Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

Ghavidel, Hesam Fallah; Kalat, Ali Akbarzadeh

doi:10.1007/s00521-016-2732-7

Cited by 32 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the state-space equations of a non-affine non-linear SISO system (Arefi et al, 2014; Fallah Ghavidel and Akbarzadeh Kalat, 2018) is expressed in following…”

Section: Problem Formulation and Mathematical Preliminariesmentioning

confidence: 99%

“…The estimation error of observer-based control according to Fallah Ghavidel and Akbarzadeh Kalat (2018) and Leu et al (1999) is considered in the following…”

Section: Controller Designmentioning

confidence: 99%

“…The controllers had two significant features: (1) using the Nussbaum function definition, no prior knowledge of the control gain direction is needed and (2) states of the system are estimated using observer-based control. Observer-based control was performed for both non-affine and affine uncertain non-linear systems by Fallah Ghavidel and Akbarzadeh Kalat (2018). It is assumed that the control gain sign is known.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

Mawlani

Arbabtafti

2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case II is limited to [−1.5, 1.5] and [−1,1]mm, respectively.

show abstract

“…In general, the state-space equations of a non-affine non-linear SISO system (Arefi et al, 2014; Fallah Ghavidel and Akbarzadeh Kalat, 2018) is expressed in following…”

Section: Problem Formulation and Mathematical Preliminariesmentioning

confidence: 99%

“…The estimation error of observer-based control according to Fallah Ghavidel and Akbarzadeh Kalat (2018) and Leu et al (1999) is considered in the following…”

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

Mawlani

Arbabtafti

2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…One effective way for chattering reduction is replacing the discontinuous function ( ) sgn ⋅ with continuous functions [39,45]. Fuzzy logic controllers are widely utilized to stabilize the systems during recent years [28,27,38,30,11,40,7,17]. In this paper, the fuzzy mechanism fs u is employed and incorporated into Equation 20 Letting σ and fs u be the input and the output variables of the FAPISMC, respectively, the instinctive rules can be written as:…”

Section: Control Problemmentioning

confidence: 99%

Fuzzy-Logic-Based Adaptive Proportional-Integral Sliding Mode Control for Active Suspension Vehicle Systems: Kalman Filtering Approach

Zare¹,

Mardani²,

Vafamand

et al. 2019

ITC

View full text Add to dashboard Cite

This paper deals with the problem of synthesizing a fuzzy-logic-based adaptive proportional-integral sliding mode control (FAPISMC) for active suspension systems based on Kalman filtering approach. To improve the performance of the controller and eliminate the effect of the chattering, the switching input is designed based on the fuzzy-logic-based approach with a minimum number of rules. In order to facilitate the stability analysis, the estimation of the state variables is used in designing the sliding surface platform. Furthermore, the gain of the controller is updated by an adaptive law to avoid any pre-knowledge of the disturbance amplitude. Subsequently, the proposed approach is more implementable in real-world processes. Finally, in order to illustrate the effectiveness and merits of the proposed approach, a suspension system is considered and simulated by the real-time hardware-in-the-loop (HiL). In this example, a quarter-car model of suspension systems is considered. Then, the obtained real-time results are compared with the linear quadratic regulator approach.

show abstract

“…During the last decades, we have witnessed an increasing trend toward application of fuzzy systems and neural networks in designing robust nonlinear controllers. [11][12][13][14][15][16][17] These researches can be considered as alternatives made toward a common objective which is estimation and compensation of uncertainty. 18 The universal approximation property has been an important motivation for this widespread application of fuzzy systems and neural networks.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control of Electrically Driven Robots Using a Model-free Observer

Izadbakhsh¹,

Khorashadizadeh²,

Kheirkhahan³

2018

Robotica

View full text Add to dashboard Cite

SummaryThis paper presents a robust tracking controller for electrically driven robots, without the need for velocity measurements of joint variables. Many observers require the system dynamics or nominal models, while a model-free observer is presented in this paper. The novelty of this paper is presenting a new observer–controller structure based on function approximation techniques and Stone–Weierstrass theorem using differential equations. In fact, it is assumed that the lumped uncertainty can be modeled by linear differential equations. Then, using Stone–Weierstrass theorem, it is verified that these differential equations are universal approximators. The advantage of proposed approach in comparison with previous related works is simplicity and reducing the dimensions of regressor matrices without the need for any information of the systems’ dynamic. Simulation results on a 6-degrees of freedom robot manipulator driven by geared permanent magnet DC motors indicate the satisfactory performance of the proposed method in overcoming uncertainties and reducing the tracking error. To evaluate the performance of proposed controller in practical implementations, experimental results on an SCARA manipulator are presented.

show abstract

Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

Cited by 32 publications

References 47 publications

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

Fuzzy-Logic-Based Adaptive Proportional-Integral Sliding Mode Control for Active Suspension Vehicle Systems: Kalman Filtering Approach

Tracking Control of Electrically Driven Robots Using a Model-free Observer

Contact Info

Product

Resources

About