This paper investigates the control challenges posed by noncollocated mechatronic systems and motivates the need for a model-based control technique towards such systems. A novel way of online constraint handling by penalty adaptation (PAMPC) is proposed and shown to be of particular relevance towards robust control of underdamped, noncollocated systems by exploiting the structure of such systems. Further, a new tunneling approach is proposed for PAMPC to maintain feasibility under uncertainty. The PAMPC is shown to be optimal for control of a benchmark mass–spring–damper system, which poses all the mentioned challenges.
AstromKWittenmarkB (2011) Computer-Controlled Systems: Theory and Design, Mineola, NY: Dover Publications.
2.
BeardsC (1992) Vibrations and Control Systems, Chichester: E. Horwood.
3.
BrownR (2013) Generalized predictive control algorithm of a simplified ground vehicle suspension system. Journal of Vibration and Control19(16): 2372–2386.
4.
CairanoSD (2007) Model predictive control of magnetically actuated mass spring dampers for automotive applications. International Journal of Control80(11): 1701–1716.
5.
CamachoEBordonsC (2003) Model Predictive Control, Berlin: Springer-Verlag.
6.
ClarkeDW (1987) Generalized predictive control – I. The basic algorithm. Automatica23(2): 137–148.
7.
DarbyMNikolaouM (2011) MPC: Current practice and challenges. Control Engineering Practice20: 328–342.
8.
De Keyser R (2003) Model based predictive control for linear systems. UNESCO Encyclopaedia of Life Support Systems, available at: http://www.eolss.net (accessed 7 May 2014). Oxford: Eolss Publishers Co Ltd.
9.
De Keyser R, et al. (2012) A specifications based PID autotuner. In: 2012 IEEE international conference on control applications (CCA), pp. 1621–1626.
10.
Dutta A, et al. (2012) Switched predictive control design for optimal wet-clutch engagement. In: IFAC workshop on engine and powertrain control, simulation and modeling (ECOSM-2012), pp. 319–324.
11.
DuttaA (2014) Robust and two-level (nonlinear) predictive control of switched dynamical systems with unknown references for optimal wet-clutch engagement. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering228(4): 233–244.
12.
GarrigaJLSoroushM (2010) Model predictive control tuning methods: A review. Industrial and Engineering Chemistry Research49(8): 3505–3515.
13.
LeeJH (2011) Model predictive control: Review of the three decades of development. International Journal of Control, Automation, and Systems9(3): 415–424.
14.
MaciejowskiJ (2002) Predictive Control with Constraints, Englewood Cliffs, NJ: Prentice Hall.
15.
ObrzutT (2009) Non-Collocation Problems in Dynamics and Control of Mechanical Systems, Cleveland, OH: Cleveland State University.
16.
PreumontA (2011) Vibration Control of Active Structures, New York, NY: Springer.
17.
QiuZ (2009) Active vibration control of a flexible beam using a non-collocated acceleration sensor and piezoelectric patch actuator. Journal of Sound and Vibration326: 438–455.
18.
Richelot J, et al. (2004) Active control of a clamped beam equipped with piezoelectric actuator and sensor using generalized predictive control. In: 2004 IEEE international symposium on industrial electronics.
19.
SokolovIBabitskyV (2001) Phase control of self-sustained vibration. Journal of Sound and Vibration248(4): 725–744.
20.
WangYBoydS (2010) Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology18(2): 267–278.
21.
WillsA (2008) Model predictive control applied to constraint handling in active noise and vibration control. IEEE Transactions on Control Systems Technology16(1): 3–12.
22.
XieY (2013) Fundamental limits in combine harvester header height control. Journal of Dynamic Systems, Measurement, and Control135(3): 1–8.
