This paper investigates a dynamic output feedback adaptive integral sliding mode predictive control (AISMPC) approach based on a dual-loop Kalman filter (DLKF) for linear discrete-time systems. The considered system class is subjected to bounded disturbances and noise, with constraints on both the inputs and states. The DLKF introduces one loop to estimate the states and another loop to estimate the disturbances. The dynamic output feedback AISMPC consists of two parts: one component is an output feedback adaptive integral sliding mode control (AISMC) to handle disturbances, and the other component is an output feedback robust model predictive control (RMPC) to achieve optimal control with constraints. It is proved that the closed-loop system is stabilized in a neighborhood of the origin. Finally, numerical simulation tests are carried out to validate the effectiveness of the proposed approach.
AlessandriABagliettoMBattistelliG (2004) On estimation error bounds for receding-horizon filters using quadratic boundedness. IEEE Transactions on Automatic Control49(8): 1350–1355.
2.
AndersonBDODehghaniA (2008) Challenges of adaptive control-past, permanent and future. Annual Reviews in Control32(2): 123–135.
3.
AthansMFalbPL (2003) Optimal control: An introduction to the theory and its applications. IEEE Transactions on Automatic Control12(3): 345–347.
4.
BaeHOhJH (2017) Novel state estimation framework for humanoid robot. Robotics and Autonomous Systems98: 258–275.
5.
DingBPanH (2014) Output feedback robust MPC with one free control move for the linear polytopic uncertain system with bounded disturbance. Automatica50: 2929–2935.
6.
DingBXiYCychowskiMT, et al. (2008) A synthesis approach for output feedback robust constrained model predictive control. Automatica44(1): 258–264.
7.
DingBZouT (2007) Synthesizing output feedback predictive control for constrained uncertain time-varying discrete systems. Acta Automatica Sinica33(1): 78–83.
8.
EdwardsCSpurgeonSK (1998) Sliding Mode Control: Theory and Applications. London: Taylor and Francis.
9.
FarboodMShasadeghiMNiknamT, et al. (2022) Fuzzy Lyapunov-based model predictive sliding mode control of nonlinear systems: An ellipsoid recursive feasibility approach. IEEE Transactions on Fuzzy Systems30(6): 1929–1938.
10.
FridmanLPoznyakABejaranoFJ (2014) Robust Output LQ Optimal Control via Integral Sliding Modes. New York: Springer.
11.
HeXJiangWJiangC (2022) Memory nonfragile output feedback robust MPC for polytopic time-delayed systems with constraints. International Journal of Control, Automation and Systems20(2): 375–391.
12.
HouMPanWHanY (2023) A discrete-time integral sliding mode predictive control using iterative dynamic linearization data model. International Journal of Control, Automation and Systems21: 2293–2302.
13.
HéctorRManuelMRaïssiT, et al. (2024) Robust interval predictive tracking control for constrained and perturbed unicycle mobile robots. International Journal of Robust and Nonlinear Control34(9): 6303–6320.
14.
IncremonaGPRubagottiMFerraraA (2017) Sliding mode control of constrained nonlinear systems. IEEE Transactions on Automatic Control62(6): 2965–2972.
15.
JazwinskiAH (1970) Stochastic Processes and Filtering Theory. New York: Academic Press.
16.
JoshiSMTaoGPatreP (2011) Direct adaptive control using an adaptive reference model. International Journal of Control84(1): 180–196.
17.
KhalidAJianxinXXinghuoY (2007) On the discrete-time integral sliding-mode control. IEEE Transactions on Automatic Control52(4): 709–715.
18.
LiHBiLLiX, et al. (2024) Robust predictive control for EEG-based brain-robot teleoperation. IEEE Transactions on Intelligent Transportation Systems25: 9130–9140.
19.
LiJYuanZDongS, et al. (2022) A nonlinear state estimation framework for humanoid robots. Robotics and Autonomous Systems153: 104100.
