Backstepping control integrated with Lyapunov-based model predictive control

Kim, Yeonsoo; Oh, Tae Hoon; Park, Taekyoon; Lee, Jong Min

doi:10.1016/j.jprocont.2018.12.007

Cited by 16 publications

(12 citation statements)

References 24 publications

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“…In order to mitigate the impact of the differential expansion as well as of the input saturation problem, Farrell et al [20,21] introduced a constrained command filter in the design of the backstepping control strategy. The corresponding command filter, which is depicted in Figure 1 is as follows…”

Section: Command Filtermentioning

confidence: 99%

“…x c 2 is the output of the command-filter. The compensated tracking errorz 1 is defined as (21) and the ζ 1 signal is chosen asζ…”

Section: Command-filter Based Controller Design With Prescribed Perfomentioning

confidence: 99%

“…To simplify the controller design process with prescribed performance, many researchers focus on the backstepping control strategy [17][18][19][20][21][22][23][24][25]. A novel backstepping control method was developed in [17] for a class of uncertain system, and the stability of the system was examined under the impact of artificial delays.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the differential signals are easily affected by high-frequency disturbances or uncertainties. In order to address this problem, Farrell et al [20,21] utilized a compensated tracking error signal to introduce the concept of the command filter so that the problem can be addressed. In [25], modified error compensation signals were proposed along with new virtual control signals so as to ensure the finite-time tracking performance for a class of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Command-Filtered Backstepping Integral Sliding Mode Control with Prescribed Performance for Ship Roll Stabilization

et al. 2019

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In this paper, a novel, robust fin controller based on the backstepping control strategy and sliding mode control is proposed to handle the problem of ship roll stabilization. First, the mathematical model of the fin control system is established, including the modeling errors and the external disturbances generated by sea waves. In order to address the side effects caused by differential expansion, a command-filter is implemented within the backstepping controller design. By introducing a new performance function and a corresponding error transformation, the compensated tracking error can be bounded to achieve the desired prescribed dynamic and steady-state responses. The sliding mode disturbance rejection control with prescribed performance is realized by combining the disturbance observer. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.

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Section: Command Filtermentioning

confidence: 99%

“…x c 2 is the output of the command-filter. The compensated tracking errorz 1 is defined as (21) and the ζ 1 signal is chosen asζ…”

Section: Command-filter Based Controller Design With Prescribed Perfomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Command-Filtered Backstepping Integral Sliding Mode Control with Prescribed Performance for Ship Roll Stabilization

et al. 2019

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“…The goals of MPC are to predict the future dynamic behavior of the system, to achieve rolling optimization, and to provide feedback correction of the model error [34]- [42]. Moreover, for universal uncertainties and disturbances, a large number of results based on the robust model predictive control (RMPC) method have been found [43]- [47]. It is known that MJLSs are also treated as a class of uncertain systems, where the uncertainty conforms to certain statistical probabilities.…”

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

Cai

et al. 2019

IEEE Access

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For a class of discrete-time Markovian jump linear systems subject to operation mode disordering, a robust model predictive control method can be proposed to solve this issue. A bijective mapping scheme between the original random process and a new random process is studied to cope with the problem of operation mode disordering. At each sampling time, the original ''min-max'' optimization problem is transformed into a convex optimization problem with linear matrix inequalities so that the complexity of solving the optimization problem can be greatly reduced. The sufficient stability condition of the Markovian jump linear systems can be achieved by using the Lyapunov stability theory. Moreover, a state feedback control law is obtained, which minimizes an infinite prediction horizon performance cost. Furthermore, the cases of uncertain and unknown transition probabilities are also considered in this paper. The simulation results show that the proposed method can guarantee the optimal control performance and the stability of Markovian jump linear systems. INDEX TERMS Robust model predictive control, Markovian jump linear systems (MJLSs), operation mode disordering, linear matrix inequalities (LMIs).

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