Elias B. Kosmatopoulos

High-order neural network structures for identification of dynamical systems

Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems. In recurrent high-order neural networks, the dynamic components are distributed throughout the network in the form of dynamic neurons. It is shown that if enough high-order connections are allowed then this network is capable of approximating arbitrary dynamical systems. Identification schemes based on high-order network architectures are designed and analyzed.

Effects of Variable Speed Limits on Motorway Traffic Flow

Variable speed limits (VSLs) displayed on roadside variable message signs (VMSs) have emerged as a widespread traffic control measure on motorways in many countries, leading to substantial traffic safety benefits; however, there is no clear evidence of improved traffic flow efficiency in operational VSL systems. The available information on VSL impact on aggregate traffic flow behavior is summarized, and the issue is investigated in more detail with real traffic data from a European motorway. It is found that VSLs decrease the slope of the flow–occupancy diagram at undercritical conditions, shift the critical occupancy to higher values, and enable higher flows at the same occupancy values in over-critical conditions. Implications of the derived findings for more efficient VSL control strategies are discussed.

Collision avoidance analysis for lane changing and merging

One of the riskiest maneuvers that a driver has to perform in a conventional highway system is to merge into the traffic and/or to perform a lane changing maneuver. Lane changing/merging collisions are responsible for one-tenth of all crash-caused traffic delays often resulting in congestion. Traffic delays and congestion, in general, increase travel time and have a negative economic impart. We analyze the kinematics of the vehicles involved in a lane changing/merging maneuver, and study the conditions under which lane changing/merging crashes can be avoided. That is, given a particular lane change/merge scenario, we calculate the minimum longitudinal spacing which the vehicles involved should initially have so that no collision, of any type, takes place during the maneuver. Simulations of a number of examples of lane changing maneuvers are used to demonstrate the results. The results of this paper could be used to assess the safety of lane changing maneuvers and provide warnings or take evasive actions to avoid collision when combined with appropriate hardware on board of vehicles.

Development of adaptive modeling techniques for non-linear hysteretic systems

Abstract Adaptive estimation procedures have gained significant attention by the research community to perform real-time identification of non-linear hysteretic structural systems under arbitrary dynamic excitations. Such techniques promise to provide real-time, robust tracking of system response as well as the ability to track time variation within the system being modeled. An overview of some of the authors’ previous work in this area is presented, along with a discussion of some of the emerging issues being tackled with regard to this class of problems. The trade-offs between parametric-based modeling and non-parametric modeling of non-linear hysteretic dynamic system behavior are discussed. Particular attention is given to (1) the effects of over- and under-parameterization on parameter convergence and system output tracking performance, (2) identifiability in multi-degree-of-freedom structural systems, (3) trade-offs in setting user-defined parameters for adaptive laws, and (4) the effects of noise on measurement integration. Both simulation and experimental results indicating the performance of the parametric and non-parametric methods are presented and their implications are discussed in the context of adaptive structures and structural health monitoring.

Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments

Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.

A flow-maximizing adaptive local ramp metering strategy

An extension of the feedback local ramp metering strategy ALINEA is proposed that allows the automatic tracking of the critical occupancy to help maximize the mainstream flow. The developed AD-ALINEA strategy may be valuable whenever the critical occupancy cannot be estimated beforehand or is subject to real-time change due to changing environmental conditions or traffic composition (e.g. trucks). An upstream-measurement based version of the adaptive strategy (AU-ALINEA) is also developed. Both strategies are successfully tested in a stochastic macroscopic simulation environment under various scenarios.

Structural properties of gradient recurrent high-order neural networks

The structural properties of Recurrent High-Order Neural Networks (RHONN) whose weights are restricted to satisfy the symmetry property, are investigated. First, it is shown that these networks are gradient and stable dynamical systems and moreover, they remain stable when either bounded deterministic or multiplicative stochastic disturbances concatenate their dynamics. Then, we prove that such networks are capable of approximating arbitrarily close, a large class of dynamical systems of the form /spl chi//spl dot/=F(/spl chi/). Appropriate learning laws, that make these neural networks able to approximate (identify) unknown dynamical systems are also proposed. The learning laws are based on Lyapunov stability theory, and they ensure error stability and robustness. >

Robust switching adaptive control of multi-input nonlinear systems

During the last decade a considerable progress has been made in the design of stabilizing controllers for nonlinear systems with known and unknown constant parameters. New design tools such as adaptive feedback linearization, adaptive back-stepping, control Lyapunov functions (CLFs) and robust control Lyapunov functions (RCLFs), nonlinear damping and switching adaptive control have been introduced. Most of the results developed are applicable to single-input feedback-linearizable systems and parametric-strict-feedback systems. These results, however, cannot be applied to multi-input feedback-linearizable systems, parametric-pure-feedback systems and systems that admit a linear-in-the-parameters CLF. In this paper, we develop a general procedure for designing robust adaptive controllers for a large class of multi-input nonlinear systems. This class of nonlinear systems includes as a special case multi-input feedback-linearizable systems, parametric-pure-feedback systems and systems that admit a linear-in-the-parameters CLF. The proposed approach uses tools from the theory of RCLF and the switching adaptive controllers proposed by the authors for overcoming the problem of computing the feedback control law when the estimation model becomes uncontrollable. The proposed control approach has also been shown to be robust with respect to exogenous bounded input disturbances.

A switching adaptive controller for feedback linearizable systems

One of the main open problems in the area of adaptive control of linear-in-the-parameters feedback linearizable systems is the computation of the feedback control law when the identification model becomes uncontrollable. The authors propose a switching adaptive control strategy that overcomes this problem. The proposed strategy is applied to nth-order feedback linearizable systems in canonical form. The closed-loop system is proved to be globally stable in the sense that all the closed-loop signals are bounded and the tracking error converges arbitrarily close to zero. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. However, the proposed controller requires knowledge of the sign and lower bound of the input vector field.

Large Scale Nonlinear Control System Fine-Tuning Through Learning

Despite the continuous advances in the fields of intelligent control and computing, the design and deployment of efficient large scale nonlinear control systems (LNCSs) requires a tedious fine-tuning of the LNCS parameters before and during the actual system operation. In the majority of LNCSs the fine-tuning process is performed by experienced personnel based on field observations via experimentation with different combinations of controller parameters, without the use of a systematic approach. The existing adaptive/neural/fuzzy control methodologies cannot be used towards the development of a systematic, automated fine-tuning procedure for general LNCS due to the strict assumptions they impose on the controlled system dynamics; on the other hand, adaptive optimization methodologies fail to guarantee an efficient and safe performance during the fine-tuning process, mainly due to the fact that these methodologies involve the use of random perturbations. In this paper, we introduce and analyze, both by means of mathematical arguments and simulation experiments, a new learning/adaptive algorithm that can provide with convergent, an efficient and safe fine-tuning of general LNCS. The proposed algorithm consists of a combination of two different algorithms proposed by Kosmatopoulos (2007 and 2008) and the incremental-extreme learning machine neural networks (I-ELM-NNs). Among the nice properties of the proposed algorithm is that it significantly outperforms the algorithms proposed by Kosmatopoulos as well as other existing adaptive optimization algorithms. Moreover, contrary to the algorithms proposed by Kosmatopoulos , the proposed algorithm can operate efficiently in the case where the exogenous system inputs (e.g., disturbances, commands, demand, etc.) are unbounded signals.