Integration of Design and Control: A Robust Control Approach Using MPC

2006 ◽  
Author(s):  
N. Chawankul ◽  
H. Budman ◽  
P.L. Douglas
Keyword(s):  
Robust Control ◽  
Control Approach ◽  
And Control

Design and Control of a Compact and Flexible Pneumatic Artificial Muscle Actuation System: Part Two—Robust Control

2011 ◽  
Author(s):  
Sai-Kit Wu ◽  
Garrett Waycaster ◽  
Tad Driver ◽  
Xiangrong Shen
Keyword(s):  
Robust Control ◽  
Nonlinear Model ◽  
Sliding Mode ◽  
Artificial Muscle ◽  
Control Approach ◽  
Actuation System ◽  
Highly Nonlinear ◽  
Pressure Dynamics ◽  
System Part ◽  
And Control

A robust control approach is presented in this part of the paper, which provides an effective servo control for the novel PAM actuation system presented in Part I. Control of PAM actuation systems is generally considered as a challenging topic, due primarily to the highly nonlinear nature of such system. With the introduction of new design features (variable-radius pulley and spring-return mechanism), the new PAM actuation system involves additional nonlinearities (e.g. the nonlinear relationship between the joint angle and the actuator length), which further increasing the control difficulty. To address this issue, a nonlinear model based approach is developed. The foundation of this approach is a dynamic model of the new actuation system, which covers the major nonlinear processes in the system, including the load dynamics, force generation from internal pressure, pressure dynamics, and mass flow regulation with servo valve. Based on this nonlinear model, a sliding mode control approach is developed, which provides a robust control of the joint motion in the presence of model uncertainties and disturbances. This control was implemented on an experimental setup, and the effectiveness of the controller demonstrated by sinusoidal tracking at different frequencies.

Integration of Design and Control: A Robust Control Approach Using MPC

2008 ◽  
Vol 85 (4) ◽  
pp. 433-446 ◽  
Author(s):  
N. Chawankul ◽  
La Ricardez Sandoval ◽  
H. Budman ◽  
P. L. Douglas
Keyword(s):  
Robust Control ◽  
Control Approach ◽  
And Control

Robust Control of a Parallel- Kinematic Nanopositioner

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Jingyan Dong ◽  
Srinivasa M. Salapaka ◽  
Placid M. Ferreira
Keyword(s):  
Robust Control ◽  
Mimo System ◽  
Model Identification ◽  
Control Design ◽  
Coupled Dynamics ◽  
Strongly Coupled ◽  
Control Approach ◽  
Domain Identification ◽  
Parallel Kinematic ◽  
And Control

This paper presents the design, model identification, and control of a parallel-kinematic XYZ nanopositioning stage for general nanomanipulation and nanomanufacturing applications. The stage has a low degree-of-freedom monolithic parallel-kinematic mechanism featuring single-axis flexure hinges. The stage is driven by piezoelectric actuators, and its displacement is detected by capacitance gauges. The control loop is closed at the end effector instead of at each joint, so as to avoid calibration difficulties and guarantee high positioning accuracy. This design has strongly coupled dynamics with each actuator input producing in multiaxis motions. The nanopositioner is modeled as a multiple input and multiple output (MIMO) system, where the control design forms an important constituent in view of the strongly coupled dynamics. The dynamics that model the MIMO plant is identified by frequency domain and time-domain identification methods. The control design based on modern robust control theory that gives a high bandwidth closed loop nanopositioning system, which is robust to physical model uncertainties arising from flexure-based mechanisms, is presented. The bandwidth, resolution, and repeatability are characterized experimentally, which demonstrate the effectiveness of the robust control approach.

A Robust Control Approach for Primary Frequency Regulation through Variable Speed Wind Turbines

2010 ◽  
Vol 130 (11) ◽  
pp. 1002-1009 ◽  
Author(s):  
Jorge Morel ◽  
Hassan Bevrani ◽  
Teruhiko Ishii ◽  
Takashi Hiyama
Keyword(s):  
Robust Control ◽  
Wind Turbines ◽  
Frequency Regulation ◽  
Variable Speed ◽  
Control Approach ◽  
Primary Frequency

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.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

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