Hugh F. Vanlandingham

Modeling and Estimation for Tracking Maneuvering Targets

A new approach to the three-dimensional airborne maneuvering target tracking problem is presented. The method, which combines the correlated acceleration target model of Singer [3] with the adaptive semi-Markov maneuver model of Gholson and Moose [8], leads to a practical real-time tracking algorithm that can be easily implemented on a modern fire-control computer. Preliminary testing with actual radar measurements indicates both improved tracking accuracy and increased filter stability in response to rapid target accelerations in elevation, bearing, and range.

Adaptive handoff algorithms for cellular overlay systems using fuzzy logic

An overlay system is a hierarchical architecture that uses large macrocells to overlay clusters of small microcells. Resource management in the overlay system is much more complex than in pure macrocell and microcell systems. A fixed parameter handoff algorithm cannot perform well in a complex and dynamic overlay environment. This paper proposes an adaptive overlay handoff algorithm that allows a systematic tradeoff among the system design parameters and improves the overall system performance.

Fusion of soft computing and hard computing in industrial applications: an overview

Soft computing (SC) is an emerging collection of methodologies which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other, but synergistic and complementary instead. Considering the number of available journal and conference papers on various combinations of these three methods, it is easy to conclude that the fusion of individual soft computing methodologies has already been advantageous in numerous applications. On the other hand, hard computing solutions are usually more straightforward to analyze; their behavior and stability are more predictable; and, the computational burden of algorithms is typically either low or moderate. These characteristics. are particularly important in real-time applications. Thus, it is natural to see SC and HC as potentially complementary methodologies. Novel combinations of different methods are needed when developing high-performance, cost-effective, and safe products for the demanding global market. We present an overview of applications in which the fusion of soft computing and hard computing has provided innovative solutions for challenging real-world problems. A carefully selected list of references is considered with evaluative discussions and conclusions.

Artificial immune systems: application to autonomous agents

The function of the immune system is to protect the living body against invaders through the use of defensive mechanisms. Some previous researchers have used artificial immune systems (AIS) to solve diverse engineering problems. The purpose of the paper is to apply the AIS technique to a distributed autonomous robotics system (DARS) problem. One of the classic problems in DARS is the dog and sheep problem. The authors try to benefit from the features of the natural immune system in the development of the dog and sheep problem. On the other hand, we find that natural immune systems are sophisticated information processors. They learn to recognize relevant patterns; they remember patterns that have been seen previously; and, they use diversity to promote robustness. Furthermore, the individual cells and molecules that comprise the immune system are distributed throughout the body, encoding and controlling the system in parallel, with no central control mechanism. The immune system uses several weapons to attack the foreign antigen. Abstractly, these weapons are the helper T-cells, B-cells, and antibodies. We simulated the dog as a B cell, the sheep as an antigen, the antibody as the dog behavior, the antigen response as the sheep behavior, and the sheep-to-pen distance as a helper T cell. The system interacts in an equivalent manner to the immune response, trying to restore the environment to its original state, which is the sheep inside the pen.

Load Transfer Control for a Gantry Crane with Arbitrary Delay Constraints

This paper describes a method to move the load of a gantry crane to a desired position in the presence of known, but arbitrary, motion-inversion delays as well as cart acceleration constraints. The method idea is based on a phase-plane analysis of the linearized model. In order to limit residual pendulation at the goal position, the method is extended to account for quadratic and cubic nonlinearities. The method of multiple scales is used to determine an approximate solution to the nonlinear equations of motion, thus providing a more accurate measure of the frequency of the oscillations. The nonlinear approach is very successful in limiting residual oscillations to very small values (less than 1 degree of amplitude), offering a reduction, with respect to the linear case, of as much as two orders of magnitude. Finally, this method offers a rationale for the future development of a controller for suppression of load oscillations in ship-mounted cranes in the presence of arbitrary delays.

Fusion of soft computing and hard computing techniques: a review of applications

Soft computing (SC) is an emerging collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it uses intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent ambiguity in human thinking with the real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other but synergistic and complementary instead, as emphasized by Dr. Zadeh . Considering the available literature, it is easy to conclude that the fusion of individual soft computing methodologies has been advantageous in numerous applications. In this paper, we give a review of applications where the fusion of soft computing and hard computing has provided innovative solutions for demanding real-world problems. A representative list of references is provided with evaluative discussions and conclusions.

