimal controller, as well as the function, among the generalized functions that dictate the actuator’s behaviour in the absence of a new control message, that leads to the optimal performance. The Thesis also presents a different line of research, related with the output oscillations that take place as a consequence of the use of classic co-design techniques of networked control. The proposed algorithm has the goal of allowing the execution of such classical co-design algorithms without causing an output oscillation that increases the value of the cost function. Such increases may, under certain circumstances, negate the advantages of the application of the classical co-design techniques. A yet another line of research, investigated algorithms, more efficient than contemporary ones, to generate task execution sequences that guarantee that at least a given number of activated jobs will be executed out of every set composed by a predetermined number of contiguous activations. This algorithm may, in the future, be applied to the generation of message transmission patterns in the above-mentioned techniques for the efficient use of network resources. The proposed task generation algorithm is better than its predecessors in the sense that it is capable of scheduling systems that cannot be scheduled by its predecessor algorithms. The Thesis also presents a mechanism that allows to perform multi-path routing in wireless sensor networks, while ensuring that no value will be counted in duplicate. Thereby, this technique improves the performance of wireless sensor networks, rendering them more suitable for control applications. As mentioned before, this Thesis is centered around techniques for the improvement of performance of distributed control systems in which several elements are connected through a fieldbus that may be subject to packet drops. The first three approaches are directly related to this topic, with the first two approaching the problem from an architectural standpoint, whereas the third one does so from more theoretical grounds. The fourth approach ensures that the approaches to this and similar problems that can be found in the literature that try to achieve goals similar to objectives of this Thesis, can do so without causing other problems that may invalidate the solutions in question. Then, the thesis presents an approach to the problem dealt with in it, which is centered in the efficient generation of the transmission patterns that are used in the aforementioned approaches.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Temperature, pH, and conductivity control of the nutrient solution of an hydroponic greenhouse

One of the aspects of modern agriculture is characterised by a culture without soil (hydroponic cultures). These culture techniques are identified by possessing automatic control systems to control the nutrient solution. In first hydroponic cultures this control was accomplished by “on- off” analog controllers that applied a single control law implemented in hardware. Therefore, the changes of the control law resulted in the change of all interface electronics. In digital control implemented by micro-controllers the alteration of such control law is easily performed by changing only a computer program, leaving untouched all the interface hardware. In this way, the use and substitution of the control strategy is improved, as well, the use of advanced control strategies.

Adaptive generalized predictive control algorithm implemented over an heterogeneous parallel architecture

In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

Fractional order modelling of fractional-order holds

Discrete time control systems require sample- and-hold circuits to perform the conversion from digital to analog. Fractional-Order Holds (FROHs) are an interpolation between the classical zero and first order holds and can be tuned to produce better system performance. However, the model of the FROH is somewhat hermetic and the design of the system becomes unnecessarily complicated. This paper addresses the modelling of the FROHs using the concepts of Fractional Calculus (FC). For this purpose, two simple fractional-order approximations are proposed whose parameters are estimated by a genetic algorithm. The results are simple to interpret, demonstrating that FC is a useful tool for the analysis of these devices.

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

On the numerical computation of the Mittag-Leffler function

Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.

On the numerical computation of the Mittag-Leffler function

Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.

Immobilization of nitrifying bacterial consortia on wood particles for bioaugmenting nitrification in shrimp culture systems

Shrimp grow out systems under zero water exchange mode demand constant remediation of total ammonia nitrogen (TAN) andNO2 −–Nto protect the crop. To address this issue, aninexpensive and user-friendly technology using immobilized nitrifying bacterial consortia (NBC) as bioaugmentors has been developed and proposed for adoption in shrimp culture systems. Indigenous NBC stored at 4 °C were activated at room temperature (28 °C) and cultured in a 2 L bench top fermentor. The consortia, after enumeration by epifluorescence microscopy,were immobilized on delignifiedwood particles of a soft wood tree Ailantus altissima (300–1500 μm) having a surface area of 1.87m2 g−1. Selection of wood particle as substratumwas based on adsorption of NBC on to the particles, biofilm formation, and their subsequent nitrification potential. The immobilization could be achievedwithin 72 h with an initial cell density of 1×105 cells mL−1. On experimenting with the lowest dosage of 0.2 g (wet weight) immobilized NBC in 20 L seawater, a TAN removal rate of 2.4 mg L−1 within three days was observed. An NBC immobilization device could be developed for on site generation of the bioaugmentor preparation as per requirement. The product of immobilization never exhibited lag phase when transferred to fresh medium. The extent of nitrification in a simulated systemwas two times the rate observed in the control systems suggesting the efficacy in real life situations. The products of nitrification in all experiments were undetectable due to denitrifying potency, whichmade the NBC an ideal option for biological nitrogen removal. The immobilized NBC thus generated has been named TANOX (Total Ammonia Nitrogen Oxidizer)

Control de posició del robot wifibot

Aquest projecte pretén presentar de forma clara i detallada l’estructura i el funcionament del robot així com dels components que el conformen. Aquesta informació és de vital importància a l’hora de desenvolupar aplicacions per al robot. Un cop descrites les característiques del robot s’analitzaran les eines necessàries i/o disponibles per poder desenvolupar programari per cada nivell de la forma més senzilla i eficient possible. Posteriorment s’analitzaran els diferents nivells de programació i se’n contrastaran els avantatges i els inconvenients de cada un. Aquest anàlisi es començarà fent pel nivell més alt i anirà baixant amb la intenció de no entrar en nivells més baixos del necessari. Baixar un nivell en la programació suposa haver de crear aplicacions sempre compatibles amb els nivells superiors de forma que com més es baixa més augmenta la complexitat. A partir d’aquest anàlisi s’ha arribat a la conclusió que per tal d’aprofitar totes les prestacions del robot és precís arribar a programar en el nivell més baix del robot. Finalment l’objectiu és obtenir una sèrie de programes per cada nivell que permetin controlar el robot i fer-lo seguir senzilles trajectòries

Millora experimental de les lleis de control predictiu aplicades sobre la plataforma PRIM I, per el seguiment de trajectòries

Aquest projecte s’aplica sobre el robot PRIM (Plataforma Robotitzada d’Informació Multimèdia), un robot autònom no humanoide creat el 2004 per Ateneu Informàtic (AI) que permet realitzar trajectòries 2D gràcies a un sistema de tracció format per dues rodes motrius propulsades independentment. La plataforma PRIM és controlada a partir del control predictiu, aquest control es va implementar en un projecte anterior, creat per l’Alexandre Blasco Gutierrez i titulat “Implementació de tècniques MPC (Model Predictiu Control) sobre la plataforma PRIM I”. El que es pretén en aquest projecte és millorar els resultats obtinguts en el passat projecte reformulant la llei de control i analitzar les discrepàncies obtingudes en les metodologies que s’utilitzen per minimitzar la funció de costos a partir de simulacions de trajectòries