Adaptive Input Shaping for Nonlinear Systems: A Case Study

2006 ◽  
Vol 129 (2) ◽  
pp. 219-223 ◽  
Author(s):  
John Stergiopoulos ◽  
Anthony Tzes
Keyword(s):  
System Theory ◽  
Nonlinear Systems ◽  
Input Shaping ◽  
Relative Time ◽  
Pendulum System ◽  
Input Shaper ◽  
Command Input ◽  
Damped Systems ◽  
Theoretical Results

Input shaping is a technique that seeks to reduce residual vibrations of lightly damped systems through modification of the command input to the system. Although several input shaping techniques have been derived primarily from linear system theory, theoretical results are hard to be traced for their application to nonlinear systems. In most of the reported cases, a fixed shaper is designed based on the linearized version around an operating point of the nonlinear system. In this paper, an adaptive form of the input shaper is proposed for a class of nonlinear lightly damped systems. The adaptive shaper adjusts the magnitude and relative time difference between its impulses according to the instant frequency and damping of the linearized systems. The efficacy of the proposed scheme and its comparison to a fixed shaper is investigated through its application to a pendulum system. The adaptive shaper’s parameters vary according to the pendulum’s angle. The illustrative examples indicate the deficiencies of the fixed case and demonstrate the efficacy of the designed controller.

Command Shaping for Flexible Systems Subject to Constant Acceleration Limits

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Jon Danielson ◽  
Jason Lawrence ◽  
William Singhose
Keyword(s):  
Effective Means ◽  
Input Shaping ◽  
Constant Force ◽  
Flexible Systems ◽  
Common Occurrence ◽  
Constant Acceleration ◽  
Command Shaping ◽  
Input Shaper ◽  
Drive Systems ◽  
Theoretical Results

Input shaping is an effective means of eliminating vibration in many types of flexible systems. This paper discusses how input shaper performance is affected by a fixed acceleration limit. This type of limit is a common occurrence in many mechanical drive systems because it corresponds to a constant force or torque input. It is shown that some input shapers are not affected by an acceleration limit under certain conditions. A test criterion is developed to determine what types of input shapers are negatively affected, and a method is proposed to compensate for the detrimental effects of the constant acceleration limit. Experimental results from an industrial crane support the main theoretical results.

Robust Input Shaper Control Design for Parameter Variations in Flexible Structures

1999 ◽  
Vol 122 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Lucy Y. Pao ◽  
Mark A. Lau
Keyword(s):  
Design Method ◽  
Structural Parameters ◽  
Flexible Structures ◽  
Control Design ◽  
Input Shaping ◽  
Input Shaper ◽  
Modeling Errors ◽  
Parameter Variations ◽  
Damped Systems ◽  
Parameter Values

Input shaping has been shown to yield good performance in the control of flexible structures while being insensitive to modeling errors. However, previous studies do not take into account the distributions of the parameter variations. We develop a new input shaping method that allows the ranges of system parameter values to be weighted according to the expected modeling errors. Comparisons with previously proposed input shaper designs are presented to illustrate the qualities of the new input shaper design method. These new shapers will be shown to have better robustness under uncertainty in structural parameters and shorter shaper lengths for lightly damped systems. [S0022-0434(00)02201-2]

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.

Unbalance Response of a Dual Rotor System: Theory and Experiment

1993 ◽  
Vol 115 (4) ◽  
pp. 427-435 ◽  
Author(s):  
K. Gupta ◽  
K. D. Gupta ◽  
K. Athre
Keyword(s):  
System Theory ◽  
Transfer Matrix ◽  
Mode Shape ◽  
Reasonable Agreement ◽  
Complex Variables ◽  
Rotor System ◽  
Experimental Results ◽  
Unbalance Response ◽  
Theoretical Results ◽  
Theory And Experiment

A dual rotor rig is developed and is briefly discussed. The rig is capable of simulating dynamically the two spool aeroengine, though it does not physically resemble the actual aeroengine configuration. Critical speeds, mode shape, and unbalance response are determined experimentally. An extended transfer matrix procedure in complex variables is developed for obtaining unbalance response of dual rotor system. Experimental results obtained are compared with theoretical results and are found to be in reasonable agreement.

scholarly journals Approximation of Linearized Systems to a Class of Nonlinear Systems Based on Dynamic Linearization

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 854
Author(s):  
Raquel S. Rodríguez ◽  
Gilberto Gonzalez Avalos ◽  
Noe Barrera Gallegos ◽  
Gerardo Ayala-Jaimes ◽  
Aaron Padilla Garcia
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Nonlinear Dynamic ◽  
Bond Graph ◽  
Physical Domain ◽  
Graph Models ◽  
Simple Task ◽  
Analysis And Synthesis ◽  
Simulation Results

An alternative method to analyze a class of nonlinear systems in a bond graph approach is proposed. It is well known that the analysis and synthesis of nonlinear systems is not a simple task. Hence, a first step can be to linearize this nonlinear system on an operation point. A methodology to obtain linearization for consecutive points along a trajectory in the physical domain is proposed. This type of linearization determines a group of linearized systems, which is an approximation close enough to original nonlinear dynamic and in this paper is called dynamic linearization. Dynamic linearization through a lemma and a procedure is established. Therefore, linearized bond graph models can be considered symmetric with respect to nonlinear system models. The proposed methodology is applied to a DC motor as a case study. In order to show the effectiveness of the dynamic linearization, simulation results are shown.

scholarly journals Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

2011 ◽  
Vol 8 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Mohamed Bahita ◽  
Khaled Belarbi
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Feedback Linearization ◽  
Pendulum System ◽  
Rbf Network ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Control Gain ◽  
On Line ◽  
Neural Feedback

In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

scholarly journals Combining Input Shaping and Adaptive Model-Following Control for Vibration Suppression

2021 ◽  
Vol 25 (1) ◽  
pp. 56-63
Author(s):  
Jinhua She ◽  
Lulu Wu ◽  
Zhen-Tao Liu ◽  
◽  
◽  
...  
Keyword(s):  
Vibration Suppression ◽  
Golden Section ◽  
Input Shaping ◽  
Adaptive Model ◽  
Servo Systems ◽  
Elastic Load ◽  
Model Following Control ◽  
Model Following ◽  
Input Shaper ◽  
Precision Motion

Vibration suppression in servo systems is significant in high-precision motion control. This paper describes a vibration-suppression method based on input shaping and adaptive model-following control. First, a zero vibration input shaper is used to suppress the vibration caused by an elastic load to obtain an ideal position output. Then, a configuration that combines input shaping with model-following control is developed to suppress the vibration caused by changes of system parameters. Finally, analyzing the percentage residual vibration reveals that it is effective to employ the sum of squared position error as a criterion. Additionally, a golden-section search is used to adjust the parameters of a compensator in an online fashion to adapt to the changes in the vibration frequency. A comparison with other input shaper methods shows the effectiveness and superiority of the developed method.

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