Recursive methods for estimating the radial basis function‐based state‐dependent autoregressive model

Identifying a nonlinear radial basis function‐based state‐dependent autoregressive (RBF‐AR) time series model is the basis for solving the corresponding prediction and control problems. This paper studies some recursive parameter estimation algorithms for the RBF‐AR model. Considering the difficulty of the nonlinear optimal problem arising in estimating the RBF‐AR model, an overall forgetting gradient algorithm is deduced based on the negative gradient search. A numerical method with a forgetting factor is provided to solve the problem of determining the optimal convergence factor. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is applied to develop an overall multi‐innovation forgetting gradient (O‐MIFG) algorithm. The simulation results indicate that the estimation model based on the O‐MIFG algorithm can capture the dynamics of the RBF‐AR model very well.     

Reliable exponential filtering for singular Markovian jump systems with time‐varying delays and sensor failures

This paper deals with the problem of exponential filtering for singular Markovian jump systems with time‐varying delays subject to sensor failures. The main objective is to design a reliable filtering such that the considered filtering error system in the presence of a time‐varying delay and sensor failures is mean‐square exponentially admissible with a specified decay rate and simultaneously satisfies an performance. First, the delay interval is partitioned into m subintervals and a novel mode‐dependent stochastic Lyapunov‐Krasovskii functional is constructed. By using the reciprocally convex inequality in each subinterval, sufficient conditions of exponential performance analysis are developed for the considered filtering error system. Then, based on these conditions, the existence conditions of the desired reliable filter are derived and the filter parameters are obtained. It should be mentioned that all the results presented here are not only dependent on the time delay but also dependent on the decay rate and the partitioning size. Furthermore, all the conditions are established in terms of strict linear matrix inequalities. Finally, two numerical examples are given to illustrate the less conservatism and effectiveness of the proposed methods.     

Two‐stage auxiliary model gradient‐based iterative algorithm for the input nonlinear controlled autoregressive system with variable‐gain nonlinearity

This article focuses on the parameter estimation problem of the input nonlinear system where an input variable‐gain nonlinear block is followed by a linear controlled autoregressive subsystem. The variable‐gain nonlinearity is described analytical by using an appropriate switching function. According to the gradient search technique and the auxiliary model identification idea, an auxiliary model‐based stochastic gradient algorithm with a forgetting factor is presented. For the sake of improving the parameter estimation accuracy, an auxiliary model gradient‐based iterative algorithm is proposed by utilizing the iterative identification theory. To further optimize the performance of the algorithm, we decompose the identification model of the system into two submodels and derive a two‐stage auxiliary model gradient‐based iterative (2S‐AM‐GI) algorithm by using the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms and show that the 2S‐AM‐GI algorithm has higher identification efficiency compared with the other two algorithms.     

Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model

This paper focuses on the parameter estimation problems of output error autoregressive systems and output error autoregressive moving average systems (i.e., the Box–Jenkins systems). Two recursive least squares parameter estimation algorithms are proposed by using the data filtering technique and the auxiliary model identification idea. The key is to use a linear filter to filter the input–output data. The proposed algorithms can identify the parameters of the system models and the noise models interactively and can generate more accurate parameter estimates than the auxiliary model based recursive least squares algorithms. Two examples are given to test the proposed algorithms.     

Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory

For Hammerstein output-error autoregressive systems, a decomposition based multi-innovation stochastic gradient (D-MISG) identification algorithm and a data filtering based multi-innovation stochastic gradient (F-MISG) identification algorithm are derived by means of the key-term separation principle and the multi-innovation identification theory. The D-MISG algorithm uses the decomposition technique to transform a Hammerstein system into two subsystems and requires less computational cost, and the F-MISG algorithm uses a linear filter to filter the input-output data and has a higher estimation accuracy for larger innovation lengths. The simulation results show that the proposed two algorithm can give satisfactory parameter estimates.     

