An Introduction to MultiAgent Systems.

A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations.

Efficient least squares adaptive algorithms for FIR transversal filtering

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims’ conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

In this paper,observer design for a class of wide-existing nonlinear systems is considered. The convergent condition of observing error is developed. Then,based on eigenstructure assignment theory,design procedure is given with a simple example to demonstrate the proposed method.

Observer Design for a Class of Nonlinear Systems

A method and system for processing subband signals using adaptive filters is provided. The system is implemented on an oversampled WOLA filterbank. Inputs signals are oversampled. The system includes an adaptive filter for each subband, and the functionality of improving the convergence properties of the adaptive filter. For example, the convergence property is improved by whitening the spectra of the oversampled subband signals and/or affine projection algorithm. The system is applicable to echo and/or noise cancellation. Adaptive step size control, adaptation process control using Double-Talk detector may be implemented. The system may further implement a non-adaptive processing for reducing uncorrelated noise and/or cross-talk resistant adaptive noise cancellation.

Method and system for processing subband signals using adaptive filters

This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms. >

A set of algorithms linking NLMS and block RLS algorithms

Range limitation of communication links among robots in a multi-robot network, mixed with the mobility of the robots, creates a potential threat of disconnection of the communication network due to the robots movements. Recently, local methods have been proposed for maintaining the initial graph of connections, as well as global methods for maintaining a path between each pair of robots. This letter compares these methods in different aspects: the freedom of movement and the workspace provided by global methods is challenged by the amount of data needed to be exchanged with the local methods. Also, in the delayed networks, it is shown that local methods are leading to less restriction on the speed of the robots involved in the network. Simulation and numerical results are provided to further illustrate this comparison.

A Comparison Between Decentralized Local and Global Methods for Connectivity Maintenance of Multi-Robot Networks

This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To diagnose the type, size, time and fault conditions, are used analytical approach and neural network for tracking fault developing online. The neuro-fuzzy nets are compared with some other identification methods in application of power plant gas turbine. Faults are considered in two forms, step, and ramp shape. This work was implemented with real data from gas turbine of Kazeroun (Iran) power plant (Mitsubishi unit) and result is presented to demonstrate the benefits of the proposed method.

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

In this paper, a new Fault Detection (FD) scheme based on combination of switching observer and Bond Graph (BG) method for linear continuous time switched systems with Average Dwell Time (ADT) approach is proposed. The proposed scheme is a BG-based two-stage FD system in which the compact state space representation and Global Analytical Redundancy Relations (GARRs) are derived based on the BG model. In the first stage, a switched observer is designed considering disturbance attenuation level, fault sensitivity and rapid fault detection criteria by solving a weighted Linear Matrix Inequality (LMI) optimization problem. Next, a new form of GARRs which is based on output estimation error of the observer and is called Error-based Global Analytical Redundancy Relations (EGARRs) is developed to combine the observer and BG method. The output estimation errors from the observer, which are adequately sensitive to faults and simultaneously the effects of disturbances are attenuated therein, are given to the EGARRs to generate the residuals of the FD system. The proposed method may be used for fault detection of switched linear systems with ADT based on the BG model of the system. Finally, two case studies including a two-tank system and a buck converter-driven DC motor are considered to show the efficiency and real-time implementation of the proposed method.

Fault Detection of continuous time linear switched systems using combination of Bond Graph method and switching observer.

In this paper we have done a survey over control and management challenges in modern power systems today in terms of emerging distributed power generation (DG), microgrid management, electricity marketing and power quality control toward smart grid. Intelligent agent and multiagent systems (MAS) technology are introduced, then properties and more potential benefits of MAS for power systems such as scalability, expertise and grid-computing are discussed to address MAS is a appropriate solution for power engineering challenges.

Mohsen Montazeri

Papers

A set of algorithms linking NLMS and block RLS algorithms

A Comparison Between Decentralized Local and Global Methods for Connectivity Maintenance of Multi-Robot Networks

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

Fault Detection of continuous time linear switched systems using combination of Bond Graph method and switching observer.

Decentralized control and management systems for power industry via multiagent systems technology