Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 5 Articles
This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the\r\nmotor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot\r\nmanipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the\r\nmass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis\r\nfunction neural network (RBF). Experiments are done using a real robot�s arm, and trajectory data are gathered during various\r\ntrials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates\r\nall the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control\r\nand lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven\r\ncontrol scheme....
Uncertain constrained discrete-time linear system is addressed using linear matrix inequality based optimization techniques. The\r\nconstraints on the inputs and states are specified as quadratic constraints but are formulated to capture hyperplane constraints as\r\nwell. The control action is of state feedback and satisfies the constraints. Uncertainty in the system is represented by unknown\r\nbounded disturbances and system perturbations in a linear fractional transform (LFT) representation. Mixed H2/H8 method is\r\napplied in a model predictive control strategy. The control law takes account of disturbances and uncertainty naturally. The validity\r\nof this approach is illustrated with two examples....
In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material\r\nand information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root\r\ncause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key\r\nfeatures of the process description, need to be captured from process knowledge and process data. The modelling methods from\r\nthese two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs,\r\nrule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and\r\nits extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these\r\nmodels, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future\r\nwork is proposed in the end....
We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a\r\nneural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure\r\nthe stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also\r\nproven....
Tracking filter design is discussed. It is argued that the basis of the present stochastic paradigm is questionable. White process\r\nnoise is not adequate as a model for target manoeuvring, stochastic least-square optimality is not relevant or required in practice,\r\nthe fact that requirements are necessary for design is ignored, and root mean square (RMS) errors are insufficient as performance\r\nmeasure. It is argued that there is no process noise and that the covariance of the assumed process noise contains the design\r\nparameters. Focus is on the basic tracking filter, the Kalman filter, which is convenient for clarity and simplicity, but the arguments\r\nand conclusions are relevant in general. For design the possibility of an observer transfer function approach is pointed out. The\r\nissues can also be considered as a consequence of the fact that there is a difference between estimation and design. The a-�Ÿ filter is\r\nused for illustration....
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