Distributed power control algorithm for multiple access systems based on Verhulst model

In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

New control algorithm for single-phase series active power filter

This paper presents a single-phase Series Active Power Filter (Series APF) for mitigation of the load voltage harmonic content, while maintaining the voltage on the DC side regulated without the support of a voltage source. The proposed series active power filter control algorithm eliminates the additional voltage source to regulate the DC voltage, and with the adopted topology it is not used a coupling transformer to interface the series active power filter with the electrical power grid. The paper describes the control strategy which encapsulates the grid synchronization scheme, the compensation voltage calculation, the damping algorithm and the dead-time compensation. The topology and control strategy of the series active power filter have been evaluated in simulation software and simulations results are presented. Experimental results, obtained with a developed laboratorial prototype, validate the theoretical assumptions, and are within the harmonic spectrum limits imposed by the international recommendations of the IEEE-519 Standard.

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.

Application of a novel optimal control algorithm to a benchmark fed-batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Personalized rule-based closed-loop control algorithm for type 1 diabetes

Type 1 diabetes-mellitus implies a life-threatening absolute insulin deficiency. Artificial pancreas (CGM sensor, insulin pump and control algorithm) is promising to outperform current open-loop therapies.

Simulation and Implementation of a PMSM Sensorless Control Algorithm for Submarine Applications

In this thesis, the study and the simulation of two advanced sensorless speed control techniques for a surface PMSM are presented. The aim is to implement a sensorless control algorithm for a submarine auxiliary propulsion system. This experimental activity is the result of a project collaboration with L3Harris Calzoni, a leader company in A&D systems for naval handling in military field. A Simulink model of the whole electric drive has been developed. Due to the satisfactory results of the simulations, the sensorless control system has been implemented in C code for STM32 environment. Finally, several tests on a real brushless machine have been carried out while the motor was connected to a mechanical load to simulate the real scenario of the final application. All the experimental results have been recorded through a graphical interface software developed at Calzoni.

Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory

This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.

Self-tuning algorithm for dsp-based motion control in cnc applications

This thesis describes the development of an adaptive control algorithm for Computerized Numerical Control (CNC) machines implemented in a multi-axis motion control board based on the TMS320C31 DSP chip. The adaptive process involves two stages: Plant Modeling and Inverse Control Application. The first stage builds a non-recursive model of the CNC system (plant) using the Least-Mean-Square (LMS) algorithm. The second stage consists of the definition of a recursive structure (the controller) that implements an inverse model of the plant by using the coefficients of the model in an algorithm called Forward-Time Calculation (FTC). In this way, when the inverse controller is implemented in series with the plant, it will pre-compensate for the modification that the original plant introduces in the input signal. The performance of this solution was verified at three different levels: Software simulation, implementation in a set of isolated motor-encoder pairs and implementation in a real CNC machine. The use of the adaptive inverse controller effectively improved the step response of the system in all three levels. In the simulation, an ideal response was obtained. In the motor-encoder test, the rise time was reduced by as much as 80%, without overshoot, in some cases. Even with the larger mass of the actual CNC machine, decrease of the rise time and elimination of the overshoot were obtained in most cases. These results lead to the conclusion that the adaptive inverse controller is a viable approach to position control in CNC machinery.

Modeling of Distributed Control Systems in Intelligent Building based on Colored Petri Nets

Distributed control systems consist of sensors, actuators and controllers, interconnected by communication networks and are characterized by a high number of concurrent process. This work presents a proposal for a procedure to model and analyze communication networks for distributed control systems in intelligent building. The approach considered for this purpose is based on the characterization of the control system as a discrete event system and application of coloured Petri net as a formal method for specification, analysis and verification of control solutions. With this approach, we develop the models that compose the communication networks for the control systems of intelligent building, which are considered the relationships between the various buildings systems. This procedure provides a structured development of models, facilitating the process of specifying the control algorithm. An application example is presented in order to illustrate the main features of this approach.

Specification of Supervisory Control Systems for Ventricular Assist Devices

One of the most important recent improvements in cardiology is the use of ventricular assist devices (VADs) to help patients with severe heart diseases, especially when they are indicated to heart transplantation. The Institute Dante Pazzanese of Cardiology has been developing an implantable centrifugal blood pump that will be able to help a sick human heart to keep blood flow and pressure at physiological levels. This device will be used as a totally or partially implantable VAD. Therefore, an improvement on device performance is important for the betterment of the level of interaction with patient`s behavior or conditions. But some failures may occur if the device`s pumping control does not follow the changes in patient`s behavior or conditions. The VAD control system must consider tolerance to faults and have a dynamic adaptation according to patient`s cardiovascular system changes, and also must attend to changes in patient conditions, behavior, or comportments. This work proposes an application of the mechatronic approach to this class of devices based on advanced techniques for control, instrumentation, and automation to define a method for developing a hierarchical supervisory control system that is able to perform VAD control dynamically, automatically, and securely. For this methodology, we used concepts based on Bayesian network for patients` diagnoses, Petri nets to generate a VAD control algorithm, and Safety Instrumented Systems to ensure VAD system security. Applying these concepts, a VAD control system is being built for method effectiveness confirmation.

