Quadrotor UAV attitude stabilization using fuzzy robust control

2021 ◽  
pp. 014233122110025
Author(s):  
David Lara Alabazares ◽  
Abdelhamid Rabhi ◽  
Claude Pegard ◽  
Fernando Torres Garcia ◽  
Gerardo Romero Galvan
Keyword(s):  
Robust Control ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Ts Fuzzy Model ◽  
Attitude Stabilization ◽  
Numerical Tools ◽  
Robust Fuzzy Controller ◽  
Takagi Sugeno ◽  
The Given

In this paper, a robust controller for attitude stabilization of a small quadrotor helicopter is developed. The TS (Takagi-Sugeno) fuzzy model approach and the [Formula: see text] robust control are combined to produce the proposed algorithm. Besides, disturbances and parametric uncertainties are considered. First, the nonlinear model of the vehicle is linearized around several operating points to obtain the representation of a TS fuzzy model, which represents the nonlinearity of the system dynamics. Then, a robust fuzzy controller is synthesized which guarantees desired control performances. The given controller is designed using numerical tools such as linear matrix inequalities (LMI). Finally, simulation results and real-time experiments are presented to validate the performance of the proposed scheme to robustly stabilize the quadrotor dynamics at the desired reference.

scholarly journals Further results on robust fuzzy dynamic systems with LMI D-stability constraints

2014 ◽  
Vol 24 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Wudhichai Assawinchaichote
Keyword(s):  
Dynamic Systems ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Local System ◽  
Linear Matrix ◽  
Fuzzy Modelling ◽  
Ts Fuzzy Model ◽  
Input Noise ◽  
Takagi Sugeno

Abstract This paper examines the problem of designing a robust H∞ fuzzy controller with D-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust H∞ fuzzy controller that guarantees (i) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

DELAYED FUZZY CONTROLLER DESIGN FOR HYPERCHAOS WITH APPLICATION TO HYPERCHAOTIC CHEN'S SYSTEM

2007 ◽  
Vol 18 (07) ◽  
pp. 1095-1105 ◽  
Author(s):  
XINGWEN LIU ◽  
XIN GAO
Keyword(s):  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Delayed State Feedback ◽  
Delay Dependent ◽  
Takagi Sugeno ◽  
Compensation Design ◽  
Hyperchaotic Systems ◽  
Control Criterion

Studied in this paper is the control problem of hyperchaotic systems. By combining Takagi–Sugeno (T–S) fuzzy model with parallel distributed compensation design technique, we propose a delay-dependent control criterion via pure delayed state feedback. Because the result is expressed in terms of linear matrix inequalities (LMIs), it is quite convenient to check in practice. Based on this criterion, a procedure is provided for designing fuzzy controller for such systems. This method is a universal one for controlling continuous hyperchaotic systems. As illustrated by its application to hyperchaotic Chen's system, the controller design is quite effective.

Takagi-Sugeno Fuzzy Load-Following Control of Nuclear Reactors Based on Reactor Point Kinetics Equations

2014 ◽  
Author(s):  
Xiuchun Luan ◽  
Jie Zhou ◽  
Yu Zhai
Keyword(s):  
Control System ◽  
Linear Models ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Neutron Density ◽  
Load Following ◽  
Wide Range ◽  
Takagi Sugeno ◽  
Operating Points

A state differential feedback control system based Takagi-Sugeno (T-S) fuzzy model is designed for load-following operation of nonlinear nuclear reactor whose operating points vary within a wide range. Linear models are first derived from the original nonlinear model on several operating points. Next the fuzzy controller is designed via using the parallel distributed compensation (PDC) scheme with the relative neutron density at the equilibrium point as the premise variable. Last the stability analysis is given by means of linear matrix inequality (LMI) approach, thus the control system is guaranteed to be stable within a large range. The simulation results demonstrate that the control system works well over a wide region of operation.

