title: RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances.
abstract: An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.
authors.affiliation: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China; School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, PR China. Electronic address: lsh@seu.edu.cn.
authors.forename: Juan
authors.initials: J
authors.lastname: Li
authors.affiliation: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China.
authors.forename: Shihua
authors.initials: S
authors.lastname: Li
authors.affiliation: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China.
authors.forename: Xisong
authors.initials: X
authors.lastname: Chen
authors.affiliation: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China.
authors.forename: Jun
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Multi-input-multi-output; Neural network inverse control; Non-minimum phase; Pseudo-plant; RBFN disturbance observer