scholarly journals Design of Explicit Fuzzy Prediction Controller for Constrained Nonlinear Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Yan Yan ◽  
Baili Su
Keyword(s):  
Nonlinear Systems ◽  
Predictive Control ◽  
Control Method ◽  
Fuzzy Model ◽  
Main Idea ◽  
Closed Loop System ◽  
Nonlinear Predictive Control ◽  
Predictive Controller ◽  
Constrained Nonlinear Systems ◽  
Fuzzy Predictive Control

This paper presents an explicit fuzzy predictive control method for a class of nonlinear systems with constrained inputs. The main idea is to construct a terminal invariant set and explicit predictive controller with affine input on the basis of T-S fuzzy model. This method need not compute the complex nonconvex nonlinear programming problem of earlier nonlinear predictive control methods and decreases the number of optimization variables and guarantees stability of the closed-loop system. The simulation results on a numerical example show the validity of the method presented in this paper.

Takagi–Sugeno Fuzzy Predictive Control for a Class of Nonlinear System With Constrains and Disturbances

2015 ◽  
Vol 10 (5) ◽  
Author(s):  
Bin Wang ◽  
Jianwei Zhang ◽  
Delan Zhu ◽  
Diyi Chen
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Predictive Control ◽  
Three Dimensional ◽  
Fuzzy Model ◽  
Control Input ◽  
Chen System ◽  
External Disturbances ◽  
Takagi Sugeno ◽  
Fuzzy Predictive Control

This paper investigates the fuzzy predictive control for a class of nonlinear system with constrains under the condition of noise. Based on the fuzzy linearization theory, a class of nonlinear systems can be described by the Takagi–Sugeno (T–S) fuzzy model. The T–S fuzzy model and predictive control are combined to stabilize the proposed class of nonlinear system, and the detailed mathematical derivation is given. Moreover, the designed controller has been optimized even if the system is constrained by output and control input, or perturbed by external disturbances. Finally, numerical simulations including three-dimensional Lorenz system, four-dimensional Chen system and five-dimensional nonlinear system with external disturbances are presented to demonstrate the universality and effectiveness of the proposed scheme. The approach proposed in this paper is simple and easy to implement and also provides reference for relevant nonlinear systems.

Vehicle stability enhancement and rollover prevention by a nonlinear predictive control method

2018 ◽  
Vol 41 (8) ◽  
pp. 2135-2149 ◽  
Author(s):  
M. Selçuk Arslan ◽  
Mert Sever
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Vertical Load ◽  
Steering Control ◽  
Yaw Rate ◽  
Rollover Prevention ◽  
Nonlinear Predictive Control ◽  
Predictive Controller ◽  
The Stability ◽  
Stability Enhancement

In this study, a nonlinear predictive control method is developed for the active steering control of a sport utility vehicle. The method is tested on a nonlinear mathematical model of an 11-degree-of-freedom vehicle. The system performance is evaluated by considering that the control law must keep the actual yaw rate close to the desired yaw rate and minimizing the vertical load changes at each wheel. The latter is proposed for this work. The vertical load changes play an important role in the dynamics and the stability of the system. The effectiveness of the control method is demonstrated through numerical simulation by using a vehicle model that includes three case studies: rapid lane change at low and high velocities and the fishhook manoeuvre. The results show that the stability of the vehicle is maintained and its rollover propensity is decreased. In addition, the proposed controller is compared with a well-known linear model predictive controller.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

Damage-Mitigating Predictive Control of Airfoil Flutter for a General Hypersonic Flight Vehicle

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Xiaohui Zhang ◽  
Yuhui Wang ◽  
Xingkai Feng ◽  
Siyuan Hou
Keyword(s):  
Predictive Control ◽  
Degrees Of Freedom ◽  
Unsteady Aerodynamics ◽  
Flight Vehicle ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Online Simulation ◽  
Nonlinear Predictive Control ◽  
Predictive Controller ◽  
Damage Accumulation Model

Abstract This paper aims to investigate the airfoil flutter damage-mitigating problem in hypersonic flow. A new adaptive robust nonlinear predictive control law is designed in this paper to mitigate the damage during airfoil flutter of a generic hypersonic flight vehicle. A three-degrees-of-freedom airfoil dynamic motion model is established, in which the third piston theory is employed to derive the unsteady aerodynamics. Then, the complicated responses of the hypersonic airfoil flutter model are analyzed. In order to mitigate the damage of the airfoil, a predictive controller is designed by introducing an adaptive predictive period, and asymptotical stability analysis of the robust nonlinear predictive controller is performed. Subsequently, based on the nonlinear aerodynamics of the airfoil and damage accumulation model, the damage of the airfoil is observed online. Simulation results illustrate the effectiveness of the proposed method.

scholarly journals Fuzzy Predictive Control of Wind Turbine Systems Via Singular Perturbation Theory

2018 ◽  
Vol 72 ◽  
pp. 01008
Author(s):  
Yan Zhang ◽  
Zhong Yang ◽  
Haifei Si
Keyword(s):  
Perturbation Theory ◽  
Singular Perturbation ◽  
Wind Turbine ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Singular Perturbation Theory ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Fuzzy Predictive Control

In this paper, the maximum power point tracking problem of variable-speed wind turbine systems is studied. The mechanical and electromagnetic dynamics of the wind turbine systems are both taken into account. Since the electromagnetic parts change much faster than the turbine part, the whole system is modeled in two time scales base on singular perturbation theory. And a T-S fuzzy model predictive control strategy is then developed. The controller is validated with a wind turbine simulator. The results have shown better performance in comparison with existing controllers.

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