Fuzzy adaptive terminal sliding mode control of SIMO nonlinear systems with T-S fuzzy model

Abadi, Ali Soltani Sharif; Mehrizi, Mohammad Hayeri; Hosseinabadi, Pooyan Alinaghi

doi:10.1109/cfis.2018.8336620

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Many control methods have been presented based-on this control scheme. This method has been used, combined with other control concepts such as fuzzy logic [1][2][3], backstepping [4], adaptive concept [5][6][7] in different studies. This control method has also been also used to address different real-time control problems.…”

Section: Introductionmentioning

confidence: 99%

Archives of Control Sciences

Abadi¹,

Hosseinabadi²,

Mekhilef³

et al. 2020

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Many real-time systems can be described as cascade space-state models of different orders. In this paper, a new predefined controller is designed using a Strongly Predefined Time Sliding Mode Control (SPSMC) scheme for a cascade high-order nonlinear system. The proposed control scheme based-on SMC methodology is designed such that the system states reach zero within a determined time prior to performing numerical simulation. Moreover, Fixed Time Sliding Mode Control (FSMC) and Terminal Sliding Mode Control (TSMC) schemes are presented and simulated to provide a comparison with the proposed predefined time scheme. The numerical simulation is performed in Simulink/MATLAB for the proposed SPSMC and the other two existing methods on two examples: second and of third order to demonstrate the effectiveness of the proposed SPSMC method. The trajectory tracking of the ship course system is addressed as an example of a second-order system. Synchronization of two chaotic systems, Genesio Tesi and Coullet, is considered as an example of a third-order system. Also, by using two performance criteria, a thorough comparison is made between the proposed predefined time scheme, SPSMC, and the two no predefined time schemes, FSMC and TSMC.

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Section: Introductionmentioning

confidence: 99%

Archives of Control Sciences

Abadi¹,

Hosseinabadi²,

Mekhilef³

et al. 2020

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“…Reference [10] designed a method to address stability analysis and stabilization problems for the continuous T-S fuzzy system with timedelay. In [11], a new adaptive terminal sliding mode control method for single-input multi-output T-S fuzzy system with unknown and external disturbance was investigated. It is 2 Advances in Fuzzy Systems worth mentioned that there exist no uncertainties in the membership functions for the type-1 T-S fuzzy system; thus, the above researches based on the type-1 T-S fuzzy model may result in conservatism.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control for Interval Type-2 Fuzzy System with Event-Triggered Scheme

Wang

Tang

Deng

et al. 2019

Advances in Fuzzy Systems

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In this paper, a synthesis approach of model predictive control (MPC) is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system with quantization error, bounded disturbance, and data loss. The novelty lies in the following technical improvements. In order to reduce the redundant data transmission, an event-triggered communication scheme is applied to determine whether the control law should be transmitted into the communication network or not. The IT2 T-S fuzzy model is utilized to address the nonlinearity of plant with parameter uncertainties, which can be captured by the lower and upper membership functions. Furthermore, the phenomena of data loss and quantization error between the controller and the actuator are expressed as Markovian chain and sector-bound uncertainties. The synthesis approach of MPC is provided by solving an MPC optimization problem over an infinite horizon objective function which explicitly considers the input constraints. By applying the quadratic boundedness (QB) technique, the recursive feasibility and quadratic stability of closed-loop system can be guaranteed. A numerical simulation and comparison studies are proposed to illustrate the effectiveness of this approach.

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