Singularity-free dynamic equations of vehicle–manipulator systems

In this paper we derive the singularity-free dynamic equations of vehicle–manipulator systems using a minimal representation. These systems are normally modeled using Euler angles, which leads to singularities, or Euler parameters, which is not a minimal representation and thus not suited for Lagrange’s equations. We circumvent these issues by introducing quasi-coordinates which allows us to derive the dynamics using minimal and globally valid non-Euclidean configuration coordinates. This is a great advantage as the configuration space of the vehicle in general is non-Euclidean. We thus obtain a computationally efficient and singularity-free formulation of the dynamic equations with the same complexity as the conventional Lagrangian approach. The closed form formulation makes the proposed approach well suited for system analysis and model-based control. This paper focuses on the dynamic properties of vehicle–manipulator systems and we present the explicit matrices needed for implementation together with several mathematical relations that can be used to speed up the algorithms. We also show how to calculate the inertia and Coriolis matrices and present these for several different vehicle–manipulator systems in such a way that this can be implemented for simulation and control purposes without extensive knowledge of the mathematical background. By presenting the explicit equations needed for implementation, the approach presented becomes more accessible and should reach a wider audience, including engineers and programmers.     

Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity

Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is covered by subsolutions that are represented by a set of predictive rules, termed classifiers. The genetic algorithm is designed to evolve classifier structures that together cover the whole problem space and represent a complete problem solution. An obvious challenge for such an online evolving, distributed knowledge representation is to continuously sustain all problem subsolutions covering all problem niches, that is, to ensure niche support. Effective niche support depends both on the probability of reproduction and on the probability of deletion of classifiers in a niche. In XCS, reproduction is occurrence-based whereas deletion is support-based. In combination, niche support is assured effectively. In this paper we present a Markov chain analysis of the niche support in XCS, which we validate experimentally. Evaluations in diverse Boolean function settings, which require non-overlapping and overlapping solution structures, support the theoretical derivations. We also consider the effects of mutation and crossover on niche support. With respect to computational complexity, the paper shows that XCS is able to maintain (partially overlapping) niches with a computational effort that is linear in the inverse of the niche occurrence frequency.

Impact of performance considerations on formal specification design

Different client applications of a given functional behaviour usually have different performance requirements. Designing a formal interface specification of the functional behaviour to allow for alternative implementations, and hence, to be suitable for clients with varying performance requirements, is a challenging task. The specifier must consider ramifications of alternative designs on performance to produce a truly implementation-neutral (and hence, performance-neutral) functionality specification. This paper illustrates the influence of performance-both duration and capacity — considerations using a case study in object-based software specification. When these considerations are combined with concerns for comprehensibility and full abstraction, specifications that result that are arguably among the most desirable.     

Flapping flight for biomimetic robotic insects: part I-system modeling

This paper presents the mathematical modeling of flapping flight inch-size micro aerial vehicles (MAVs), namely micromechanical flying insects (MFIs). The target robotic insects are electromechanical devices propelled by a pair of independent flapping wings to achieve sustained autonomous flight, thereby mimicking real insects. In this paper, we describe the system dynamic models which include several elements that are substantially different from those present in fixed or rotary wing MAVs. These models include the wing-thorax dynamics, the flapping flight aerodynamics at a low Reynolds number regime, the body dynamics, and the biomimetic sensory system consisting of ocelli, halteres, magnetic compass, and optical flow sensors. The mathematical models are developed based on biological principles, analytical models, and experimental data. They are presented in the Virtual Insect Flight Simulator (VIFS) and are integrated together to give a realistic simulation for MFI and insect flight. VIFS is a software tool intended for modeling flapping flight mechanisms and for testing and evaluating the performance of different flight control algorithms.     

Discrete-time adaptive control utilizing prior information

We present schemes for discrete-time adaptive control of a linear time-invariant system, which is partially known, along with an analysis of their convergence properties.     

O-Minimal Hybrid Systems

An important approach to decidability questions for verification algorithms of hybrid systems has been the construction of a bisimulation. Bisimulations are finite state quotients whose reachability properties are equivalent to those of the original infinite state hybrid system. In this paper we introduce the notion of o-minimal hybrid systems, which are initialized hybrid systems whose relevant sets and flows are definable in an o-minimal theory. We prove that o-minimal hybrid systems always admit finite bisimulations. We then present specific examples of hybrid systems with complex continuous dynamics for which finite bisimulations exist. Date received: June 9, 1998. Date revised: June 28, 1999.     

Memory neuron networks for identification and control of dynamicalsystems

This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feedforward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems.     

Stochastic optimization over continuous and discrete variables withapplications to concept learning under noise

We consider optimization problems where the objective function is defined over some continuous and some discrete variables, and only noise corrupted values of the objective function are observable. Such optimization problems occur naturally in PAC learning with noisy samples. We propose a stochastic learning algorithm based on the model of a hybrid team of learning automata involved in a stochastic game with incomplete information to solve this optimization problem and establish its convergence properties. We then illustrate an application of this automata model in learning a class of conjunctive logic expressions over both nominal and linear attributes under noise     

Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation

Prevailing efforts to study the standard formulation of motion and structure recovery have recently been focused on issues of sensitivity and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues, we study the main aspects of motion and structure recovery: the choice of objective function, optimization techniques and sensitivity and robustness issues in the presence of noise.We clearly reveal the relationship among different objective functions, such as (normalized) epipolar constraints, reprojection error or triangulation, all of which can be unified in a new optimal triangulation procedure. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so-called essential manifold. Based on recent developments of optimization techniques on Riemannian manifolds, in particular on Stiefel or Grassmann manifolds, we propose a Riemannian Newton algorithm to solve the motion and structure recovery problem, making use of the natural differential geometric structure of the essential manifold.We provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points and the simulation results clarify the difference between the effect of bas-relief ambiguity, rotation and translation confounding and other types of local minima. This leads to consistent interpretations of simulation results over a large range of signal-to-noise ratio and variety of configurations.