State and Disturbance Estimation Based on Internal Model Control Structure and Its Application to Sensorless Grasping Control

2007 ◽  
Author(s):  
Ryoichi Suzuki ◽  
Nobuaki Fujiki ◽  
Akira Sugawara ◽  
Nobuaki Kobayashi ◽  
Kouhei Ito
Keyword(s):  
Internal Model ◽  
Control Structure ◽  
Internal Model Control ◽  
Disturbance Estimation ◽  
Model Control

Robust and Effective PID Controller Identification for Delay Dominant Systems

2014 ◽  
Vol 625 ◽  
pp. 478-481
Author(s):  
Lemma Dendena Tufa ◽  
Marappagounder Ramasamy
Keyword(s):  
Time Delay ◽  
Time Constant ◽  
Pid Controller ◽  
Internal Model ◽  
Control Structure ◽  
Internal Model Control ◽  
Controller Tuning ◽  
Model Control ◽  
Pid Controller Tuning ◽  
Improved Performance

A novel PID controller identification method based on internal model control structure is proposed. The proposed method avoids the necessity of approximating the time delay for designing the PID controller. It results in a robust and effective PID controller tuning. The method is effective for both time constant and time delay dominant systems, with much improved performance for the latter case.

scholarly journals New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guohai Liu ◽  
Jun Yuan ◽  
Wenxiang Zhao ◽  
Yaojie Mi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Internal Model ◽  
Generalized Inverse ◽  
Control Structure ◽  
Control Method ◽  
Internal Model Control ◽  
Drive System ◽  
Motor Drive ◽  
Model Control

Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM) control method, which is based on neural network generalized inverse (NNGI). This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

scholarly journals Fractional-order internal model control algorithm based on the force/position control structure of redundant actuation parallel robot

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989214 ◽  
Author(s):  
Shuhuan Wen ◽  
Xueheng Hu ◽  
Baowei Zhang ◽  
Miao Sheng ◽  
HK Lam ◽  
...  
Keyword(s):  
Internal Model ◽  
Control Structure ◽  
Control Method ◽  
Hybrid Control ◽  
Parallel Robot ◽  
Internal Model Control ◽  
Redundant Actuation ◽  
Model Control ◽  
Fractional Filter ◽  
Derivative Control

This article proposes a new control structure for the complex redundant actuation parallel robot based on force/position hybrid control structure. The traditional proportional–integral–derivative control method, integer-order internal model control method and fractional-filter internal model control–proportional–derivative control method are used in the position structure of force/position hybrid control. The fractional-filter internal model control–proportional–derivative control method is used in the position loop of the permanent magnet synchronous motor to reduce the position error. A fractional-order theory with the internal model control method is used in redundant actuation force control structure, which can improve the control precision of the driving force of the parallel robot. The Admas/Matlab simulation results show that the proposed method outperforms other methods and can obtain good robustness and tracking performance.

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