Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Series active variable geometry suspension application to comfort enhancement

This paper explores the potential of the Series Active Variable Geometry Suspension (SAVGS) for comfort and road holding enhancement. The SAVGS concept introduces significant nonlinearities associated with the rotation of the mechanical link that connects the chassis to the spring-damper unit. Although conventional linearization procedures implemented in multi-body software packages can deal with this configuration, they produce linear models of reduced applicability. To overcome this limitation, an alternative linearization approach based on energy conservation principles is proposed and successfully applied to one corner of the car, thus enabling the use of linear robust control techniques. An H∞ controller is synthesized for this simplified quarter-car linear model and tuned based on the singular value decomposition of the system's transfer matrix. The proposed control is thoroughly tested with one-corner and full-vehicle nonlinear multi-body models. In the SAVGS setup, the actuator appears in series with the passive spring-damper and therefore it would typically be categorized as a low bandwidth or slow active suspension. However, results presented in this paper for an SAVGS-retrofitted Grand Tourer show that this technology has the potential to also improve the high frequency suspension functions such as comfort and road holding

Multivariable Adaptive Control Design Under Internal Model Control Structure.

A new adaptive multivariate control scheme has been devised. The method combines the best characteristics of conventional adaptive systems and internal model control (IMC) structure. The control scheme builds by itself the required models and avoids the ambiguities in the definition of performance specifications. The problem of plant inversion associated with the IMC structure has been solved. The method introduced in this work is based on the properties of the Smith-McMillan form. However, the method does not require the explicit determination of the form. Furthermore, the computation of a stable plant inverse requires only matrix inversion and scalar polynomial factorization. The resulting algorithm is suitable for on-line operation. The control schemed is implemented through the following stages: (1) Identification. The parameters of a multivariable ARX model are estimated using a recursive least square algorithm with variable forgetting factor. The input and output orders can be used as additional degrees of freedom. The algorithm developed shows good numerical characteristics with fast convergence even for a large number of parameters. (2) Computation of the manipulated variables. The model is used to determine a controller following the IMC approach. The resulting equations are solved to compute the required manipulated variables. The algorithm for system inversion allows computations to be executed on-line. (3) Filtering. The usual filters of the IMC approach are also used in the adaptive scheme. The objective is to reduce the sensitivity of the controller. Only non-adaptive non-interactive filters have been considered. The results with first order low pass filters are satisfactory. The bandwidth of the filter is used as an additional tuning parameter. The adaptive control strategy has been extensively tested using computer simulation. The tests include extensions to non-linear plants. Comparisons with non-adaptive IMC control show the advantage of the new scheme developed in this work