Fuzzy model predictive control for 2-DOF robotic arms

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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Fuzzy Model Predictive Control for a Comfort Air-Conditioning System

2006 IEEE International Conference on Automation Science and Engineering ◽

10.1109/coase.2006.326937 ◽

2006 ◽

Cited By ~ 6

Author(s):

Yonghong Huang ◽

Nianping Li ◽

Yixun Yi ◽

Jihong Zhan

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Air Conditioning ◽

Fuzzy Model ◽

Air Conditioning System

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Active fault tolerant control of a wind turbine via T-S fuzzy model predictive control

22nd Mediterranean Conference on Control and Automation ◽

10.1109/med.2014.6961413 ◽

2014 ◽

Cited By ~ 1

Author(s):

Xiaoran Feng ◽

Ron Patton

Keyword(s):

Model Predictive Control ◽

Wind Turbine ◽

Predictive Control ◽

Active Fault ◽

Fault Tolerant ◽

Fuzzy Model ◽

Fault Tolerant Control ◽

Active Fault Tolerant Control

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Integrating a model predictive control into a spray dryer simulator for a closed-loop control strategy

International Journal of Heat and Mass Transfer ◽

10.1016/j.ijheatmasstransfer.2021.121010 ◽

2021 ◽

Vol 170 ◽

pp. 121010

Author(s):

Sadegh Poozesh ◽

Marc Karam ◽

Nelson Akafuah ◽

Yifen Wang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Closed Loop ◽

Closed Loop Control ◽

Spray Dryer ◽

Loop Control

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

Complexity ◽

10.1155/2018/6468517 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 6

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.

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