Fuzzy H∞ Synchronization for Chaotic Systems with Time-Varying Delay

2011 ◽  
Vol 66 (3-4) ◽  
pp. 151-160
Author(s):  
Choon Ki Ahn
Keyword(s):  
Chaotic Systems ◽  
External Disturbance ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Time Varying ◽  
Ts Fuzzy Model ◽  
Time Varying Delay ◽  
Norm Constraint ◽  
Takagi Sugeno ◽  
Varying Delay

In this paper, we propose a newH∞ synchronization method for fuzzy model based chaotic systems with external disturbance and time-varying delay. Based on Lyapunov-Krasovskii theory, Takagi- Sugeno (TS) fuzzy model, and linear matrix inequality (LMI) approach, the H∞ synchronization controller is presented to not only guarantee stable synchronization but also reduce the effect of external disturbance to an H∞ norm constraint. The proposed controller can be obtained by solving a convex optimization problem represented by the LMI. A simulation study is presented to demonstrate the validity of the proposed approach.

Robust-Optimal Fuzzy Model-Based Control of Flexible Spacecraft With Actuator Constraint

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Chokri Sendi ◽  
Mohammad A. Ayoubi
Keyword(s):  
Vibration Suppression ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Lyapunov Stability Theory ◽  
Flexible Spacecraft ◽  
Linear Matrix ◽  
Nonlinear Controller ◽  
Model Based ◽  
Stability Performance ◽  
Robust Fuzzy Controller

This paper presents a robust-optimal fuzzy controller for position and attitude stabilization and vibration suppression of a flexible spacecraft during antenna retargeting maneuver. The fuzzy controller is based on Takagi–Sugeno (T–S) fuzzy model and uses the parallel distributed compensator (PDC) technique to quadratically stabilize the closed-loop system. The proposed controller is robust to parameter and unstructured uncertainties of the model. We improve the performance and the efficiency of the controller by minimizing the upper bound of the actuator's amplitude and maximizing the uncertainties terms included in the T–S fuzzy model. In addition to actuator amplitude constraint, a fuzzy model-based observer is considered for estimating unmeasurable states. Using Lyapunov stability theory and linear matrix inequalities (LMIs), we formulate the problem of designing an optimal-robust fuzzy controller/observer with actuator amplitude constraint as a convex optimization problem. Numerical simulation is provided to demonstrate and compare the stability, performance, and robustness of the proposed fuzzy controller with a baseline nonlinear controller.

scholarly journals Nonlinear modelling and optimal control via Takagi-Sugeno fuzzy techniques: A quadrotor stabilization

2020 ◽  
Vol 71 (1) ◽  
pp. 1-10
Author(s):  
Miroslav Pokorný ◽  
Tomáš Dočekal ◽  
Danica Rosinová
Keyword(s):  
Closed Loop ◽  
Fuzzy Controller ◽  
Fitness Function ◽  
Fuzzy Model ◽  
Loop System ◽  
Linear Matrix ◽  
Closed Loop System ◽  
Linear Quadratic ◽  
Fuzzy Aggregation ◽  
Takagi Sugeno

AbstractUsing the principles of Takagi-Sugeno fuzzy modelling allows the integration of flexible fuzzy approaches and rigorous mathematical tools of linear system theory into one common framework. The rule-based T-S fuzzy model splits a nonlinear system into several linear subsystems. Parallel Distributed Compensation (PDC) controller synthesis uses these T-S fuzzy model rules. The resulting fuzzy controller is nonlinear, based on fuzzy aggregation of state controllers of individual linear subsystems. The system is optimized by the linear quadratic control (LQC) method, its stability is analysed using the Lyapunov method. Stability conditions are guaranteed by a system of linear matrix inequalities (LMIs) formulated and solved for the closed loop system with the proposed PDC controller. The additional GA optimization procedure is introduced, and a new type of its fitness function is proposed to improve the closed-loop system performance.

scholarly journals A probabilistic robust mixed H2/H∞ fuzzy control method for hypersonic vehicles based on reliability theory

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775415 ◽  
Author(s):  
Xiaomeng Yin ◽  
Xinming Li ◽  
Lei Liu ◽  
Yongji Wang ◽  
Xing Wei
Keyword(s):  
Fuzzy Control ◽  
Flight Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Control Design ◽  
Hypersonic Vehicle ◽  
Linear Matrix ◽  
Hypersonic Vehicles ◽  
Robust Fuzzy Controller

Achieving balance between robustness and performance is always a challenge in the hypersonic vehicle flight control design. In this research, we focus on dealing with uncertainties of the fuzzy control system from the viewpoint of reliability. A probabilistic robust mixed H2/ H∞ fuzzy control method for hypersonic vehicles is presented by describing the uncertain parameters as random variables. First, a Takagi–Sugeno fuzzy model is employed for the hypersonic vehicle nonlinear dynamics characteristics. Next, a robust fuzzy controller is developed by solving a reliability-based multi-objective linear matrix inequality optimization problem, in which the H2/ H∞ performance is optimized under the condition that the system is robustly reliable to uncertainties. By this method, the system performance and reliability can be taken into account simultaneously, which reduces the conservatism in the robust fuzzy control design. Finally, simulation results of a hypersonic vehicle demonstrate the feasibility and effectiveness of the presented method.

