Dynamic anti-windup based control method for state constrained systems

Based on the dynamic anti-windup strategy, an alternative control methodology for state constrained systems is presented. The proposed method is an a posteriori approach for state constrained systems, which is differentiated from the usual a priori approach. That is, first a linear controller is designed to show a desirable nominal performance by ignoring state constraints. Then, an additional compensator is introduced to account for state constraints. By minimizing a reasonable performance index, a dynamic compensator is derived explicitly, which is expressed in plant and controller parameters. The proposed method not only provides a graceful performance degradation, but it also guarantees the total stability of the resulting systems. An illustrative example is given to show the effectiveness of the proposed method.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

Improved estimation of defocus blur and spatial shifts in spatial domain: a homotopy-based approach

This paper presents a homotopy-based algorithm for the recovery of depth cues in the spatial domain. The algorithm specifically deals with defocus blur and spatial shifts, that is 2D motion, stereo disparities and/or zooming disparities. These cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space. We show that they can be simultaneously computed by resolving a system of equations using a homotopy method. The proposed algorithm is tested using synthetic and real images. The results confirm that the use of a homotopy method leads to a dense and accurate estimation of depth cues. This approach has been integrated into an application for relief estimation from remotely sensed images.     

A passivity approach to controller design for quasi-resonant converters

In this paper, the passivity-based approach to controller design for the boost and buck quasi-resonant converters is addressed. Quasi-resonant converters use a soft switching method where the power devices switch at zero voltage or zero current, resulting in low switching loss and high power density. The controller design is carried out using the generalized state space average equations of these converter circuits and the celebrated “energy shaping plus damping injection” ideas of the passivity-based approach. Both direct and indirect output voltage regulation schemes are studied. Simulation results are presented to illustrate the features of the proposed controllers.     

Using sign algebra for qualitative spatial reasoning about the configuration of mechanisms

This paper presents a novel approach to qualitative spatial reasoning about the spatial configuration of mechanisms that could be applied in the earlier phases of mechanism design. The basic idea in this approach is to predict all possible spatial configurations of a mechanism using sign algebra. This paper addresses spatial configuration problems of direction, position, and orientation of mechanisms. Spatial configuration matrices are introduced to describe and manipulate the spatial information of primitive mechanisms. Using these matrices, the overall spatial configurations of a complicated mechanism are constructed and then they are expressed by spatial configuration state matrices. These matrices are composed based on sign algebra and used to reason about all possible spatial solutions of a mechanism. The qualitative spatial reasoning proposed in this paper could provide useful prior information on the spatial configuration of mechanisms earlier in the conceptual design of mechanisms. The proposed approach is illustrated with a design example.     

Container spaces and functional features for top-down 3D layout design

Although there have been many advances in computer-aided modelling techniques and representations of mechanical parts, there are areas where exact modelling is a handicap. One of these is 3D layout design. Here, simpler models are useful for initial design sketches to verify kinematic behaviour and organise product structure before the detailed component design phase begins. A commitment to exact, or close approximational geometry too early can imply a commitment to form before functionality has been finalised. This paper describes a system for top-down 3D layout design based on simple conceptual elements which can be used as a basis for visualisation, discussion, definition of product structure and kinematic functionality in the conceptual design phase before the embodiment or detailing begins. This tool forms a bridge between the abstract nature of the conceptual design phase and the geometric nature of the embodiment phase. The 3D layout module uses design spaces with simple geometry and kinematic connections to represent a product. The design spaces act as containers or envelopes within which the final component design is to be realised. The kinematic connections allow the behaviour of the product to be simulated to gain more information (such as overall component dimensions and areas of potential collisions) for the detailed design phase. In addition the paper describes the design process based on the proposed 3D layout design system and contrasts this with the traditional design process. An industrial case study is presented to illustrate the following advantages of the proposed approach: (i) the design process proceeds faster because unnecessary layout parameter and constraint modifications are avoided since kinematic functionality verification precedes the detail design, (ii) the design process can produce better designs since alternative solution principles can be explored early in the design process. Theoretical issues are discussed concerning kinematic constraint inheritance during design space decomposition and concerning computer support for non-rigid design spaces.     

Hypergraph imaging: an overview

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation.     

On the equivalence of causal LTI iterative learning control and feedback control

The goal of iterative learning control (ILC) is to improve the accuracy of a system that repeatedly follows a reference trajectory. This paper proves that for each causal linear time-invariant ILC, there is an equivalent feedback that achieves the ultimate ILC error with no iterations. Remarkably, this equivalent feedback depends only on the ILC operators and hence requires no plant knowledge. This equivalence is obtained whether or not the ILC includes current-cycle feedback. If the ILC system is internally stable and converges to zero error, there exists an internally stabilizing feedback that approaches zero error at high gain. Since conventional feedback requires no iterations, there is no reason to use causal ILC.     

Tracking in a class of nonminimum-phase systems with nonlinear internal dynamics via sliding mode control using method of system center

A method of asymptotic output tracking in a class of causal nonminimum-phase uncertain nonlinear systems is considered. Local asymptotic stability of output tracking-error dynamics are provided for the specified class of systems with the nonlinear hyperbolic at the origin internal dynamics forced by a reference output profile and external disturbances defined by a known linear exosystem. The nonlinear vector field of the internal dynamics is expanded in a power series, obtained at a selected operational point in the internal dynamics state space. The presented technique employs some linear algebraic methods and sliding mode control approach. The solution is a complete constructive algorithm.     

Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.