Linear parameter variant modeling and parameter identification of a cable-driven micromanipulator for surgical robot

2018 ◽  
Vol 233 (5) ◽  
pp. 1828-1840 ◽  
Author(s):  
Wenjie Wang ◽  
Lingtao Yu ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Model ◽  
Joint Angle ◽  
Linear Time ◽  
Surgical Robot ◽  
Least Square ◽  
Feedback Signal ◽  
Model Parameters ◽  
Linear Parameter ◽  
Nonlinear Dynamic Model

This paper proposes a novel cable-driven micromanipulator for surgical robots. A single-joint principle prototype for surgical robot micromanipulator was manufactured to test the proposed design. Elasticity and friction were assessed to establish a joint angle estimator; estimator parameters were obtained by a combination of least square method and genetic algorithm. Angle closed-loop control was performed by considering the joint angle estimator output as the feedback signal. A nonlinear dynamic model was established in the state-space and described as a linear parameter variant model. The dynamic model parameters were determined via nonlinear modeling method, linear time invariant interpolator, and genetic algorithm. The angle estimator performs well and the linear parameter variant model efficiently estimates the micromanipulator’s behavior. The results presented here provide a workable foundation for surgical robot micromanipulator force estimation and control.

Topology Optimization for Piecewise Dynamic Models Using a Genetic Algorithm

2010 ◽  
Author(s):  
Yi Liu ◽  
Dragan Djurdjanovic
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Optimization Procedure ◽  
Nonlinear Dynamic System ◽  
Local Model ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Node Connectivity

It has been demonstrated in the previous research that the node connectivity in the graph encoding the topological neighborhood relationships between local models in a piecewise dynamic model may significantly affect the cooperative learning process. It was shown that a graph with a larger connectivity leads to a quicker learning adaption due to more rapidly decaying transients of the estimation of local model parameters. In the same time, it was shown that the accuracy could be degraded by a larger bias in the asymptotic portion of the estimations of local model parameters. The efforts in topology optimization should therefore strive towards a high accuracy of the asymptotic portion of the estimator of local model parameters while simultaneously accelerating the decay of the estimation transients. In this paper, we pursue minimization of the residual sum of squares of a piecewise dynamic model after a predetermined number of training steps. The optimization of inter-model topology is implemented via a genetic algorithm that manipulates adjacency matrices of the graph underlying the piecewise dynamic model. An example of applying the topology optimization procedure on a peicewise linear model of a highly nonlinear dynamic system is provided to show the efficacy of the new method.

A parameter identification method for robot dynamic models using a balancing mechanism

Robotica ◽  
1989 ◽  
Vol 7 (4) ◽  
pp. 327-337 ◽  
Author(s):  
T. G. Lim ◽  
H. S. Cho ◽  
W. K. Chung
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Least Square ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Robot Dynamics ◽  
Dynamic Parameters ◽  
Joint Torques ◽  
Motion Data ◽  
Identification Method

SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.

Optimal Design Parameters of Cab’s Isolation System for Vibratory Roller Using a Multi-Objective Genetic Algorithm

2018 ◽  
Vol 875 ◽  
pp. 105-112 ◽  
Author(s):  
Van Quynh Le ◽  
Khac Tuan Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Ride Comfort ◽  
Design Parameters ◽  
Nonlinear Dynamic Model ◽  
Nsga Ii ◽  
Isolation System ◽  
Multi Objective

In order to improve the vibratory roller ride comfort, a multi-objective optimization method based on the improved genetic algorithm NSGA-II is proposed to optimize the design parameters of cab’s isolation system when vehicle operates under the different conditions. To achieve this goal, 3D nonlinear dynamic model of a single drum vibratory roller was developed based on the analysis of the interaction between vibratory roller and soil. The weighted r.m.s acceleration responses of the vertical driver’s seat, pitch and roll angle of the cab are chosen as the objective functions. The optimal design parameters of cab’s isolation system are indentified based on a combination of the vehicle nonlinear dynamic model of Matlab/Simulink and the NSGA - II genetic algorithm method. The results indicate that three objective function values are reduced significantly to improve vehicle ride comfort.

Nonlinear Dynamic Model of a Two-Stage Pressure Relief Valve for Designers

2001 ◽  
Vol 124 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Pei-Sun Zung ◽  
Ming-Hwei Perng
Keyword(s):  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Dynamic Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Nonlinear Dynamic Model ◽  
Relief Valve ◽  
Two Stage ◽  
Pressure Relief ◽  
Wide Range

This paper presents a handy nonlinear dynamic model for the design of a two stage pilot pressure relief servo-valve. Previous surveys indicate that the performance of existing control valves has been limited by the lack of an accurate dynamic model. However, most of the existing dynamic models of pressure relief valves are developed for the selection of a suitable valve for a hydraulic system, and assume model parameters which are not directly controllable during the manufacturing process. As a result, such models are less useful for a manufacturer eager to improve the performance of a pressure valve. In contrast, model parameters in the present approach have been limited to dimensions measurable from the blue prints of the valve such that a specific design can be evaluated by simulation before actually manufacturing the valve. Moreover, the resultant model shows excellent agreement with experiments in a wide range of operating conditions.

scholarly journals Toward force detection of a cable-driven micromanipulator for a surgical robot based on disturbance observer

2017 ◽  
Vol 8 (2) ◽  
pp. 323-335
Author(s):  
Wenjie Wang ◽  
Lingtao Yu ◽  
Jing Yang
Keyword(s):  
Minimally Invasive ◽  
External Force ◽  
Disturbance Observer ◽  
Joint Angle ◽  
Surgical Robot ◽  
Force Sensing ◽  
Average Error ◽  
Feedback Signal ◽  
Loop Control ◽  
Tissue Properties

