Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

The Pondicherry interpretation of quantum mechanics: An overview

An overview of the Pondicherry interpretation of quantum mechanics is presented. This interpretation proceeds from the recognition that the fundamental theoretical framework of physics is a probability algorithm, which serves to describe an objective fuzziness (the literal meaning of Heisenberg's term "Unschaerfe," usually mistranslated as "uncertainty") by assigning objective probabilities to the possible outcomes of unperformed measurements. Although it rejects attempts to construe quantum states as evolving ontological states, it arrives at an objective description of the quantum world that owes nothing to observers or the goings-on in physics laboratories. In fact, unless such attempts are rejected, quantum theory's true ontological implications cannot be seen. Among these are the radically relational nature of space, the numerical identity of the corresponding relata, the incomplete spatiotemporal differentiation of the physical world, and the consequent top-down structure of reality, which defies attempts to model it from the bottom up, whether on the basis of an intrinsically differentiated spacetime manifold or out of a multitude of individual building blocks.Comment: 18 pages, 1 eps figure, v3: with corrections made in proo

A short survey of normative properties of possibility distributions

In 2001 Carlsson and Full´er [1] introduced the possibilistic mean value, variance and covariance of fuzzy numbers. In 2003 Full´er and Majlender [4] introduced the notations of crisp weighted possibilistic mean value, variance and covariance of fuzzy numbers, which are consistent with the extension principle. In 2003 Carlsson, Full´er and Majlender [2] proved the possibilisticCauc hy-Schwartz inequality. Drawing heavily on [1, 2, 3, 4, 5] we will summarize some normative properties of possibility distributions

The fluctuation spectra around a Gaussian classical solution of a tensor model and the general relativity

Tensor models can be interpreted as theory of dynamical fuzzy spaces. In this paper, I study numerically the fluctuation spectra around a Gaussian classical solution of a tensor model, which represents a fuzzy flat space in arbitrary dimensions. It is found that the momentum distribution of the low-lying low-momentum spectra is in agreement with that of the metric tensor modulo the general coordinate transformation in the general relativity at least in the dimensions studied numerically, i.e. one to four dimensions. This result suggests that the effective field theory around the solution is described in a similar manner as the general relativity.Comment: 29 pages, 13 figure

Evolutionary computing for metals properties modelling

This is a post print version of the article, the official published version can be obtained from the link below.During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which utilizes GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are utilized as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.Financial support from the UK EPSRC (Engineering and Physical Sciences Research Council) under grant number GR/R70514/01 was used in this study