Monica Patrascu

A software tool for modeling and simulation of numerical P systems

UNLABELLED A P system represents a distributed and parallel bio-inspired computing model in which basic data structures are multi-sets or strings. Numerical P systems have been recently introduced and they use numerical variables and local programs (or evolution rules), usually in a deterministic way. They may find interesting applications in areas such as computational biology, process control or robotics. The first simulator of numerical P systems (SNUPS) has been designed, implemented and made available to the scientific community by the authors of this paper. SNUPS allows a wide range of applications, from modeling and simulation of ordinary differential equations, to the use of membrane systems as computational blocks of cognitive architectures, and as controllers for autonomous mobile robots. This paper describes the functioning of a numerical P system and presents an overview of SNUPS capabilities together with an illustrative example. AVAILABILITY SNUPS is freely available to researchers as a standalone application and may be downloaded from a dedicated website, http://snups.ics.pub.ro/, which includes an user manual and sample membrane structures.

HELGA: a heterogeneous encoding lifelike genetic algorithm for population evolution modeling and simulation

Today, there is a substantial need for population evolution modeling in multidisciplinary research areas, such as social sciences (sociology, anthropology etc.), which can neither be solved formally, nor empirically at global scale, thus requiring the development of heuristic techniques, like evolutionary algorithms. Therefore, existent methodologies of social simulation can be extended from microenvironments to large scale modeling of extremely complex systems, as it is the case of human evolution. The modeling of population evolution prediction is currently used in high interest areas, from migration flows, to financial crisis simulation, to the free-market economy models, as well as multi-national family dynamics and cultural aspects. The high performance of evolutionary computing has been already proven in the context of virus population evolution, be they biological or cybernetic, thus making them the perfect method for our endeavour. This paper presents the design and implementation of a soft computing application, namely HELGA—heterogeneous encoding lifelike genetic algorithm. HELGA was designed for the modeling and simulation of Earth population evolution, on a global scale, throughout multiple historical eras. The model shows the tendency of human evolution towards one mixed race. The algorithm takes into account the influence of social factors, such as the Industrial Revolution or the discovery of the New World. Our method, for which all constraints have been based on validated research, has aligned precisely to real historical events: it anticipated the end of antiquity, the industrial era, the baby boom phenomenon and so on. For example, the poor health conditions of the Middle Ages have caused a drastic drop in population size. The presented model shows the valiant result of one final race, and although this theory cannot be formally proved in the present, we consider that our results can be used as hypothesis for future social science research. Also, HELGA can be extended with new capabilities and constraints.

Emergent intelligence in agents: A scalable architecture for smart cities

The Smart City concept is the context in which a scalable agent architecture with emergent properties is introduced in this paper. These agents form on-demand control loops within the urban system, taking into account both the protection and the comfort of its inhabitants, at varying degrees of intelligence and abstraction of tasks and/or purpose. The resulting flexibility and scalability of the system allows expansion and contraction of the systemic environments, integration with emergency response units, protection of human lives, as well as day to day regular operations. The case study included in this paper illustrates the real-time creation of a control loop for an underground railway intersection system. The objective of forming the traffic light and rail switch control loops is first formulated, followed by the message exchange between devices in order to perform the reorganization of the system, in an agent based simulation scenario.

Genetically enhanced modal controller design for seismic vibration in nonlinear multi-damper configuration

Structural integrity and human safety are topics of high interest, especially in the light of technological advancements that are currently leading to implementations of smart cities. This article presents the design methodology of a modal controller for seismic vibration, when equipping building with smart devices, like magnetorheological dampers, which are inherently nonlinear. A formal-heuristic hybrid multiloop configuration is thus adopted, making use of both the maneuverability of conventional controllers and the intelligent complexity of evolutionary optimization. For validation purposes, an optimal controller is also implemented for the same structure. The two control systems’ performances are analyzed and compared, taking into account two types of earthquake disturbances and significant mass variation throughout the structure.