Abstract. Force sensing plays an important role in minimally invasive surgery (MIS). Force sensing makes it possible for the surgeon to feel the tissue properties and apply an appropriate level force and avoid tissue damage. The micromanipulators are compact and to allow appropriate disinfection, it is inappropriate to integrate sensors at the end of the micromanipulator. In this study, a new asymmetric cable-driven type of micromanipulator for a surgical robot was designed, and a joint angle estimator (JAE) was designed based on the dynamical model of the single cable-driven joint. Closed-loop control of the joint angle was carried out by regarding the JAE output as the feedback signal. On this basis, an external force estimator was designed using a disturbance observer (DOB). The experimental results show an average accuracy of the joint angle estimator of about −0.150°, with excellent control precision, the largest absolute error of about 0.95°, and an average error of 0.175°. The accuracy of the force estimator was at a high level during static loading. The estimated accuracy was 94 % at external force is greater than 1 N, and the estimated accuracy was 82 % for an external force of 0.3 N. These results predict that force sensing of a cable-driven micromanipulator in this paper can used to realize the micromanipulator's force feedback of a minimally invasive surgical robot.

CONTROL OF OBJECTS OF OIL REFINING MODEL

2017 ◽  
pp. 78-82
Author(s):  
L. G. Tugashova ◽  
K. L. Gorshkova
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Dynamic Model ◽  
Nonlinear Dynamic ◽  
Control Model ◽  
Oil Refining ◽  
Nonlinear Dynamic Model ◽  
Control Actions ◽  
Main Components ◽  
And Control

The approaches to improve the management of processes of oil refining. The description of the control model and the adjustment of the coefficients of the controller by using genetic algorithm. Selected basic adjustable parameters and control actions. The main components of the control circuit for the models are: limitations of the regression model, nonlinear dynamic model, the unit of optimization.

scholarly journals Real-Time Disturbances Estimating and Compensating of Nonlinear Dynamic Model for Underwater Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Yuexin Zhang ◽  
Lihui Wang
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Navigation System ◽  
Nonlinear Dynamic ◽  
Model Parameters ◽  
System State ◽  
Nonlinear Dynamic Model ◽  
External Disturbances ◽  
Cubature Kalman Filter ◽  
Nonlinear Disturbances

To reduce the deviation caused by the stochastic environmental disturbances, estimating these disturbances is required to compensate the navigation system. Based on the idea of Kalman filter using least-squares algorithm for optimal estimation, a nonlinear disturbances estimator which can be perfectly integrated with cubature Kalman filter (CKF) is proposed. For the nonlinear disturbances estimator, the disturbances are estimated by gain matrix, innovation sequences, and innovation covariance generated by CKF. The disturbances estimating and compensating algorithm consists of three parts. Firstly, the navigation system state space model is established based on nonlinear dynamic model of six degrees of freedom. Secondly, the external disturbances are estimated by using CKF and a nonlinear estimator. Finally, the disturbances compensation is carried out by improving the system state equation. In view of the uncertainty of the dynamic model and the randomness of external disturbances, numerical simulation experiments are conducted in the circumstances of sinusoidal disturbances, random disturbances, and uncertain model parameters. The results demonstrate that the proposed method can estimate disturbances effectively and improves navigation accuracy significantly.

Control Strategy for Space Manipulator Based on Genetic Algorithm Self-Tuning

2013 ◽  
Vol 706-708 ◽  
pp. 695-699
Author(s):  
Chao Wang ◽  
Zheng Hong Dong ◽  
Yong Ming Gao ◽  
Hang Yin
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Model ◽  
Control Strategy ◽  
Pid Controller ◽  
Joint Angle ◽  
Control Performance ◽  
Space Manipulator ◽  
Self Tuning ◽  
Control Accuracy

For questions of joint angle control of space manipulator, a method based on genetic algorithm PID self-tuning is proposed, which can improve the control accuracy of joint angle. We establish the dynamic model of the space manipulator and design the PID controller, and then using the genetic algorithm to tuning the PID parameters, and simulate in MATLAB. The result shows that the control performance of the space manipulator is improved, and the difficulties of traditional PID parameter regulation can be avoided by this method.

scholarly journals Linear parameter-varying model for a refuellable zinc–air battery

2020 ◽  
Vol 7 (12) ◽  
pp. 201107
Author(s):  
Woranunt Lao-atiman ◽  
Sorin Olaru ◽  
Sette Diop ◽  
Sigurd Skogestad ◽  
Amornchai Arpornwichanop ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Linear Time ◽  
Storage System ◽  
High Capacity ◽  
Operating Conditions ◽  
Nonlinear Behaviour ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Air Battery ◽  
Parameter Varying

Due to the increasing trend of using renewable energy, the development of an energy storage system (ESS) attracts great research interest. A zinc–air battery (ZAB) is a promising ESS due to its high capacity, low cost and high potential to support circular economy principles. However, despite ZABs' technological advancements, a generic dynamic model for a ZAB, which is a key component for effective battery management and monitoring, is still lacking. ZABs show nonlinear behaviour where the steady-state gain is strongly dependent on operating conditions. The present study aims to develop a dynamic model, being capable of predicting the nonlinear dynamic behaviour of a refuellable ZAB, using a linear parameter-varying (LPV) technique. The LPV model is constructed from a family of linear time-invariant models, where the discharge current level is used as a scheduling parameter. The developed LPV model is benchmarked against linear and nonlinear model counterparts. Herein, the LPV model performs remarkably well in capturing the nonlinear behaviour of a ZAB. It significantly outperforms the linear model. Overall, the LPV approach provides a systematic way to construct a robust dynamic model which well represents the nonlinear behaviour of a ZAB.

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