Dynamic Modeling and Control of a Ball-Joint-Like Variable-Reluctance Spherical Motor

1992 ◽  
Author(s):  
Kok-Meng Lee ◽  
Xiao-an Wang
Keyword(s):  
Dynamic Modeling ◽  
Ball Joint ◽  
Modeling And Control ◽  
Spherical Motor ◽  
Variable Reluctance ◽  
And Control

Dynamic Modeling and Control of a Ball-Joint-Like Variable-Reluctance Spherical Motor

1996 ◽  
Vol 118 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Kok-Meng Lee ◽  
Ronald B. Roth ◽  
Zhi Zhou
Keyword(s):  
Dynamic Modeling ◽  
Rotor Dynamics ◽  
Inverse Model ◽  
Control Input ◽  
Forward Dynamics ◽  
Modeling And Control ◽  
Spherical Motor ◽  
Variable Reluctance ◽  
Robot Wrist ◽  
And Control

Examination of existing joint designs for robot wrist applications has indicated that a spherical wrist motor offers a major performance advantage in trajectory planning and control as compared to the popular three-consecutive-rotational joint wrist. The tradeoff, however, is the complexity of the dynamic modeling and control. This paper presents the dynamic modeling and the control strategy of a three degree-of-freedom (DOF) variable-reluctance (VR) spherical motor which presents some attractive possibilities by combining pitch, roll, and yaw motion in a single joint. The spherical motor dynamics consist of the rotor dynamics and a torque model. The torque model is described as a function of coil excitations and a permeance model in terms of the relative position between the rotor and the stator. Both the forward dynamics which determine the rotor motion as a result of activating the electromagnetic coils and the inverse model which determines the coil excitations required to generate the desired torque are derived in this paper. The solution to the forward dynamics of the spherical motor is unique, but the inverse model has many solutions and therefore an optimization is desired. Experimental results verifying the dynamic model are presented. The control of a VR spherical motor consists of two parts; namely, the control of the rotor dynamics with the actuating torque as system input, and the determination of the optimal electrical inputs for a specified actuating torque. The simulation results and implementation issues in determining the optimal control input vectors are addressed. It is expected that the resulting analysis will serve as a basis for dynamic modeling, motion control development, and design optimization of the VR spherical motor.

Riccati Discrete Time Transfer Matrix Method for Dynamic Modeling and Simulation of an Underwater Towed System

2012 ◽  
Vol 79 (4) ◽  
Author(s):  
Guoping Wang ◽  
Bao Rong ◽  
Ling Tao ◽  
Xiaoting Rui
Keyword(s):  
Modeling And Simulation ◽  
Dynamic Modeling ◽  
Computational Efficiency ◽  
Finite Segment ◽  
Modeling And Control ◽  
Global Dynamic ◽  
Transfer Equations ◽  
High Matrix ◽  
Towed Cable ◽  
And Control

Efficient, precise dynamic modeling and control of complex underwater towed systems has become a research focus in the field of multibody dynamics. In this paper, based on finite segment model of cable, by defining the new state vectors and deducing the new transfer equations of underwater towed systems, a new highly efficient method for dynamic modeling and simulation of underwater towed systems is presented and the pay-out/reel-in process of towed cable is studied. The computational efficiency and numerical stability of the proposed method are discussed. When using the method to study the dynamics of underwater towed systems, it avoids the global dynamic equations of system, and simplifies solving procedure. Irrespective of the degree of freedom of underwater towed system, the matrices involved in the proposed method are always very small, which greatly improve the computational efficiency and avoids the computing difficulties caused by too high matrix orders for complex underwater towed systems. Formulations of the method as well as numerical simulations are given to validate the proposed method.

scholarly journals A Wind Climate Dynamic Modeling and Control Using Weibull and Extreme Value Distribution System

2019 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Tim Chen ◽  
Alfred Hausladen ◽  
Jonathan Sstamler ◽  
Dneil Granger ◽  
Abu Hurayraasiv Khanand ◽  
...  
Keyword(s):  
Dynamic Modeling ◽  
Distribution System ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Modeling And Control ◽  
Wind Climate ◽  
And Control ◽  
Climate Dynamic
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