U-Model-Based Two-Degree-of-Freedom Internal Model Control of Nonlinear Dynamic Systems

Abstract

This paper proposes a U-Model-Based Two-Degree-of-Freedom Internal Model Control (UTDF-IMC) structure with strength in nonlinear dynamic inversion, and separation of tracking design and robustness design. This approach can effectively accommodate modeling error and disturbance while removing those widely used linearization techniques for nonlinear plants/processes. To assure the expansion and applications, it analyses the key properties associated with the UTDF-IMC. For initial benchmark testing, computational experiments are conducted using MATLAB/Simulink for two mismatched linear and nonlinear plants. Further tests consider an industrial system, in which the IMC of a Permanent Magnet Synchronous Motor (PMSM) is simulated to demonstrate the effectiveness of the design procedure for potential industrial applications.

Special Issue “Complex Dynamic System Modelling, Identification and Control”

“…Li et al [1] propose a U-model-based two-degree-of-freedom internal model control (UTDF-IMC) structure with strength in nonlinear dynamic inversion and the separation of tracking design and robustness design. This approach can effectively accommodate modeling error and disturbance while removing those widely used linearization techniques for nonlinear plants/processes.…”

A new configuration of composite nonlinear feedback control for nonlinear systems with input saturation

“…U-control, taking the U-model-based nonlinear dynamic inversion as its base stone, designs the whole control system, favourably achieving linear system performances, separately from plant (model), so that it significantly simplifies the nonlinear control system design procedures. U-control can apply linear control system design methodologies to all U-model described dynamic plant and provide flexible control system configurations (Hussain et al, 2019; Li et al, 2021). The kernel issue in U-control is the robust nonlinear dynamic inversion against model uncertainty and external disturbance.…”

Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems

“…With the development of engineering automation requirements, the research of control strategies based on multi-input multi-output nonlinear systems has attracted growing attention. In recent decades, many control schemes have been proposed for stability analysis and the control for nonlinear systems, such as the adaptive technique [ 1 , 2 , 3 ], backstepping technique [ 4 , 5 , 6 ], U model control [ 7 , 8 , 9 ], sliding mode control [ 10 , 11 , 12 ], super twisting algorithm [ 13 , 14 ], neural network technique [ 6 , 15 , 16 ], etc. In particular, neural network technology has attracted many researchers’ attention because of the following aspects: (1) a neural network has the strong ability to learn any function and can approximate any nonlinear system, and (2) because of the self-learning ability of neural networks, the controller does not need much system model and parameter information, so neural network control can be widely used to solve the control problems caused by uncertain models [ 17 ].…”