Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation

Cao, Boqiang; Nie, Xiaobing

doi:10.1002/rnc.5642

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…In this paper, we use the following time-varying constraint in Figure 2 to limit the boundary system output (16) where 𝜙 1 (t) and 𝜙 2 (t) are the decaying performance function and can be described as [47] 𝜙…”

Section: Modeling Of a Flexible Riser With N-section Pipesmentioning

confidence: 99%

“…In this section, by synthesizing Lyapunov's direct method, the finite-dimensional backstepping method and observer technique, a boundary control with disturbance observer is designed to suppress the vibrations of the total riser system and to prevent the boundary vibration from violating the asymmetric time-varying constraint (16), simultaneously. In this article, the boundary controller is designed as…”

Section: Control Designmentioning

confidence: 99%

“…First, input nonlinearity is often encountered in vibration control systems, which may severely limit the performance of the controller and cause damage itself [15]. In order to solve this problem, many scholars have proposed their methods [16][17][18][19][20][21][22][23]. In [20], Liu et al presented a boundary control method based on the original PDEs to regulate the hose's vibration and handle the effect of the input constraint.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

Liu,

Yao,

Liu

2023

Asian Journal of Control

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In this paper, an adaptive neural network controller is proposed for vibration suppression of a multisectional riser system with unknown boundary disturbance, time‐varying asymmetric output constraint, and input nonlinearity. The considered riser system is composed of a continuous connection of several different pipes, and its dynamic models are represented by a set of multiple continuously connected partial differential equations (PDEs) and an ordinary differential equation (ODE) at the top boundary. Considering input nonlinearity, external disturbance, and system uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, a barrier Lyapunov function is employed to guarantee the restrictions. With the proposed boundary control, the stability of the closed‐loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy.

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Section: Modeling Of a Flexible Riser With N-section Pipesmentioning

confidence: 99%

Section: Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

Liu,

Yao,

Liu

2023

Asian Journal of Control

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“…To improve the performance of the control system, observers can be introduced to estimate some variables or parameters. In general, these results are mostly about the design of observers with known inputs, which are relatively conservative (Cao and Nie, 2021).…”

Section: Introductionmentioning

confidence: 99%

Design of unknown input observer for discrete-time Markov jump systems with unknown input in both state equation and output equation

Lian,

Tian

2023

Transactions of the Institute of Measurement and Control

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The problem of unknown input observer design for discrete-time nonlinear generalized Markov jump systems is studied. First, like a normal system, the whole nonlinear system is transformed into a local linear system, and then a large number of linear system theories can be applied to solve related problems. Second, in the observer design of general discrete-time Markov jump systems, only the unknown input in the state equation is usually considered. In this paper, the unknown input is considered in both the state equation and the output equation. The state estimation error system is derived by defining the error. The non-uniform Lyapunov functional is selected to stabilize the estimation error system using the Lyapunov theory. The sufficient conditions for the stability of the system are obtained and transformed into the feasibility problem of linear matrix inequality. The problem of unknown input observer design for discrete-time nonlinear generalized Markov jump systems is solved using MATLAB software. Finally, a numerical example of two rules and two modes is used to verify the effectiveness and feasibility of the proposed unknown input observer.

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“…31 However, the input saturation of MEMS gyroscopes control system has not been paid attention and studied. The input saturation is modeled by the saturation function and tangent function, 32,33 respectively. To deal with the input saturation, an anti-saturation compensator is designed in this article, where an auxiliary system constructed by the difference of actual control input and ideal control input is introduced.…”

Section: Introductionmentioning

confidence: 99%

Finite time observer‐based output feedback control of MEMS gyroscopes with input saturation

Zhang

Shi

2022

Intl J Robust & Nonlinear

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The finite time observer‐based output feedback control of MEMS gyroscopes with input saturation is addressed in this article. For the unmeasured speed signal, neural network (NN) learning‐based finite time observer is designed to observe the speed signal in finite time, where the adaptive NN is employed to approximate the nonlinear dynamics with system uncertainties. Considering the input saturation, an auxiliary system is introduced to design the anti‐saturation compensator, such that the actual controller can escape saturation with less time and faster convergence can be achieved. Then the anti‐saturation compensator further works together with output feedback controller to control the dynamics of MEMS gyroscopes. Simulation results are given to verify the faster speed observation and faster convergence of the proposed controller under input saturation.

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Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation

Cited by 7 publications

References 54 publications

Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

Design of unknown input observer for discrete-time Markov jump systems with unknown input in both state equation and output equation

Finite time observer‐based output feedback control of MEMS gyroscopes with input saturation

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