Model predictive control of dynamically substructured systems with application to a servohydraulically-actuated mechanical plant

Copyright ©2010 Institution of Engineering and Technology (IET). This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory and Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.Dynamically substructured systems (DSS) are increasingly used by the dynamics testing community. DSS involves the physical testing of full-size critical components in parallel with numerical testing of the remaining components. This has certain advantages over other testing methods. However, the synchronisation of the signals at the interface between the physical and numerical substructures of DSS requires a high fidelity controller. In practice, the performance of the DSS testing can be degraded by input saturation of the actuators. In this study, the authors use model predictive control (MPC) to cope with the saturation problem in DSS. To facilitate the MPC and observer design for DSS, a modified DSS framework based on an existing one is proposed. As a case study, a quasi-motorcycle (QM) system is converted into the modified DSS framework and a traditional on-line MPC control strategy is implemented in real time

A novel robust disturbance rejection anti-windup framework

This is an Author's Original Manuscript of an article submitted for consideration in the International Journal of Control [copyright Taylor & Francis] and is available online at http://www.tandfonline.com/10.1080/00207179.2010.542774In this article, we propose a novel anti-windup (AW) framework for coping with input saturation in the disturbance rejection problem of stable plant systems. This framework is based on the one developed by Weston and Postlethwaite (W&P) (Weston, P.F., and Postlethwaite, I. (2000), ‘Linear Conditioning for Systems Containing Saturating Actuators’, Automatica, 36, 1347–1354). The new AW-design improves the disturbance rejection performance over the design framework usually suggested for the coprime-factorisation based W&P-approach. Performance improvement is achieved by explicitly incorporating a transfer function, which represents the effect of the disturbance on the nonlinear loop, into the AW compensator synthesis. An extra degree of freedom is exploited for the coprime factorisation, resulting in an implicitly computed multivariable algebraic loop for the AW-implementation. Suggestions are made to overcome the algebraic loop problem via explicit computation. Furthermore, paralleling the results of former work (Turner, M.C., Herrmann, G., and Postlethwaite, I. (2007), ‘Incorporating Robustness Requirements into Antiwindup Design’, IEEE Transactions on Automatic Control, 52, 1842–1855), the additive plant uncertainty is incorporated into the AW compensator synthesis, by using a novel augmentation for the disturbance rejection problem. In this new framework, it is shown that the internal model control (IMC) scheme is optimally robust, as was the case in Turner, Herrmann, and Postlethwaite (2007) and Zheng and Morari (Zheng, A., and Morari, M. (1994), ‘Anti-windup using Internal Model Control’, International Journal of Control, 60, 1015–1024). The new AW approach is applied to the control of dynamically substructured systems (DSS) subject to external excitation signals and actuator limits. The benefit of this approach is demonstrated in the simulations for a small-scale building mass damper DSS and a quasi-motorcycle DSS

Model reference adaptive control of a nonsmooth dynamical system

In this paper a modified model reference adaptive control (MRAC) technique is presented which can be used to control systems with nonsmooth characteristics. Using unmodified MRAC on (noisy) nonsmooth systems leads to destabilization of the controller. A localized analysis is presented which shows that the mechanism behind this behavior is the presence of a time invariant zero eigenvalue in the system. The modified algorithm is designed to eliminate this zero eigenvalue, making all the system eigenvalues stable. Both the modified and unmodified strategies are applied to an experimental system with a nonsmooth deadzone characteristic. As expected the unmodified algorithm cannot control the system, whereas the modified algorithm gives stable robust control, which has significantly improved performance over linear fixed gain control

MCS Adaptive Control of Nonlinear Systems: utilizing the properties of chaos

This paper discusses a novel approach to the control of chaos based on the use of the adaptive minimal control synthesis algorithm. The strategies presented are based on the explicit exploitation of different properties of chaotic systems including the boundedness of the chaotic attractors and their topological transitivity (or ergodicity). It is shown that chaos can be exploited to synthesize more efficient control techniques for nonlinear systems. For instance, by using the ergodicity of the chaotic trajectory, we show that a local adaptive control strategy can be used to synthesize a global controller. An application is to the swing-up control of a double inverted pendulum

Model-Based Motion Filtering for Improving Arm Gesture Recognition Performance

We describe a model-based motion filtering process that when applied to human arm motion data leads to improved arm gesture recognition. By arm gestures, we mean movements of the arm (and positional placement of the hand) that may or may not have any meaningful intent. Arm movements or gestures can be viewed as responses to muscle actuations that are guided by responses of the nervous system. Our method makes strides towards capturing this underlying knowledge of human performance by integrating a model for the arm based on dynamics and containing a control system. We hypothesize that by embedding the human performance knowledge into the processing of arm movements, it will lead to better recognition performance. We present details for the design of our filter, our analysis of the filter from both expert-user and multiple-user pilot studies. Our results show that the filter has a positive impact on the recognition performance for arm gestures