Abstract
For motion control of uncertain servomechanisms, nonlinear dynamics including smooth and nonsmooth types, external disturbances, signal measurement noises, asymmetric input saturation, and so on seriously hinder the further development of high-performance closed-loop control algorithms. However, already existing control strategies cannot address the above-mentioned issues at the same time. It greatly increases the difficulty of controller design especially when some states are not measurable. Inspired by above motivations, this paper exploits neural networks to deal with nonlinear dynamics including discontinuous types, and combines extended state observers to estimate disturbances and unmeasurable states for uncertain nonlinear servomechanisms. Meanwhile, the desired-command-based model compensation approach is integrated into the controller design. It is worth noting that the neural network weights are updated by the combination of the estimation error and tracking error to acquire better approximation accuracy. According to above technologies, a novel extended-state-observer-based neural network adaptive motion control algorithm will be synthesized. The bounded stability of the whole closed-loop system is proved strictly. In addition, the comparisons of the application results on an electro-hydraulic servo system verify the availability and superiority of the developed control algorithm.
Highlights
Keywords Servomechanism · Input saturation · Motion control · Neural network · Extended state observer. Servo mechanical systems, such as servo motor systems [1,2,3,4], hydraulic servo systems [5,6,7,8], and aerocrafts [9, 10], play an important role in industry and engineering applications, including processing, manufacturing, production, and so on. They are often affected by unknown nonlinear dynamics, including continuous and discontinuous types, external disturbances, signal measurement noises, input saturation, and other factors
By taking advantage of the good approximation performance of neural network (NN) for unknown functions, the authors in [7] and [23] have synthesized an adaptive NN (ANN) control algorithm to treat largely uncertain nonlinear dynamics existing in hydraulic cylinder actuated active suspension systems
In [21], a NN-based adaptive robust control (ARC) control algorithm has been synthesized for a linear motor driven stage to obtain high-accuracy tracking effect and reject external disturbances
Summary
Servo mechanical systems, such as servo motor systems [1,2,3,4], hydraulic servo systems [5,6,7,8], and aerocrafts [9, 10], play an important role in industry and engineering applications, including processing, manufacturing, production, and so on. The proposed control algorithm can simultaneously handle smooth and nonsmooth nonlinear dynamics, matched and mismatched exogenous disturbances, measurement signal noises, and input saturation. Control Goal: To construct a bounded input ν with only partial state measurements, so that the yo χ 1 can follow any desired command χ 1d(t) ∈ Σ1 closely in spite of various modeling uncertainties and input saturation nonlinearity.
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