Adaptive Robust Control of Circular Machining Contour Error Using Global Task Coordinate Frame

2013 ◽  
Author(s):  
Tyler A. Davis ◽  
Yung C. Shin ◽  
Bin Yao
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Research Work ◽  
Approximation Error ◽  
Adaptive Robust Control ◽  
Contour Error ◽  
Coordinate Frame ◽  
Tool Deflection ◽  
Machining Processes ◽  
Highly Nonlinear

The contour error of machining processes is defined as the difference between the desired and actual produced shape. Two major factors contributing to contour error are axis position error and tool deflection. A large amount of research work formulates the contour error in convenient locally defined task coordinate frames that are subject to significant approximation error. The more accurate global task coordinate frame (GTCF) can be used, but transforming the control problem to the GTCF leads to a highly nonlinear control problem. An adaptive robust control (ARC) approach is designed to control machine position in the GTCF, while directly accounting for tool deflection, to minimize the contour error. The combined GTCF/ARC approach is experimentally validated by applying the control to circular contours on a three axis milling machine. The results show that the proposed approach reduces contour error in all cases tested.

Adaptive Robust Control of Circular Machining Contour Error Using Global Task Coordinate Frame

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Tyler A. Davis ◽  
Yung C. Shin ◽  
Bin Yao
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Research Work ◽  
Approximation Error ◽  
Adaptive Robust Control ◽  
Contour Error ◽  
Coordinate Frame ◽  
Tool Deflection ◽  
Machining Processes ◽  
Highly Nonlinear

The contour error (CE) of machining processes is defined as the difference between the desired and actual produced shape. Two major factors contributing to CE are axis position error and tool deflection. A large amount of research work formulates the CE in convenient locally defined task coordinate frames that are subject to significant approximation error. The more accurate global task coordinate frame (GTCF) can be used, but transforming the control problem to the GTCF leads to a highly nonlinear control problem. An adaptive robust control (ARC) approach is designed to control machine position in the GTCF, while additionally accounting for tool deflection, to minimize the CE. The combined GTCF/ARC approach is experimentally validated by applying the control to circular contours on a three axis milling machine. The results show that the proposed approach reduces CE in all cases tested.

Adaptive robust control of machining force and contour error with tool deflection using global task coordinate frame

2016 ◽  
Vol 232 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Tyler A Davis ◽  
Yung C Shin ◽  
Bin Yao
Keyword(s):  
High Performance ◽  
Milling Process ◽  
Adaptive Robust Control ◽  
Contour Error ◽  
Coordinate Frame ◽  
Tool Deflection ◽  
Peripheral Milling ◽  
Robust Controller ◽  
Control Approach ◽  
Machining Force

Peripheral milling process productivity or quality can be improved by controlling either cutting force or contour error. While each means for improvement is often addressed individually, efforts to control both aspects simultaneously are less common in the literature. This article describes an approach to control both the contour error and force using an adaptive robust controller. The axes dynamic behavior and tool deflection are considered as the two major sources of error expressly considered in the control design and are embedded in a global task coordinate frame representation of contour error. The adaptive control component maintains high-performance control of both force and contour error in the presence of significant model error or external disturbances. The control approach is implemented on a three-axis machine tool for validation. Experimental results indicate that significant improvements to both contour error and force regulation have been achieved.

Coordinated motion control of a pneumatic-cylinder-driven biaxial gantry for contour tracking tasks

2017 ◽  
Vol 40 (7) ◽  
pp. 2249-2258 ◽  
Author(s):  
Deyuan Meng ◽  
Aimin Li ◽  
Fei Chen ◽  
Kai Zhang
Keyword(s):  
Robust Control ◽  
Motion Control ◽  
Adaptive Robust Control ◽  
Contour Error ◽  
Pneumatic Cylinder ◽  
Coordinate Frame ◽  
Practical Implementation ◽  
Model Parameters ◽  
Contour Tracking ◽  
Coordinated Motion

In this paper, coordinated motion control of the pneumatic-cylinder-driven biaxial gantry for precise contour tracking is investigated. An adaptive robust coordinated motion controller is developed by incorporating the task coordinate formulation into the adaptive robust control architecture. Specifically, a task coordinate frame is used to approximately calculate the contour error, which is utilized for controller design to generate coordination between two axes. Furthermore, the proposed controller utilizes online parameter adaptation to estimate some important unknown model parameters, and employs a robust control law to attenuate the effects of parameter estimation errors, unmodelled dynamics and external disturbances. Therefore, certain transient contouring performance and steady-state contour tracking accuracy can be guaranteed. Extensive comparative experimental results obtained verify the effectiveness of the proposed coordinated motion control strategy and its performance robustness to sudden disturbances in practical implementation.