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Modelado, simulación y control digital de autogiros

Este proyecto consiste en diseñar el algoritmo de control de un autogiro no tripulado. Su aplicación principal es llevar a cabo tareas rutinarias o peligrosas para el piloto como, por ejemplo, extinción de incendios, evaluación de riesgo químico o vigilancia de lugares de acceso restringido. Se realiza un estudio del movimiento del vehículo para obtener su modelo dinámico. A partir de las ecuaciones que describen su movimiento, se realiza una simulación numérica del vehículo. Se incorpora el controlador diseñado y se evalúa su funcionamiento. Finalmente, se implementa el sistema en un microcontrolador.

FPGA-Based Control Design for Power Electronic Applications

Tehoelektoniikkalaitteella tarkoitetaan ohjaus- ja säätöjärjestelmää, jolla sähköä muokataan saatavilla olevasta muodosta haluttuun uuteen muotoon ja samalla hallitaan sähköisen tehon virtausta lähteestä käyttökohteeseen. Tämä siis eroaa signaalielektroniikasta, jossa sähköllä tyypillisesti siirretään tietoa hyödyntäen eri tiloja. Tehoelektroniikkalaitteita vertailtaessa katsotaan yleensä niiden luotettavuutta, kokoa, tehokkuutta, säätötarkkuutta ja tietysti hintaa. Tyypillisiä tehoelektroniikkalaitteita ovat taajuudenmuuttajat, UPS (Uninterruptible Power Supply) -laitteet, hitsauskoneet, induktiokuumentimet sekä erilaiset teholähteet. Perinteisesti näiden laitteiden ohjaus toteutetaan käyttäen mikroprosessoreja, ASIC- (Application Specific Integrated Circuit) tai IC (Intergrated Circuit) -piirejä sekä analogisia säätimiä. Tässä tutkimuksessa on analysoitu FPGA (Field Programmable Gate Array) -piirien soveltuvuutta tehoelektroniikan ohjaukseen. FPGA-piirien rakenne muodostuu erilaisista loogisista elementeistä ja niiden välisistä yhdysjohdoista.Loogiset elementit ovat porttipiirejä ja kiikkuja. Yhdysjohdot ja loogiset elementit ovat piirissä kiinteitä eikä koostumusta tai lukumäärää voi jälkikäteen muuttaa. Ohjelmoitavuus syntyy elementtien välisistä liitännöistä. Piirissä on lukuisia, jopa miljoonia kytkimiä, joiden asento voidaan asettaa. Siten piirin peruselementeistä voidaan muodostaa lukematon määrä erilaisia toiminnallisia kokonaisuuksia. FPGA-piirejä on pitkään käytetty kommunikointialan tuotteissa ja siksi niiden kehitys on viime vuosina ollut nopeaa. Samalla hinnat ovat pudonneet. Tästä johtuen FPGA-piiristä on tullut kiinnostava vaihtoehto myös tehoelektroniikkalaitteiden ohjaukseen. Väitöstyössä FPGA-piirien käytön soveltuvuutta on tutkittu käyttäen kahta vaativaa ja erilaista käytännön tehoelektroniikkalaitetta: taajuudenmuuttajaa ja hitsauskonetta. Molempiin testikohteisiin rakennettiin alan suomalaisten teollisuusyritysten kanssa soveltuvat prototyypit,joiden ohjauselektroniikka muutettiin FPGA-pohjaiseksi. Lisäksi kehitettiin tätä uutta tekniikkaa hyödyntävät uudentyyppiset ohjausmenetelmät. Prototyyppien toimivuutta verrattiin vastaaviin perinteisillä menetelmillä ohjattuihin kaupallisiin tuotteisiin ja havaittiin FPGA-piirien mahdollistaman rinnakkaisen laskennantuomat edut molempien tehoelektroniikkalaitteiden toimivuudessa. Työssä on myösesitetty uusia menetelmiä ja työkaluja FPGA-pohjaisen säätöjärjestelmän kehitykseen ja testaukseen. Esitetyillä menetelmillä tuotteiden kehitys saadaan mahdollisimman nopeaksi ja tehokkaaksi. Lisäksi työssä on kehitetty FPGA:n sisäinen ohjaus- ja kommunikointiväylärakenne, joka palvelee tehoelektroniikkalaitteiden ohjaussovelluksia. Uusi kommunikointirakenne edistää lisäksi jo tehtyjen osajärjestelmien uudelleen käytettävyyttä tulevissa sovelluksissa ja tuotesukupolvissa.