A mobile-based robot, a robot with an actuator failure, a manipulator with structural flexibility, and robots grasping an articulated object are systems with unactuated or "passive" joints. The dynamics and ...
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A mobile-based robot, a robot with an actuator failure, a manipulator with structural flexibility, and robots grasping an articulated object are systems with unactuated or "passive" joints. The dynamics and control of these robot systems is more complex than fully-actuated systems because unactuated joints may lead to uncontrollability. Based on a recursive formulation of the inverse dynamics for manipulators with some unactuated joints, control laws are developed for joint space control and task space control. Important in the task space control law for underactuated systems is the inversion of the generalized Jacobian. Spatial operator algebra techniques are used to develop computationally efficient algorithms for the inverse generalized Jacobian and the control laws.
This paper considers the problem of parameter identification for rapidly time-varying systems. where the parameters are related to a measurable auxiliary variable. The parameter variation with respect to the auxiliary...
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This paper considers the problem of parameter identification for rapidly time-varying systems. where the parameters are related to a measurable auxiliary variable. The parameter variation with respect to the auxiliary variable is modelled as a polynomial and estimation is carried out by a bank of least-squares estimators each assigned to a different set of values of the auxiliary variable. This approach allows accurate tracking of rapidly varying parameters while still using long integration times to counteract noise and without the requirement for an a priori model of the time-variation.
The paper implies that even using a reduced, non-sufficient data statistic, recursive Bayesian estimation can be made consistent with the ideal full-data solution so that: (a) if the distribution of data belongs to a ...
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The paper implies that even using a reduced, non-sufficient data statistic, recursive Bayesian estimation can be made consistent with the ideal full-data solution so that: (a) if the distribution of data belongs to a given parametric family, the posterior density of unknown parameters converges to Dirac function pointing to their appropriate value, and (b) the posterior uncertainty is proportional to the loss of information caused by data compression. At the same time, the paper shows an appealing geometric interpretation of data compression (using both sufficient and non-sufficient data statistic) and subsequent posterior restoration.
A procedure for estimating the parameters of lumped parameter models (thermal networks) describing thermal properties in buildings is derived. The statistical efficient method is performed in two steps, where the firs...
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A procedure for estimating the parameters of lumped parameter models (thermal networks) describing thermal properties in buildings is derived. The statistical efficient method is performed in two steps, where the first step uses standard system identification tools for Black box modeling. The estimates from the first step are used as input data to the second step, where the parameters of a given thermal network are estimated. The method is applied to measurements from a prototype PASSYS test cell at the J.R.C. in Ispra, Italy, and the estimation results are compared with results previously presented in literature.
作者：
J. WeymannJ.-L. FargesJ.-J. HenryCERT-ONERA
Centre d'Etudes et de Recherches de Toulouse Departement d'Etudes et de Recherches en Automatique (DERA) 2 avenue Edouard Belin 31055 Toulouse Cedex France
This paper presents an optimal guidance algorithm which takes into account the driver compliance to route advices. The optimization problem consists in minimizing the travel time of guided vehicles using a model which...
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This paper presents an optimal guidance algorithm which takes into account the driver compliance to route advices. The optimization problem consists in minimizing the travel time of guided vehicles using a model which describes the traffic by a set of flows on a graph. Driver compliance is modelled by additional constraints on flow variables. This optimization problem is solved using the Simplex Algorithm, recursively An assessment of the criteria degradation induced by non-perfect compliance is performed. Results show that this degradation of the criteria is significant in heavy traffic conditions and that human factors cannot be neglected in route guidance systems.
At the heart of adaptive/predictive process control is the process identification algorithm. recursive least squares, the most popular identification algorithm, has severe limitations that restrict its use in many ind...
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At the heart of adaptive/predictive process control is the process identification algorithm. recursive least squares, the most popular identification algorithm, has severe limitations that restrict its use in many industrial applications. Several improved versions of recursive least squares have been published by various authors. Three of these algorithms are compared for robustness using computer simulations. The robustness is evaluated based on the prevention of drift and covariance blowup, and on the convergeence and tracking of parameters. The advantages and limitations of each algorithm are discussed.
This paper explains the inadequacies due to ill-conditioning of classical recursive least squares signal estimation algorithms based on Taylor series expansions, then shows how the algorithms may be restructured using...
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This paper explains the inadequacies due to ill-conditioning of classical recursive least squares signal estimation algorithms based on Taylor series expansions, then shows how the algorithms may be restructured using orthogonal expansions, at little cost in extra complexity, to provide well-conditioned versions suitable for implementation in a variety of digital signal processing applications. Several open questions are posed, mainly connected with the incorporation of signal windowing to provide smoothing filters.
The use of parameter estimation techniques in practical applications requires accurate analysis of the associated measurement and computation problems. With reference to an already proposed model identification proced...
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The use of parameter estimation techniques in practical applications requires accurate analysis of the associated measurement and computation problems. With reference to an already proposed model identification procedure, this paper deals with the experimental tests carried out in order to highlight problems and to find the most appropriate solutions. In particular, a synchronization method is described, and some suggestions concerning the optimal working conditions of all the necessary devices are reported.
In this paper, recursive algorithms are presented for the online state and parameter estimation of a linear time invariant single-input single-output (SISO) discrete-time singular system. The model considered is in th...
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In this paper, recursive algorithms are presented for the online state and parameter estimation of a linear time invariant single-input single-output (SISO) discrete-time singular system. The model considered is in the canonical observable form. The approach is based on the generalised Kalman filter and can be developed in two steps. First, the parameters are estimated by recursive least squares method. These parameters are then used to estimate the state by the generalised Kalman filter in the second step. The results are illustrated by a numerical example.
The paper establishes a geometric formulation for nonlinear parameter estimation using reduced statistics. If a reduced, rather than sufficient statistic is used in estimation, an equivalence class of densities, [p(t)...
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The paper establishes a geometric formulation for nonlinear parameter estimation using reduced statistics. If a reduced, rather than sufficient statistic is used in estimation, an equivalence class of densities, [p(t)], rather than the true posterior density p(t) is determined. Two questions arise in this connection: (1) What kind of a reduced statistic allows recursive computations? (2) What is the appropriate representative p(t) of the equivalence class [p(t)? Typically, the first question is not posed at all and the second one is resolved by heuristic considerations. The present paper attempts to cast the problem into a solid mathematical structure. It closely follows the differential-geometric approach suggested in Kulhavy (Automatica, 26, 545-555, 1990) but goes into more detail. Roughly, admissible statistics are characterized here as homomorphisms of a group containing all possible likelihoods. A representative of the pertinent equivalence class is constructed by a projection along this class onto an orthogonal submanifold going through a prespecified density p* and imbedded in the manifold of possible posteriors. The obtained projection p(t) has attractive extremal properties: it minimizes the Kullback-Leibler distance from the "reference" point p* and, at the same time, the dual Kullback-Leibler distance from the true point p(t).
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