Observer-based robust control for flexible-joint robot manipulators: A state-dependent Riccati equation-based approach

2020 ◽  
Vol 42 (16) ◽  
pp. 3135-3155
Author(s):  
Neda Nasiri ◽  
Ahmad Fakharian ◽  
Mohammad Bagher Menhaj
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Riccati Equation ◽  
Power Transmission ◽  
Control Method ◽  
Main Idea ◽  
Robotic Systems ◽  
Flexible Joint ◽  
State Dependent ◽  
Highly Nonlinear

In this paper, the robust control problem is tackled by employing the state-dependent Riccati equation (SDRE) for uncertain systems with unmeasurable states subject to mismatched time-varying disturbances. The proposed observer-based robust (OBR) controller is applied to two highly nonlinear, coupled and large robotic systems: namely a manipulator presenting joint flexibility due to deformation of the power transmission elements between the actuator and the robot known as flexible-joint robot (FJR) and also an FJR incorporating geared permanent magnet DC motor dynamics in its dynamic model called electrical flexible-joint robot (EFJR). A novel state-dependent coefficient (SDC) form is introduced for uncertain EFJRs. Rather than coping with the OBR control problem for such complex uncertain robotic systems, the main idea is to solve an equivalent nonlinear optimal control problem where the uncertainty and disturbance bounds are incorporated in the performance index. The stability proof is presented. Solving the complicated robust control problem for FJRs and EFJRs subject to uncertainty and disturbances via a simple and flexible nonlinear optimal approach and no need of state measurement are the main advantages of the proposed control method. Finally, simulation results are included to verify the efficiency and superiority of the control scheme.

scholarly journals Modeling and adaptive robust wavelet control for a liquid container system under slosh and uncertainty

2020 ◽  
pp. 002029402095248
Author(s):  
Mohammad Abdulrahman Al-Mashhadani
Keyword(s):  
Robust Control ◽  
Feedback Linearization ◽  
High Speed ◽  
Adaptive Robust Control ◽  
Control Technique ◽  
Ground Vehicles ◽  
New Approach ◽  
Liquid Slosh ◽  
Attenuation Level ◽  
Highly Nonlinear

Liquid sloshing in moving or stationary containers and flexible uncertainty caused by the slosh are considered to be the most probable causing unexpected coupling effects on the dynamics of many systems such as aerospace, ground vehicles, and high speed industries arms. The coupling of dynamic liquid slosh in a container system with the uncertainty caused by the sensors or dampers is rare documented and this coupling can be considered as a highly nonlinear system. In this paper, an investigation is presented to demonstrate a new approach for enabling the reduction of the liquid slosh and uncertainty by implementing adaptive robust wavelet control technique. Starting by creating the mathematical dynamic model for the nonlinear slosh coupled by uncertainty, adaptive robust control based wavelet transform is applied for calculating optimal motion that minimize residual slosh and uncertainty. Subsequently the adaptive robust control based wavelet network approximation and the appropriate parameter algorithms for the container system with slosh and uncertainty are derived to achieve the feedback linearization, adaptive control, and H∞ tracking performance. The simulation results show that the effects of slosh errors and external uncertainty can be successfully attenuated within a desired attenuation level.

scholarly journals Adaptive Robust Control for Uncertain Systems via Data-Driven Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Qingliang Zeng
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Adaptive Learning ◽  
Uncertain Systems ◽  
Adaptive Robust Control ◽  
System Stability ◽  
Data Driven ◽  
Algebraic Riccati Equation ◽  
Stability And Convergence ◽  
Online Data

Although solving the robust control problem with offline manner has been studied, it is not easy to solve it using the online method, especially for uncertain systems. In this paper, a novel approach based on an online data-driven learning is suggested to address the robust control problem for uncertain systems. To this end, the robust control problem of uncertain systems is first transformed into an optimal problem of the nominal systems via selecting an appropriate value function that denotes the uncertainties, regulation, and control. Then, a data-driven learning framework is constructed, where Kronecker’s products and vectorization operations are used to reformulate the derived algebraic Riccati equation (ARE). To obtain the solution of this ARE, an adaptive learning law is designed; this helps to retain the convergence of the estimated solutions. The closed-loop system stability and convergence have been proved. Finally, simulations are given to illustrate the effectiveness of the method.

Path Control of Surface Ships Using Sliding Modes

1992 ◽  
Vol 36 (02) ◽  
pp. 141-153 ◽  
Author(s):  
Fotis A. Papoulias ◽  
Anthony J. Healey
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Sliding Mode ◽  
Operational Conditions ◽  
Surface Ship ◽  
Path Control ◽  
Highly Nonlinear ◽  
Robust Control Design ◽  
Cross Track ◽  
Guidance Laws

The surface ship path control problem is formulated as a nonlinear state space control problem subject to disturbances, modeling errors, and parameter uncertainty. A "destroyer study" ship using a nonlinear maneuvering model is used as the system model. The system to be controlled is highly nonlinear with parameters that vary with speed and operational conditions. Uncertainty in the force coefficients, as well as in disturbances of the seaway lead to the need for a robust control for navigational accuracy. This paper investigates the use of cross track error and line-of-sight guidance laws with a sliding mode autopilot for path control of surface ships, and presents results based on computer simulations using the model described above. The results illustrate the simplicity and effectiveness of the robust control design for compensating the nonlinearities and disturbance effects with improvements in the navigational accuracy. Comparisons between the two guidance schemes are presented and guidelines are developed with respect to proper guidance selection.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

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