Computational grids (CGs) have become an attractive research area as they suggest a suitable environment for developing large scale parallel applications. CGs integrate a large amount of distributed heterogeneous reso...
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This paper presents a closed-loop control system for regulating blood glucose level in type-1 diabetic patients. The goals of designing controller are 1. avoidance of hypoglycemia (blood glucose concentrations should ...
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In the last decade, small-scale unmanned helicopters have attracted a significant interest due to their unique abilities. Autonomous path following of them is one of the challenging missions, especially a time-critica...
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In this paper we describe a two-step scheme for approximate model checking of discrete time stochastic hybrid systems. In the first step, the stochastic hybrid system is approximated by a finite state Markov chain. In...
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In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of i...
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Abstract A dual variational Bayes filter for states and parameter estimation in IDE based spatiotemporal dynamic systems is developed. Recursive updates are obtained from a restricted variational Bayesian perspective,...
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Abstract A dual variational Bayes filter for states and parameter estimation in IDE based spatiotemporal dynamic systems is developed. Recursive updates are obtained from a restricted variational Bayesian perspective, using a dual filtering formulation where parameters are allowed to evolve in time. The added benefit over conventional point estimate filters is that parameter distributions are readily available for one to take advantage of in the design of complex experiments or in adaptive control scenarios. The dual filter is evaluated in a simulation study and seen to perform favorably when compared to a standard SMC approach.
We study the problem of finding the minimum-length curvature constrained closed path through a set of regions in the plane. This problem is referred to as the Dubins Traveling Salesperson Problem with Neighborhoods (D...
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Abstract In this paper an anti-slip predictive controller is designed and implemented in order to control the rear wheels of a V-PRA (Variable Powered Rear Axle) vehicle. The control algorithm is EPSAC, a Model based ...
Abstract In this paper an anti-slip predictive controller is designed and implemented in order to control the rear wheels of a V-PRA (Variable Powered Rear Axle) vehicle. The control algorithm is EPSAC, a Model based Predictive control (MPC) strategy. It uses explicitly a model of the system for the prediction of the future system output, in order to calculate the optimal control input which will bring the system to a required setpoint. The main goals of this predictive controller were setpoint trajectory following, disturbance rejection and maintain overall the stability of the V-PRA vehicle. Although the process is non-linear, being influenced by several parameters, linear discrete-time ARX models could be obtained based on system linearization around an operating point. An initial SISO controller was developed and tested, based on a Simulink model of the process. The control performances were tested successfully in presence of all kind of disturbances, denoting a robust controller.
In this paper,we derive in time domain the constitutive equation and relevant properties of the fractional order weighted distributed parameter Maxwell *** weight function can be replaced by the Fourier series,which l...
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In this paper,we derive in time domain the constitutive equation and relevant properties of the fractional order weighted distributed parameter Maxwell *** weight function can be replaced by the Fourier series,which leads to three types of fractional order distributed parameter *** inverse Laplace transformand asymptotic properties
In high-dimensional modelling cases, a fuzzy modelling approach based on the grid-partitioning of fuzzy sets always meets great challenges, as it cannot avoid the problem of introducing a huge number of fuzzy rules. T...
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In high-dimensional modelling cases, a fuzzy modelling approach based on the grid-partitioning of fuzzy sets always meets great challenges, as it cannot avoid the problem of introducing a huge number of fuzzy rules. To tackle this issue, a new grid-partitioning based fuzzy modelling paradigm is proposed in this paper to construct a compact fuzzy system by including 'short fuzzy rules', in which only a few but strategic premises are used. In the proposed approach, the generation of fuzzy rules is data-orientated, a consideration which can greatly reduce the computational complexity. A new framework for fuzzy reasoning and defuzzification is also devised, which employs some archived reference data to help choose the most suitable fuzzy rules. In material engineering, describing the behaviour of mechanical properties of alloys is often a high dimensional modelling problem, which involves the complexity of materials' chemical composites and their underlying physical processing mechanisms. In this paper, the proposed approach was successfully applied to generate models of ultimate tensile strength of alloy steel. Compared with the standard grid partitioning based fuzzy modelling paradigms, the new method shows an improvement in both complexity and interpretability. Compared with the clustering-based fuzzy modelling approaches, the proposed method can achieve the same accuracy level and is more transparent.
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