Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators

Shahnazi, Reza

doi:10.1016/j.neucom.2015.07.098

Cited by 29 publications

(10 citation statements)

References 53 publications

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“…In order to solve this problem, some scholars use the main controllers based on the fuzzy model in the internal model control structure, which have achieved good results in practical applications [29][30][31]. In [4,17,32,33], some inverse controllers based on a type-2 fuzzy model control design strategy are developed, which have been successfully applied to the pH neutralization process and the spherical Recently, the interval type-2 fuzzy logic system (IT2-FLS) has been widely applied in a variety of fields, such as the pH neutralization process [3,4], the nonlinear continuous bioreactor with bifurcation [5], the mobile robot [6,7], the coupled-tank system [8], the temperature control [9], the double-inverted pendulum [10], aerospace [11], the classification and curve identification datasets [12], airplane flight control [13], the travelling salesman problem [14], power management and electrical control [15], benchmark control problems [16], and so on. The IT2-FLS extends the reasoning and design freedom of the fuzzy system because of the expanding dimension performance provided by the footprint of uncertainty (FOU) [17].…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Zhao

Cao

et al. 2017

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Multivariable coupling, nonlinear and large time delays exist in the coil outlet temperature (COT) control system of the ethylene cracking furnace, which make it hard to achieve accurate control over the COT of the furnace in actual production. To solve these problems, an inverse controller based on an interval type-2 fuzzy model control strategy is introduced. In this paper, the proposed control scheme is divided into two parts: one is the approach structure part of the interval type-2 fuzzy model (IT2-FM), which is utilized to approach the process output. The other is the interval type-2 fuzzy model inverse controller (IT2-FMIC) part, which is utilized to control the output process to achieve the target value. In addition, on the cyber-physical system platform, the actual industrial data are used to test and obtain the mathematical model of the COT control system of the ethylene cracking furnace. Finally, the proposed inverse controller based on the IT2-FM control scheme has been implemented on the COT control system of the ethylene cracking furnace, and the simulation results show that the proposed method is feasible.

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Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Zhao

Cao

et al. 2017

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“…In [41], an auxiliary system was designed with the same order as that of the studied MIMO attitude system to handle the input saturation. Some adaptive type-1 or type-2 fuzzy/neural schemes have also been proposed to control the nonlinear systems with actuator saturation in [42][43][44]. In [35], the input saturation was regarded as external disturbances, and a disturbanceobserver-based terminal sliding mode control was developed for SISO (single-input single-output) system.…”

Section: Introductionmentioning

confidence: 99%

Finite‐Time Tracking Control for a Class of MIMO Nonlinear Systems with Unknown Asymmetric Saturations

2017

Mathematical Problems in Engineering

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This paper addresses the problem of finite-time tracking control for multiple-input and multiple-output (MIMO) nonlinear systems with asymmetric saturations. A systematic approach is proposed to eliminate the effects of unmeasured external disturbances and unknown asymmetric saturations. In the proposed control strategy, a terminal sliding mode disturbance observer is provided to estimate the augmented disturbance (which contains the unknown asymmetric input saturation and external disturbance). The approximation error of the augmented disturbance can converge to zero in a fixed finite-time interval. Furthermore, a novel finitetime tracking control algorithm is developed to guarantee fast convergence of the tracking error. Compared with the existing results on finite-time tracking control, the chattering problem and the input saturation problem can be solved in a unified framework. Several simulations are given to demonstrate the effectiveness of the proposed approach.

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“…Compared with integer-order systems, there is very little research dealing with multi-input-multi-output (MIMO) fractional-order systems [1,. This fact can be explained by the specificity of MIMO systems and the difficulties with the extension of the approaches employed for integer-order systems to fractional ones [10,22,37,41,46,50,56].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the existing controls in [2,8,10,11,40,62], the adaptive fuzzy control laws presented in [47][48][49] have solved the tracking problem for nonlinear uncertain discrete-time systems with unknown control direction and input nonlinearities (such as dead zone, backlash-like hysteresis, and backlash), by using the reinforcement learning algorithm. • Uncertainty: In most practical situations, the systems under control are unknown or partially unknown [4,7,12,13,17,19,22,31,42,56,57,61]. These facts require specific control tools to deal with the controller design process, being one of the most extended ones the adaptive control paradigm.…”

Section: Introductionmentioning

confidence: 99%

“…• Saturation in the control input: In many industrial systems, the input (actuator) saturation can exist [1, 15-18, 22, 42]. It usually gravely limits the system performance and sometimes even makes the closedloop system unstable [1,[15][16][17][18]22]. Therefore, the analysis and control of nonlinear systems with input saturation are important fields in the practical control system design and have been receiving great attention in recent years [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

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This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input-multioutput nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup property of fractional-order derivative, the system is transformed into a normalized fractional-order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real condition, the Razumikhin Lemma, the frequency-distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof. The main advantages of this work are that:(1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

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Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators

Cited by 29 publications

References 53 publications

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Finite‐Time Tracking Control for a Class of MIMO Nonlinear Systems with Unknown Asymmetric Saturations

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

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