Further Results on T-S Fuzzy Controller Design Subject to Input Constraint

2008 ◽  
Vol 12 (2) ◽  
pp. 190-197 ◽  
Author(s):  
Hugang Han ◽  
Keyword(s):  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Model ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Input Constraint ◽  
Initial State ◽  
Control Gain ◽  
Matrix Variable ◽  
Takagi Sugeno

In general, when using the Takagi-Sugeno (T-S) fuzzy model to develop a control system, the state feedback control gain can be obtained by solving some linear matrix inequalities (LMIs). In this paper, we consider a class of nonlinear systems with input constraint (saturation). To obtain the control gain, we require to employ certain extra LMIs besides the general ones. As a result, all the LMIs are more conservative. At the same time, one of the extra LMIs confines the initial state to a region, which is referred to as an ellipsoid, and is relevant to a matrix variable in the LMIs. Therefore, the goals of this paper are: 1) making the ellipsoid as large as possible so that the initial state can be confined to the region easily and; 2) making all the LMIs more feasible to obtain the control gain.

Design of Robust Double-Fuzzy-Summation Nonparallel Distributed Compensation Controller for Chaotic Power Systems

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Navid Vafamand ◽  
Mohammad Hassan Khooban ◽  
Alireza Khayatian ◽  
Frede Blabbjerg
Keyword(s):  
Power Systems ◽  
Controller Design ◽  
Chaotic Behavior ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Ts Fuzzy Model ◽  
Fuzzy Membership Functions ◽  
Local Controller ◽  
Takagi Sugeno

This paper studies a systematic linear matrix inequality (LMI) approach for controller design of nonlinear chaotic power systems. The presented method is based on a Takagi–Sugeno (TS) fuzzy model, a double-fuzzy-summation nonparallel distributed compensation (non-PDC) controller, and a double-fuzzy-summation nonquadratic Lyapunov function (NQLF). Since time derivatives of fuzzy membership functions (MFs) appear in the NQLF-based controller design conditions, local controller design criteria is considered, and sufficient conditions are formulated in terms of LMIs. Compared with the existing works in hand, the proposed LMI conditions provide less conservative results due to the special structure of the NQLF and the non-PDC controller in which two fuzzy summations are employed. To evaluate the effectiveness of the presented approach, two practical benchmark power systems, which exhibit chaotic behavior, are considered. Simulation results and hardware-in-the-loop illustrate the advantages of the proposed method compared with the recently published works.

Robust Stabilization And Control Of Takagi-Sugeno Fuzzy Systems With Parameter Uncertainties And Disturbances Via State Feedback And Output Feedback

2020 ◽  
Vol 9 (3) ◽  
pp. 63-99
Author(s):  
Iqbal Ahammed A.K. ◽  
Mohammed Fazle Azeem
Keyword(s):  
Output Feedback ◽  
Fuzzy Systems ◽  
State Feedback ◽  
Robust Stabilization ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Ts Fuzzy Model ◽  
Takagi Sugeno ◽  
And Control

Most of the systems in the industry contain extreme non-linearity and uncertainties, which are hard to design and control utilizing general nonlinear systems. To conquer this sort of troubles, different plans have been produced in the most recent two decades, among which a popular methodology is Takagi-Sugeno fuzzy control. In this article, we present robust stabilization and control of Takagi-Sugeno (T-S) fuzzy systems with parameter uncertainties and disturbances. Initially, Takagi and Sugeno (TS) fuzzy model is used to represent a nonlinear system. Based on this T-S fuzzy model, fuzzy controller design schemes for state feedback and output feedback is also developed. Then, necessary conditions are derived for robust stabilization in the intelligence of Lyapunov asymptotic stability and are expressed in the arrangement of linear matrix inequalities (LMIs). The proposed system is implemented in the working platform of MATLAB and the simulation results are provided to illustrate the effectiveness of the proposed methods.

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