Tourism multi‐sided platforms and the social innovation trajectory: The case of Airbnb

Multi‐sided platforms (MSPs) have recently gained significant attention due to their disruptive innovation capacity, which is introducing deep changes in several industries. Previous work has studied how MSPs grow and design the development of their business models (BMs). However, none of the previous work has studied how these MSPs innovate their BM by leveraging and implementing social innovations. In this paper we aim at filling this gap. In particular, we focus on the tourism sector and, using the theoretical lens of business model innovation (BMI), we explore the Airbnb case study with a qualitative approach. We identify three main stages that characterized the innovation of the Airbnb's BM. Each of them presents specific peculiarities that have been analyzed. Our study offers new insights to understand how the BM of the MSPs is evolving toward a more sophisticated one that includes also a deeper orientation toward sustainable and social goals.     

Sensor allocation with guaranteed exponential stability for linear multi‐rate sampled‐data systems

This paper addresses sensor allocation with guaranteed exponential stability for linear multi‐rate sampled‐data systems. It is assumed that a continuous‐time linear plant is exponentially stabilized by a continuous‐time linear controller. Given sensors with incommensurate sampling rates, the objective is to allocate each state to a sensor such that the resulting multi‐rate sampled‐data system remains exponentially stable. The main contributions of this paper are twofold. First, we propose sufficient Krasovskii‐based conditions to partition the state vector among sensors such that exponential stability of the closed‐loop system is guaranteed. Second, the problem of finding a partition that guarantees exponential stability is cast as a mixed integer program subject to linear matrix inequalities. The theoretical results are successfully applied to two robotic problems: path‐following in unicycles and hovering in quadrotors. Copyright © 2015 John Wiley & Sons, Ltd.     

Exponential H∞ filtering for switched linear systems with interval time‐varying delay

This paper deals with the problem of exponential H_∞ filtering for a class of continuous‐time switched linear system with interval time‐varying delay. The time delay under consideration includes two cases: one is that the time delay is differentiable and bounded with a constant delay‐derivative bound, whereas the other is that the time delay is continuous and bounded. Switched linear filters are designed to ensure that the filtering error systems under switching signal with average dwell time are exponentially stable with a prescribed H_∞ noise attenuation level. Based on the free‐weighting matrix approach and the average dwell technology, delay‐dependent sufficient conditions for the existence of such a filter are derived and formulated in terms of linear matrix inequalities (LMIs). By solving that corresponding LMIs, the desired filter parameterized matrices and the minimal average dwell time are obtained. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed results. Copyright © 2008 John Wiley & Sons, Ltd.     

A straightforward design technique for narrowband multi‐stage low‐noise amplifiers with I/O conjugate match

Low‐noise amplifier (LNA) designers often struggle to simultaneously satisfy gain, noise, stability, and I/O matching requirements. In this article, a novel design technique, tailored for two‐stage low‐noise amplifiers, is presented. The proposed design method is completely deterministic and exploits inductive source degeneration to obtain a two‐stage LNA featuring perfect input and output match together with low noise figure (NF) and a pre‐determined gain, including stability analysis. A novel flowchart is provided together with the corresponding design chart that contains gain, matching, and stability information, therefore addressing all key figures‐of‐merit of a linear amplifier. The design chart is easily implementable in commercial Electronic Design Automation software, to aid designers in the difficult task of selecting the appropriate source degeneration inductor value. The noise performance, on the other hand, is the best possible since the matching networks are designed to provide the input of the two Field Effect Transistors with the optimum termination for noise. The design method is validated with two separate test vehicles operating respectively at Ka‐band (26.5‐31.5 GHz) and K‐band (20.0‐24.0 GHz). The realized Monolithic Microwave Integrated Circuits exhibit 18 dB gain for both versions, NF of 1.5 and 1.2 dB, respectively for the Ka‐band and K‐band version. Input and output matching are typically better than 12 and 15 dB.     

On reachable set estimation of two‐dimensional systems described by the Roesser model with time‐varying delays

In this paper, the problem of reachable set estimation of two‐dimensional (2‐D) discrete‐time systems described by the Roesser model with interval time‐varying delays is considered for the first time. New 2‐D weighted summation inequalities, which provide a tighter lower bound than the commonly used Jensen summation inequality, are proposed. Based on the Lyapunov‐Krasovskii functional approach, and by using the 2‐D weighted summation inequalities presented in this paper, new delay‐dependent conditions are derived to ensure the existence of an ellipsoid that bounds the system states in the presence of bounded disturbances. The derived conditions are expressed in terms of linear matrix inequalities, which can be solved by various computational tools to determine a smallest possible ellipsoidal bound. Applications to exponential stability analysis of 2‐D systems with delays are also presented. The effectiveness of the obtained results are illustrated by numerical examples.     

