scholarly journals Adaptive MIMO Supervisory Control Design Using Modeling Error

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Zhi-Ren Tsai ◽  
Yau-Zen Chang
Keyword(s):  
Nonlinear Systems ◽  
Supervisory Control ◽  
Fuzzy Model ◽  
Control Law ◽  
Modeling Error ◽  
Nonminimum Phase ◽  
Phase Dynamics ◽  
Control Scheme ◽  
The Face ◽  
Aircraft System

This paper proposes an adaptive control scheme for nonlinear systems with significant nonminimum phase dynamics. The scheme is composed of an inner-level adaptive fuzzy PD control law and an outer-level supervisory control law. Importantly, the inner-level controller of the two-level scheme is designed based on a fuzzy model, which takes nonminimum phase phenomenon and modeling error explicitly into account. The scheme is both much simpler in design and more applicable to general nonlinear systems when compared with most existing nonlinear controllers. Effectiveness of the proposed control strategy is demonstrated by numerical simulation of the control of a five-degree-of-freedom aircraft system in the face of bursting disturbances.

Sliding Mode Based LMI Criterion for Robust Stabilization of Uncertain Fractional Order Nonlinear Systems

2013 ◽  
Author(s):  
Sara Dadras ◽  
YangQuan Chen
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Nonlinear Dynamical Systems ◽  
Sliding Mode ◽  
Control Technique ◽  
Control Law ◽  
Mode Control ◽  
Systems Model ◽  
Control Scheme

A robust sliding mode control (SMC) technique is introduced in this paper for a class of fractional order (FO) nonlinear dynamical systems. Using the sliding mode control technique, a sliding surface is determined and the control law is established. A new LMI criterion based on the sliding mode control law is derived to make the states of the FO nonlinear system asymptotically gravitate toward the origin which can work for any order of the system, 0<q<2. The designed control scheme can also control the uncertain FO nonlinear systems, i.e. the controller is robust against the system uncertainty and guarantees the property of asymptotical stability. The advantage of the method is that the control scheme does not depend on the order of systems model and it is fairly simple. So, there is no complexity in the application of our proposed method. An illustrative simulation result is given to demonstrate the effectiveness of the proposed robust sliding mode control design.

Discrete Sliding-Mode Control for a Class of T-S Fuzzy Models with Modeling Error

2014 ◽  
Vol 18 (6) ◽  
pp. 908-917 ◽  
Author(s):  
Hugang Han ◽  
◽  
Hak-Keung Lam ◽  
Keyword(s):  
Sliding Mode ◽  
Fuzzy Model ◽  
Variable Structure ◽  
System Stability ◽  
Modeling Error ◽  
Structure Control ◽  
Auxiliary Component ◽  
Control Scheme ◽  
Adaptive Laws ◽  
Discrete Sliding Mode Control

This paper proposes a discrete sliding-mode controller for a class of nonlinear systems described by a T-S fuzzy model subject to modeling error, which may influence the system performance and the overall system stability. While most of existing literature treats the modeling error under the so-called parallel distributed compensation framework by using some norm-bounded matrices, the proposed control scheme in this paper integrates a feedback component, which mainly consists of fuzzy approximators to deal with the modeling error and an auxiliary component of the variable structure control with a sector to guarantee the global stability of the closed-loop system when the system state travels outside the sector. With the consideration of system stability, adaptive laws adjusting the parameters in the system are developed based on the Lyapunov synthesis approach. Finally, simulation results will confirm the effectiveness of the approach proposed in this paper.

INDIRECT ADAPTIVE CONTROL OF NONLINEAR SYSTEMS BASED ON BILINEAR NEURO-FUZZY APPROXIMATION

2013 ◽  
Vol 23 (05) ◽  
pp. 1350022 ◽  
Author(s):  
YIANNIS BOUTALIS ◽  
MANOLIS CHRISTODOULOU ◽  
DIMITRIOS THEODORIDIS
Keyword(s):  
Nonlinear Systems ◽  
A Priori ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Parametric Model ◽  
Control Law ◽  
Sampled Data ◽  
Approximation Model ◽  
Neuro Fuzzy ◽  
Indicator Functions

In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method.

scholarly journals Disturbance compensation-based output feedback adaptive control for shape memory alloy actuator system

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199399
Author(s):  
Xiaoguang Li ◽  
Bi Zhang ◽  
Daohui Zhang ◽  
Xingang Zhao ◽  
Jianda Han
Keyword(s):  
Adaptive Control ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Control Method ◽  
Control Law ◽  
Reference Trajectory ◽  
Disturbance Compensation ◽  
Comparison Results ◽  
Control Scheme ◽  
Actuator System

Shape memory alloy (SMA) has been utilized as the material of smart actuators due to the miniaturization and lightweight. However, the nonlinearity and hysteresis of SMA material seriously affect the precise control. In this article, a novel disturbance compensation-based adaptive control scheme is developed to improve the control performance of SMA actuator system. Firstly, the nominal model is constructed based on the physical process. Next, an estimator is developed to online update not only the unmeasured system states but also the total disturbance. Then, the novel adaptive controller, which is composed of the nominal control law and the compensation control law, is designed. Finally, the proposed scheme is evaluated in the SMA experimental setup. The comparison results have demonstrated that the proposed control method can track reference trajectory accurately, reject load variations and stochastic disturbances timely, and exhibit satisfactory robust stability. The proposed control scheme is system independent and has some potential in other types of SMA-actuated systems.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

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