20.
LiaoYHedrickJK (2015) Robust model predictive control with discrete-time integral sliding surface. In: IEEE 2015 American control conference, Chicago, IL, 1–3 July, pp. 1641–1646. Piscataway, NJ: IEEE.
21.
LiuKYaoY (2016) Robust Control: Theory and Applications. Hoboken, NJ: John Wiley and Sons.
22.
MaHXiongZLiY, et al. (2021) Sliding mode control for uncertain discrete-time systems using an adaptive reaching law. IEEE Transactions on Circuits and Systems II: Express Briefs68(2): 722–726.
23.
NicolisDAlleviFRoccoP (2020) Operational space model predictive sliding mode control for redundant manipulators. IEEE Transactions on Robotics36(4): 1348–1355.
24.
NikolaosAGeorgeBMirceaL (2014) Construction of invariant polytopic sets with specified complexity. International Journal of Control87(8): 1681–1693.
25.
PingX (2017) Output feedback robust MPC based on off-line observer for LPV systems via quadratic boundedness. Asian Journal of Control19(4): 1641–1653.
26.
PingXYangSDingB, et al. (2020) A convexity approach to dynamic output feedback robust MPC for LPV systems with bounded disturbances. International Journal of Control, Automation and Systems18(6): 1378–1391.
27.
PostlethwaiteITurnerMCHerrmannG (2007) Robust control applications. Annual Reviews in Control31(1): 27–39.
28.
RoySBaldiSFridmanLM (2020) On adaptive sliding mode control without a priori bounded uncertainty. Automatica111: 108650.
29.
RubagottiMEstradaACastaosF, et al. (2011a) Integral sliding mode control for nonlinear systems with matched and unmatched perturbations. IEEE Transactions on Automatic Control56(11): 2699–2704.
30.
RubagottiMIncremonaGPRaimondoDM, et al. (2021) Constrained nonlinear discrete-time sliding mode control based on a receding horizon approach. IEEE Transactions on Automatic Control66(8): 3802–3809.
31.
RubagottiMRaimondoDMFerraraA, et al. (2011b) Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems. IEEE Transactions on Automatic Control56(3): 556–570.
32.
ShahDShahAMehtaA (2021) Higher order networked sliding mode controller for heat exchanger connected via data communication network. European Journal of Control58: 301–314.
33.
SteinbergerMCastilloIHornM, et al. (2019) Robust output tracking of constrained perturbed linear systems via model predictive sliding mode control. International Journal of Robust and Nonlinear Control30(3): 1258–1274.
34.
VettoriSDi LorenzoEPeetersB, et al. (2023) An adaptive-noise augmented Kalman filter approach for input-state estimation in structural dynamics. Mechanical Systems and Signal Processing184: 109654.
35.
WangHRenJHanM, et al. (2023) Robust adaptive three-dimensional trajectory tracking control for unmanned underwater vehicles with disturbances and uncertain dynamics. Ocean Engineering289: 116184.
36.
XiaoHZhangSZhaoD, et al. (2024) Output feedback integral sliding mode predictive control for linear discrete time systems. European Journal of Control80: 101131.
37.
XiaoHZhaoD (2020) Discrete-time dynamic output feedback integral sliding model predictive control. In: IEEE 2020 39th Chinese control conference, Shenyang, China, 27–29 July, pp. 2357–2365. Piscataway, NJ: IEEE.
38.
XiaoHZhaoDGaoS, et al. (2022) Sliding mode predictive control: A survey. Annual Reviews in Control54: 148–166.
39.
ZhangSZhaoDSpurgeonSK (2021) Output feedback robust distributed model predictive control for parallel systems in process networks with competitive characteristics. Control Engineering Practice113: 104842.
40.
ZhangXMaryamKAngelosG, et al. (2017) Robust optimal control with adjustable uncertainty sets. Automatica75: 249–259.
