A DSP-based fully digital PMSM servo drive using on-line self-tuning PI controller

2002 ◽  
Author(s):  
Bin Zhang ◽  
Yaohua Li ◽  
Yansheng Zuo
Keyword(s):  
Pi Controller ◽  
Servo Drive ◽  
Self Tuning ◽  
On Line

scholarly journals Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

2021 ◽  
Vol 11 (23) ◽  
pp. 11090
Author(s):  
Omar Aguilar-Mejía ◽  
Hertwin Minor-Popocatl ◽  
Prudencio Fidel Pacheco-García ◽  
Ruben Tapia-Olvera
Keyword(s):  
Pi Controller ◽  
Servo Drive ◽  
Robust Controller ◽  
External Disturbances ◽  
Pi Controllers ◽  
Optimal Linear ◽  
On Line ◽  
Linear State ◽  
Trajectory Tracking Controller ◽  
Moth Flame Optimization Algorithm

In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method.

scholarly journals Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 487
Author(s):  
Fumitake Fujii ◽  
Akinori Kaneishi ◽  
Takafumi Nii ◽  
Ryu’ichiro Maenishi ◽  
Soma Tanaka
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Pid Control ◽  
Multiple Input Multiple Output ◽  
Industrial Process ◽  
Pi Controller ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Self Tuning ◽  
Two Degree Of Freedom

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller.

Intelligent Synchronous Control for Gantry Position Stage

2014 ◽  
Vol 620 ◽  
pp. 317-320
Author(s):  
Po Huan Chou ◽  
Faa Jeng Lin ◽  
Wen Chuan Chen ◽  
Ying Min Chen
Keyword(s):  
External Disturbance ◽  
Back Propagation ◽  
Field Oriented Control ◽  
Servo Drive ◽  
Control Approach ◽  
Synchronous Control ◽  
Linear Motors ◽  
On Line ◽  
On Line Learning ◽  
Servo Drive System

A cross-coupled proportional-integral-derivative neural network (PIDNN) control is proposed in this study for the synchronous control of a dual linear motors servo system which is installed in a gantry position stage. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using PIDNN is proposed for the field-oriented control PMLSM servo drive system. In the proposed approach, the on-line learning algorithms of the PIDNN are derived using back-propagation (BP) method to guarantee the convergence of the network. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.

A Simple Algorithm for Self-Tuning Control of Low-Order Models

1988 ◽  
Vol 202 (4) ◽  
pp. 301-303
Author(s):  
M A Magdy ◽  
J Katupitiya
Keyword(s):  
Simple Algorithm ◽  
Estimation Algorithm ◽  
Recursive Least Squares ◽  
Discrete Time System ◽  
Time System ◽  
Simplified Approach ◽  
Estimated Parameters ◽  
Self Tuning ◽  
On Line ◽  
Low Order

A simplified approach to the design of self-tuning controllers or low-order systems is presented. The parameters of the continuous time system rather than the discrete time system are identified on-line using a recursive least-squares estimation algorithm. The estimated parameters are then used to calculate the controller parameters so that the system is forced to have a pre-specified closed-loop system performance. In some applications, adopting this approach reduces the number of parameters to be estimated. Further, the controller parameters are obtained using closed-form equations, thus avoiding the on-line solution of polynomial equations. An example is included.

A novel self-tuning PI controller for a DFIG-based wind turbine system

2019 ◽  
Vol 10 (2) ◽  
pp. 176
Author(s):  
Shubhranshu Mohan Parida ◽  
Pravat Kumar Rout ◽  
Sanjeeb Kumar Kar
Keyword(s):  
Wind Turbine ◽  
Pi Controller ◽  
Wind Turbine System ◽  
Self Tuning

A Self-Tuning Multivariable PI Controller

1988 ◽  
Author(s):  
Terho T. Jussila ◽  
Juha T. Tanttu
Keyword(s):  
Pi Controller ◽  
Self Tuning
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