Numerical Discretization Estimation for Ordinary Differential Equation via Hybrid Discretization

Simulation of control system is mostly developed based on the use of ordinary differential equation (ODE). With the advancement of technologies especially in term of real time computation, the traditional numerical approaches seem outdated to fit in to the current real-time discrete systems. Numerical method such as Euler’s approach has inaccurate approximation as compared to other methods such as Heun’s (RK2), Runge-Kutta (RK4), and Adams-Bashforth (AB2) methods, and these methods on the other hands suffers from high calculation time. In this work, Hybrid Discretization (HD) method is proposed to solve both approximation accuracy and calculation speed of the discretization. HD adapts RK2 method to correct the approximation error for one-to-ten step depending on the sampling time. Later, the system will return to Forward Euler’s method to maintain the calculation speed. The HD is applied to two first order ODE test functions and the result of this work shows a significance improvement in terms of the accuracy of the approximation and slight improvement in term of the calculation time. The accuracy of about 9% is obtained as compared to a similar step time in Euler’s, and comparable calculation time is maintained. In conclusion, it is shown that this new technique of discretization has better approximation than its counterparts and the method can serve as an important simulation tool in modeling and control of dynamic system.

[1]  Yongchang Zhang,et al.  A Universal Multiple-Vector-Based Model Predictive Control of Induction Motor Drives , 2018, IEEE Transactions on Power Electronics.

[2]  Jin Zhao,et al.  A sliding mode flux observer for predictive torque controlled induction motor drive , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[3]  Guanghui Sun,et al.  Contouring Control of Linear Motor Direct-Drive X-Y Table Via Chattering-Free Discrete-Time Sliding Mode Control , 2018, 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE).

[4]  Guanghui Sun,et al.  Discrete-Time Fractional Order Terminal Sliding Mode Tracking Control for Linear Motor , 2018, IEEE Transactions on Industrial Electronics.

[5]  Guanghui Wen,et al.  Discrete-Time Fast Terminal Sliding Mode Control for Permanent Magnet Linear Motor , 2018, IEEE Transactions on Industrial Electronics.

[6]  Ali Emadi,et al.  Online multi-parameter estimation of interior permanent magnet motor drives with finite control set model predictive control , 2017 .

[7]  Marco Rivera,et al.  Model Predictive Control for Power Converters and Drives: Advances and Trends , 2017, IEEE Transactions on Industrial Electronics.

[8]  Xinghuo Yu,et al.  Discrete-Time Terminal Sliding Mode Control Systems Based on Euler's Discretization , 2014, IEEE Transactions on Automatic Control.

[9]  Luca Dieci,et al.  Numerical solution of discontinuous differential systems: Approaching the discontinuity surface from one side , 2013 .

[10]  D. Griffiths,et al.  Numerical Methods for Ordinary Differential Equations - Initial Value Problems , 2010, Springer undergraduate mathematics series.

[11]  Silverio Bolognani,et al.  Design and Implementation of Model Predictive Control for Electrical Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[12]  Steven C. Chapra,et al.  Numerical Methods for Engineers , 1986 .

[13]  Sukanta Das,et al.  Model predictive field oriented speed control of brushless doubly-fed reluctance motor drive , 2018, 2018 International Conference on Power, Instrumentation, Control and Computing (PICC).

[14]  Ignacio Gonzalez-Prieto,et al.  Model Predictive Control of Six-Phase Induction Motor Drives Using Virtual Voltage Vectors , 2018, IEEE Transactions on Industrial Electronics.