Luc Baron

H ∞ model reduction for discrete-time Markov jump linear systems with partially known transition probabilities

In this article, the fault detection (FD) problem for a class of discrete-time Markov jump linear system (MJLS) with partially known transition probabilities is investigated. The proposed systems are more general, which relax the traditional assumption in Markov jump systems that all the transition probabilities must be completely known. A residual generator is constructed and the corresponding FD is formulated as an H ∞ filtering problem by which the error between residual and fault are minimised in the H ∞ sense. The linear matrix inequality-based sufficient conditions for the existence of FD filter are derived. A numerical example on a multiplier–accelerator model economic system is given to illustrate the potential of the developed theoretical results.

The direct kinematics of parallel manipulators under joint-sensor redundancy

We study, for each of the possible joint-sensor layouts, the subspaces into which the motion of the hip-attachment points of parallel manipulators are completely measured. The projection of the motion of these points onto their subspaces allows us to write the underlying direct kinematics as a linear algebraic system constrained by the proper orthogonality of the rotation matrix. Although the solution of this problem requires a nonlinear technique, we propose a linear procedure that provides what we term a polar least square estimate. The resulting procedure is fast, robust to measurement noise, and produces estimates with about the same accuracy as a nonlinear procedure.

Design of Stabilizing Controllers With a Dynamic Gain for Feedforward Nonlinear Time-Delay Systems

In this technical note, constructive control techniques have been proposed for controlling feedforward nonlinear time-delay systems. The nonlinear terms admit an incremental rate depending on the input or delayed input. Based on the Lyapunov-Razumikhin theorem and Lyapunov-Krasovskii theorem, the delay-independent feedback controllers are explicitly constructed such that the closed-loop systems are globally asymptotically stable. An example is given to demonstrate the effectiveness of the proposed design procedure.

Brief paper: Feedback stabilization for high order feedforward nonlinear time-delay systems

This paper investigates the problem of global strong stabilization by state feedback, for a family of high order feedforward nonlinear time-delay systems. The uncertain nonlinearities are assumed to satisfy a polynomial growth assumption with an input or delayed input dependent rate. With the help of the appropriate Lyapunov-Krasovskii functionals, and a rescaling transformation with a gain to be tuned online by a dynamic equation, we propose a dynamic low gain state feedback control scheme. A simulation example is given to demonstrate the effectiveness of the proposed design procedure.

Type-2 Takagi-Sugeno-Kang Fuzzy Logic Modeling using Subtractive Clustering

In this paper, a subtractive clustering identification algorithm is introduced to model type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLS). The type-2 TSK FLS identification algorithm is an extension of the type-1 TSK FLS modeling algorithm proposed in (S. L. Chiu, 1994), (S. L. Chiu, 1997). In the type-2 algorithm, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 FLS form input/output data. Then using type-2 TSK FLS theory (J. M. Mendel, 2001), expand the type-1 FLS to a type-2 TSK FLS. Minimum error models are obtained through enumerative search of optimum values for spreading percentage of cluster centers and consequence parameters. By doing so, fuzzy modeling of type-2 TSK FLS is found to be more effective than that of type-1 TSK FLS. Experimental results confirm the effectiveness of this method. A comparison of the Type-1 and -2 TSK FLSs is presented and the limitations of this method are discussed

Tool wear monitoring using genetically-generated fuzzy knowledge bases

Fuzzy logic is an AI method that is being implemented in a growing number of different fields. One of these applications is tool wear monitoring. The construction of a fuzzy knowledge base from a set of experimental data by a human expert however, is a time consuming task, and hence, limits the expansion of the use of this AI method. Alternatively, the fuzzy knowledge base can be automatically constructed by a genetic algorithm from the same set of experimental data without requiring any human expert. This paper compares these two fuzzy knowledge base construction methods and the results obtained in a tool wear monitoring application.

The joint‐limits and singularity avoidance in robotic welding

Purpose – The aim of this paper is to develop a redundancy‐resolution (RR) algorithm to optimize the joint space trajectory of the six‐rotation‐axis industrial robot as performing arc‐welding tasks.Design/methodology/approach – The rotation of the tool around its symmetry axis is clearly irrelevant to the view of the task to be accomplished besides some exceptional situations. When performed with a general 6‐degrees‐of‐freedom (DOF) manipulator, there exists one DOF of redundancy that remains. By taking advantage of the symmetry axis of the welding electrode, the authors decompose the required instantaneous twist of the electrode into two orthogonal components, one lying into the relevant task subspace and one into the redundant task subspace, respectively. Joint‐limits and singularity avoidance are considered as the optimization objectives.Findings – The twist‐decomposition algorithm is able to optimize effectively the joint space trajectory. It has been tested and demonstrated in simulation.Originality/...

Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling

In this paper, a micromilling type-2 fuzzy tool condition monitoring system based on multiple AE acoustic emission signal features is proposed. The type-2 fuzzy logic system is used as not only a powerful tool to model acoustic emission signal, but also a great estimator for the ambiguities and uncertainties associated with the signal itself. Using the results of root-mean-square error estimation and the variations in the results of type-2 fuzzy modeling of all signal features, the most reliable ones are selected and integrated into cutting tool life estimation models. The obtained results show that the type-2 fuzzy tool life estimation is in accordance with the cutting tool wear state during the micromilling process. The information about uncertainty prediction of tool life is of great importance for tool condition investigation and crucial when making decisions about maintaining the machining quality.

The kinematic decoupling of parallel manipulators using joint-sensor data

In this paper we decouple the translational and rotational degrees of freedom of the end-effector of parallel manipulators, and hence, decompose the direct kinematics problem into two simpler subproblems. Most of the redundant joint-sensor layouts produce a linear decoupling equation expressing the least-square solution of position for a given orientation of the end-effector. The resulting orientation problem can be cast as a linear algebraic system constrained by the proper orthogonality of the rotation matrix. Although this problem is nonlinear, we propose a procedure that provides what we term a decoupled polar least-square estimate. The resulting procedure is fast, robust to measurement noise, and produces estimates with about the same accuracy as a procedure for nonlinear systems if sufficient redundancy is used.

TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

This paper presents an experimental study for turning process in machining by using Takagi-Sugeno-Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input-output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cutting conditions. The experimental results show its effectiveness and satisfactory comparisons relative to other artificial intelligence methods.