Robust sampled‐data model predictive control for networked systems with time‐varying delay

In this paper, the problem of sampled‐data model predictive control (MPC) is investigated for linear networked control systems with both input delay and input saturation. The delay‐induced nonlinearity is overapproximatively modeled as a polytopic inclusion. The nonlinear behavior of input saturation is expressed as a convex polytope. The resulting closed‐loop systems are represented as linear systems with polytopic and additive norm‐bounded uncertainties. The aim is to determine a robust MPC controller that asymptotically stabilizes the uncertain system at the origin with a certain level of quadratic performance. The effectiveness of the proposed algorithm is demonstrated by a numerical example.     

Nonfragile H∞ filtering for discrete multirate time‐delayed systems over sensor networks characterized by Gilbert‐Elliott models

In this article, the nonfragile H_∞ filtering problem is investigated for a class of discrete multirate time‐delayed systems over sensor networks. The probabilistic packet dropout occurs during the information transmissions among the sensor nodes in the sensor network characterized by the Gilbert‐Elliott model. In order to take the multirate sampling into account, the state updating period of the system and the sampling period of the sensors are allowed to be different. The variation of the filter gain is considered to reflect the physical errors with the filter implementation. The aim of this article is to design a set of nonfragile filters such that, in the presence of multirate sampling, time‐delays, and packet dropouts, the filtering error dynamics is exponentially mean‐square stable and also satisfies the H_∞ performance requirement. By using the Lyapunov‐Krasovskii functional approach, a sufficient condition is derived, which ensures the exponential mean‐square stability and the H_∞ performance requirement of the filtering error dynamics. Then, the filter gains are characterized in terms of the solution to a set of matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.     

Two recursive least squares parameter estimation algorithms for multirate multiple-input systems by using the auxiliary model

This paper considers identification problems of multirate multiple-input output error systems, derives the input-output representations by using the state space models of the multirate systems, and presents two auxiliary model based recursive least squares algorithms for the corresponding output error models with each subsystem having different or same denominator polynomials. The simulation results show the effectiveness of the proposed algorithms.     

On the thru‐reflect‐line (TRL) numerical calibration and error analysis for parameter extraction of circuit model

In this work, a resistor standard is introduced into our previously proposed numerical thru‐reflect‐line (TRL) calibration procedure in order to determine the characteristic impedance of the line standard of calibration on the basis of a deterministic method of moments (MoM) algorithm. A comprehensive analytical derivation is presented with regards to electrical properties of such a resistor standard in comparison to other standards. In addition, an error analysis is detailed, which reveals correlations of characteristic parameters in connection with equivalent circuit model development from the conversions from field‐based S‐parameters to circuit‐based Y‐ or Z‐parameters. Interesting properties and criteria are derived, allowing accurate parameter extractions. To validate the proposed numerical TRL calibration procedure with this new resistor standard concept and the developed error analysis, the characteristic impedance of a microstrip line is extracted from a commercial software. In addition, a further example with microstrip discontinuity is shown and the effectiveness of the proposed technique is verified. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Asynchronous H∞ filtering for time delayed APF with MDADT based on T‐S fuzzy model

This paper investigates the H_∞ asynchronous filtering problem for active power filter (APF) systems with time delayed to monitor the states of these systems. APF system is represented by T‐S fuzzy model. The phenomenon of asynchronous often appears in the practical system. And in this paper, asynchronous means that the switching of the filters we designed has a lag to the switching of the system models. By using the MDADT and the Lyapunov‐Krasovskii function methods, the switching filtering error system is delay‐dependent stability and has the H_∞ performance. The filter parameter is solved by linear matrix inequalities (LMIs). A simulation example is given to show the efficiency of the proposed methods.