Guest editorial special issue on fusion of soft computing and hard computing in industrial applications

I T IS A true honor and a great pleasure for us to be able to present a Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications in the IEEE TRANSACTIONS ONSYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS ANDREVIEWS. The roots of this Special Issue can be traced back to the SMC-98 conference that was held in La Jolla, CA. One of the highlights of that conference was the panel discussion on “New Frontiers in Information/Intelligent Systems.” Dr. Lotfi A. Zadeh was the moderator of the panel, and the panelists were all world-class scholars in the field of soft computing (SC). While the discussion was certainly stimulating and provided inspiring visions, something was missing—the dominating and continuing role of conventional hard computing (HC) in developing successful products was not recognized at all—and that made us thinking of possible technical activities on the fusionof these two principal methodologies. In this 21st century, the engineering problems are becoming increasingly demanding. Thus, a constructive fusion of all possible methodologies is needed in developing innovative and competitive solutions. The primary goal of the fusion or symbiosis of SC and HC is to create computationally efficient, highly predictable, robust, and intelligent systems. This Special Issue contains a representative collection of application papers, where the methodological fusion aspect is in a major role. A kind of “sister product” of this special issue was the recent panel discussion on the same topic, which took place in the SMCia/01 workshop. Dr. David B. Fogel moderated the American–European–Asian panel in Blacksburg, VA. One of the conclusions of the panel was that the fusion of soft computing and hard computing is certainly needed—and is already widely used—in developing intelligent systems and industrial products. Also, soft computing is like mathematics or computer programming without immediate connections to specific applications; this sets new requirements for the engineering curricula. Our Special Issue contains nine papers authored by recognized international scholars. The papers were selected by a strict peer review from those 23 manuscripts that were received for consideration from ten countries. These papers show that the fusion of soft computing and hard computing can really be advantageous when developing intelligent systems for various applications. Unfortunately, due to space limitations, we had to leave out a few high-quality contributions.

Applications of adaptive state estimation theory

Two main areas of application of adaptive state estimation theory are presented. Following a review of the basic estimation approach, its application to both the control of nonlinear plants and to the problem of tracking maneuvering targets is presented. Results are brought together from these two areas of investigation to provide insight into the wide range of possible applications of the general estimation method.

A computationally efficient technique for state estimation of nonlinear systems

Abstract An estimation technique is presented for the class of nonlinear systems consisting of memoryless nonlinearities embedded in a dynamic linear system. The approach is based on a useful sampled-data nonlinear system simulation method, which involves the addition of an extra state variable for each nonlinear element. The nonlinear estimator is developed along the lines of the basic Kalman state estimation, using quasilinearization instead of the Taylor series linearization used in extended Kalman filters. It is demonstrated that this new method out performs the extended Kalman filter in terms of the mean-square error of the state estimate. This estimator was used effectively for state estimation in cases where the extended Kalman filter does not converge. Moreover the new method is directly applicable to feedback systems with multiple nonlinearities and stochastic disturbances.

Immune network simulation of reactive control of a robot arm manipulator

The field of robotics is an important application area for artificial immune systems. We can make use of the immune system properties to control and to identify complex or even unknown systems. Much research has been done in studying the dynamics of the autonomous mobile robot with particular interest in obstacle avoidance and/or trajectory following. Our research is concerned with robot arm manipulator trajectory planning which is based on sensor-based reactive control. In order to control a robot, it must be kinematically analyzed and the result of this analysis entered into the controller of the robot. Kinematics analysis starts with calculating the forward kinematics solution (FKS). The FKS is a prerequisite for obtaining the inverse kinematics solution (IKS) that is entered into the robot controller and forms the basis of the remote control of the robot. While calculating the IKS, there are many possible poses that links of that manipulator can take to reach the designated point in the robot space. An important question, when a manipulator is dealing with a real dynamically changing environment, is what is the best pose that will get to the target point in the shortest time. We use one of the features of the immune system to build a network of manipulator joints that will interact together to reach the desired target point within the shortest time. The use of an artificial immune network simulator in this research is to play the role of prediction for robot behavior in the new environment.