Model predictive control of nonlinear hybrid systems with discrete inputs employing a hybrid fuzzy model

2008 ◽  
Vol 2 (2) ◽  
pp. 491-509 ◽  
Author(s):  
Gorazd Karer ◽  
Gašper Mušič ◽  
Igor Škrjanc ◽  
Borut Zupančič
Keyword(s):  
Model Predictive Control ◽  
Hybrid Systems ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Nonlinear Hybrid

scholarly journals A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guolian Hou ◽  
Linjuan Gong ◽  
Xiaoyan Dai ◽  
Mengyi Wang ◽  
Congzhi Huang
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Control Process ◽  
Fuzzy Model ◽  
Weight Coefficient ◽  
Combined Cycle ◽  
Generation Process ◽  
Selection Mechanism

The complex characteristics of the gas turbine in a combined cycle unit have brought great difficulties in its control process. Meanwhile, the increasing emphasis on the efficiency, safety, and cleanliness of the power generation process also makes it significantly important to put forward advanced control strategies to satisfy the desired control demands of the gas turbine system. Therefore, aiming at higher control performance of the gas turbine in the gas-steam combined cycle process, a novel fuzzy model predictive control (FMPC) strategy based on the fuzzy selection mechanism and simultaneous heat transfer search (SHTS) algorithm is presented in this paper. The objective function of rolling optimization in this novel FMPC consists of two parts which represent the state optimization and output optimization. In the weight coefficient selection of those two parts, the fuzzy selection mechanism is introduced to overcome the uncertainties existing in the system. Furthermore, on account of the rapidity of the control process, the SHTS algorithm is used to solve the optimization problem rather than the traditional quadratic programming method. The validity of the proposed method is confirmed through simulation experiments of the gas turbine in a combined power plant. The simulation results demonstrate the remarkable superiorities of the adopted algorithm with higher control precision and stronger disturbance rejection ability as well as less optimization time.

scholarly journals Anti‐sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control

2020 ◽  
Vol 56 (10) ◽  
pp. 490-493
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Mobile Robots ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Path Tracking ◽  
Wheeled Mobile Robots

Fuzzy model predictive control for 2-DOF robotic arms

2018 ◽  
Vol 38 (5) ◽  
pp. 568-575 ◽  
Author(s):  
Weilin Yang ◽  
Wentao Zhang ◽  
Dezhi Xu ◽  
Wenxu Yan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Robotic Arm ◽  
Real Time System ◽  
Content Type ◽  
Loop Control ◽  
Robotic Arms ◽  
Intrinsic Nonlinearity ◽  
Robotic Arm Control

Purpose Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However, it is usually nontrivial to tune the parameters in a PID controller. This paper aims to propose a model-based control strategy of robotic arms. Design/methodology/approach A Takagi–Sugeno (T-S) fuzzy model, which is capable of approximating nonlinear systems, is used to describe the dynamics of a robotic arm. Model predictive control (MPC) based on the T-S fuzzy model is considered, which optimizes system performance with respect to a user-defined cost function. Findings The control gains are optimized online according to the real-time system state. Furthermore, the proposed method takes into account the input constraints. Simulations demonstrate the effectiveness of the fuzzy MPC approach. It is shown that asymptotic stability is achieved for the closed-loop control system. Originality/value The T-S fuzzy model is discussed in the modeling of robotic arm dynamics. Fuzzy MPC is used for robotic arm control, which can optimize the transient performance with respect to a user-defined criteria.

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