Evolutionary Modeling of Industrial Plants and Design of PID Controllers

This chapter brings forth the practical aspect of using genetic algorithms (GAs) in aiding PID (Proportional-Integral-Derivative) control design for real world industrial processes. Plants such water tanks, heaters, fans and motors are usually hard to tune on-site, especially after prolonged use of the equipment when degradation of performances is inevitable, while plants like seismic dampers have inherent nonlinear behaviors that make formal controller design difficult at best. Therefore, this chapter introduces a series of practical steps that can be taken by control engineers in order to (re)design viable PID controllers for their plants. This chapter describes how genetic algorithms can be applied to problems in control systems and model identification. Considering the plant inputs and outputs that can be observed during functioning, we offer a quick method for identifying model parameters, which can be used later by the genetic algorithm to find a suitable controller. While, in formal control theory, a raw estimation of the model parameters can significantly reduce the performance of a real-world system, the genetic algorithm method can find suitable controllers quickly and efficiently, offering access even to performance criteria that is hard to quantify in classical design procedure, such as integral indexes. The applicability of the GAs in real world problems is outlined through case studies that take into account the particularities of each system, from first to second order responses, the absence or presence of time delay, nonlinearities, constraints and controller performance. The steps performed in the case studies show how GAs have made the jump from their origins to a practicing engineer’s toolbox (GAOT-ECM in this case, a Genetic Algorithm Optimization Toolbox Extension for Control and Modeling). Moreover, a comprehensive analysis is performed, that takes into account both the various performance criteria, and the tuning parameters of the genetic algorithm, over the obtained models and controllers. The influence of the GA parameters is discussed in order to help practitioners choose the best suited GA configuration for their particular problem. In all, this chapter offers a comprehensive step-by-step application of genetic algorithms in industrial setting, from plant modeling to controller design.

Tuning of PID Controllers for Non-Linear MIMO Systems Using Genetic Algorithms

Abstract The proposed control strategy presented in this paper makes use of conventional control algorithms in combination with an evolutionary optimization technique. The resulting strategy is applied to a non-linear MIMO system and several implementations of genetic algorithms are tested. Results show better performance when tuning conventional interdependent control loops by means of genetic algorithm optimization. The authors present the implementation of a data acquisition and control system for a 3-dimensional crane.

Nonlinear fuzzy control of human heart rate during aerobic endurance training with respect to significant model variations

In the field of physical activity a significant importance is held by aerobic endurance training, which is a relatively low intensity exercise that depends primarily on aerobic energy generating processes. This type of training is used for the overall endurance and fitness of the body by engaging all the major systems of the body and pushing the limits of their functions with the ultimate goal of adapting these structures to the stress of the physical effort and thus improving the performance output. However, using classic training methods, it is often impossible to ensure the necessary operating points in order to obtain the desired results. The human heart rate model with respect to the velocity of walking or running presents inherent nonlinearities that are required to be taken into account when designing computer integrated training aids. This paper implements such a tool using a fuzzy control system for human heart rate during aerobic endurance training. The controller is tested and validated using two nonlinear human-on-treadmill models, and it presents robustness to significant model variations, as well as uncertainties in the model parameters and white noise type of disturbances. The designed control system ensures desired heart rate profiles, finding its usefulness in supporting the training configuration process by specialized professionals.

Applying Agent Based Simulation to the Design of Traffic Control Systems with Respect to Real-World Urban Complexity

The problem of reducing traffic congestion in a city has always been difficult to solve with monolithic control methods, which have both high costs and increased implementation complexity. This paper aims to minimize vehicle waiting time at stoplights by using a multi-agent system control technology. Moreover, the system is required to respond adequately to the presence of emergency intervention vehicles, allowing them quick and sure passage, but without significantly interrupting regular traffic. The solution designed in this paper allows for on demand synchronization of intersections, depending on the traffic context at any given time. In order to test this concept, an agent based simulation model has been developed, that offers real world traffic simulations on urban maps, and integrated complex road networks and traffic participant behaviour, with a possibility to measure the performance of the control system through parameters such as noise levels and emissions.

Real time agent based simulation for smart city emergency protocols

This work proposes a holistic perspective on a coordination strategy for an Intelligent Operation Centre (IOC) at city level that accounts for the implementation of critical intervention protocols. The proposed solution depicts an agent based simulation scenario, demonstrating the real-time integration of large amounts of data into a decision making process. Based on this type of analysis, leaders have the possibility to analyze data for better decisions, anticipate problems to resolve them proactively and coordinate resources to operate effectively. The proposed agent-based coordination solution is validated through an Agent-Based oriented simulation (ABS) of specific behavioural scenarios. These scenarios account for emergency response in case of city wide critical situations in a larger perspective of a scalable multi-agent system whose main task is human life protection.

Hybrid geno-fuzzy controller for seismic vibration control

This paper evaluates the possibility of applying a geno-fuzzy control strategy to a magnetorheological semi-active damper for seismic vibration control. The proposed control strategy is designed and then tested and validated in a simulated environment. The control strategy is validated by considering a more destructive seismic disturbance as input for the damper-structure system. The proposed geno-fuzzy hybrid controller offers improved performance and implementability for real-time applications.