Generalized Internal Model Control Strategy for Vehicular Single-phase PWM Rectifiers under Parametric Uncertainties

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.

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Study on Adaptive Cruise Internal Model Control Strategy of Truck

Recent Trends in Intelligent Computing, Communication and Devices - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-13-9406-5_114 ◽

2019 ◽

pp. 949-955

Author(s):

Jingjing Fan ◽

Wenbo Chu ◽

Li Wang

Keyword(s):

Control Strategy ◽

Internal Model ◽

Internal Model Control ◽

Model Control

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Internal model control structure using adaptive inverse control strategy

ISA Transactions ◽

10.1016/s0019-0578(07)60209-2 ◽

2005 ◽

Vol 44 (3) ◽

pp. 353-362 ◽

Cited By ~ 18

Author(s):

Muhammad Shafiq

Keyword(s):

Control Strategy ◽

Internal Model ◽

Control Structure ◽

Internal Model Control ◽

Adaptive Inverse Control ◽

Inverse Control ◽

Model Control

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Two fuzzy internal model control methods for nonlinear uncertain systems

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2016-0026 ◽

2017 ◽

Vol 10 (2) ◽

pp. 223-240 ◽

Cited By ~ 1

Author(s):

Amira Aydi ◽

Mohamed Djemel ◽

Mohamed Chtourou

Keyword(s):

Internal Model ◽

Fuzzy Model ◽

Internal Model Control ◽

Local Model ◽

Parametric Uncertainties ◽

Local Models ◽

Content Type ◽

Model Control ◽

The Stability ◽

Takagi Sugeno

Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model. Findings According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties. Originality/value This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

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