Abstract
A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM), and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO) for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT) nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline) RLO and second-stage (online) adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP) method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.
Highlights
During the past decade, many fuzzy theories [1,2,3,4,5,6,7] and delay analysis [8,9,10,11,12,13] have attracted great attention from both the academic and industrial communities, and there have been many successful applications
The two-stage control method in this paper is proposed to suppress the modeling error to guarantee the stability of predictive control system in the presence of this modeling error
Neural networks (NNs) or NARMAX/NARX neural networks [17] are composed of simple elements operating in parallel, inspired by biological nervous systems
Summary
Many fuzzy theories [1,2,3,4,5,6,7] and delay analysis [8,9,10,11,12,13] have attracted great attention from both the academic and industrial communities, and there have been many successful applications. Due to the overfitting problem and the local optimal problem of NN, the method [20] is not suitable for real applications because of the need for lengthy convergence time These neural techniques [20, 21] have usually been demonstrated under nonlinear control due to their powerful nonlinear modeling capability [22] and adaptability. To alleviate the requirements for accurate modeling of the plant, the proposed NARMAX plant and control models are trained by initially using novel offline methods with the RLO algorithm to improve this drawback It guarantees the gradient decent method against the local optimal solution and speeds up the convergence of the PSO [24].
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