Uncertain T-S model-based robust controller design with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si28.gif" display="inline" id="mml28" overflow="scroll"><mml:mi mathvariant="script">D</mml:mi></mml:math>-stability constraints—A simulation study of quadrotor attitude stabilization

Cherifi, Abdelmadjid; Guelton, Kevin; Arcese, Laurent

doi:10.1016/j.engappai.2017.08.021

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…In this regard, numerous studies have covered various aspects of Unmanned Aerial Vehicle (UAV) systems such as, stability enhancement [3], payload drop missions [4], fault-tolerance [5], visual-based control [6], taxi model [10], path following [11] and quadrotor stabilization [12]. Particularly, the fuzzy control of a quadrotor with or without optimization is reported in [4,5,12,14,15,16]. On the other hand, human's expertise does not allow the synthesis of an optimal FLC, therefore; fuzzybased controllers exhibit limited performance and robustness.…”

Section: Introductionmentioning

confidence: 99%

“…The commonly used optimization methods include; Genetic Algorithms [27], Ant Colony Optimization (ACO) [17,24], Particle Swarm Optimization (PSO) [28], Bee Colony Optimization [29] and others [30]. These algorithms have been applied for various UAV systems such as birotor helicopter [21,23,24], quadrotor [4,6,12,14,15,16,18,25], hexacopter [31] and so on.…”

Section: Introductionmentioning

confidence: 99%

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Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Zatout

Rezoug

et al. 2021

International Journal of Systems Science

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Bio-inspired optimization algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviors of some animal species in such a way that allows solving optimization problems. The present paper aims at applying three metaheuristic methods for optimizing Fuzzy Logic Controllers used for quadrotor attitude stabilization. The investigated methods are Particle Swarm Optimization (PSO), BAT algorithm and Cuckoo Search (CS). These methods are applied to find the best output distribution of singleton membership functions of the Fuzzy Controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: Integral Squared Error, Integral Time-weighted Absolute Error and Integral Time-Squared Error. These metrics allow performance comparison of to compare the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than Cuckoo Search. According to these results, the BAT-based Fuzzy Controller exhibits superior performance compared with other algorithms to stabilize the quadrotor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Zatout

Rezoug

et al. 2021

International Journal of Systems Science

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“…Extending the D-stable method to the nonlinear system, many controller design methods have been developed with the T-S fuzzy model [22][23][24]. Additionally, the control problem of perturbed nonlinear systems is solved by applying the D-stable method and robust control theory [25]. In [22][23][24][25], it can be seen that the controller designed by these papers can obtain the better transient behaviors of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the control problem of perturbed nonlinear systems is solved by applying the D-stable method and robust control theory [25]. In [22][23][24][25], it can be seen that the controller designed by these papers can obtain the better transient behaviors of nonlinear systems. However, it is known that the value of control gains is also much bigger when the transient behaviors such as settling time may not meet the cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Qiao¹,

Chang

Lin

et al. 2021

Processes

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In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method.

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“…The method plays the role of a nonlinear system in mathematical modeling by if-then fuzzy rules and sublinear systems. Due to the rapid development of computer speed, the T-S method has been studied by many papers [19][20][21][22][23][24][25][26][27]. To take advantage of the T-S fuzzy system, the synchronization of the chaotic system has been reconstructed in the form of a T-S system [28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer-Based Linear Matrix Inequality for the Synchronization of Takagi-Sugeno Fuzzy Chaotic Systems

et al. 2020

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Uncertain T-S model-based robust controller design withD-stability constraints—A simulation study of quadrotor attitude stabilization

Cited by 11 publications

References 40 publications

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

Disturbance Observer-Based Linear Matrix Inequality for the Synchronization of Takagi-Sugeno Fuzzy Chaotic Systems

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