Dual RBFNNs-Based Model-Free Adaptive Control With Aspen HYSYS Simulation

Abstract

In this brief, we propose a new data-driven model-free adaptive control (MFAC) method with dual radial basis function neural networks (RBFNNs) for a class of discrete-time nonlinear systems. The main novelty lies in that it provides a systematic design method for controller structure by the direct usage of I/O data, rather than using the first-principle model or offline identified plant model. The controller structure is determined by equivalent-dynamic-linearization representation of the ideal nonlinear controller, and the controller parameters are tuned by the pseudogradient information extracted from the I/O data of the plant, which can deal with the unknown nonlinear system. The stability of the closed-loop control system and the stability of the training process for RBFNNs are guaranteed by rigorous theoretical analysis. Meanwhile, the effectiveness and the applicability of the proposed method are further demonstrated by the numerical example and Aspen HYSYS simulation of distillation column in crude styrene produce process.

Model‐free adaptive formation control for unknown multiinput‐multioutput nonlinear heterogeneous discrete‐time multiagent systems with bounded disturbance

“…This is an energy constraint, which accords with the energy conservation principle. Furthermore, widely practical plants have been verified to satisfy these assumptions, such as, the crude styrene distillation process in Reference 31, the damping control of the power systems in Reference 32, even for image process in video surveillance, 33 etc.…”

“…Third, MFAC can guarantee rigorous stability for a class of SISO and MIMO systems 29,30 . In addition, MFAC has substantial practical applications, like the MAS, 21,22 the chemical process, 31 power system, 32 even for image process in video surveillance, 33 etc. As far as the authors' knowledge goes, there is no research on the formation control for unknown MIMO nonlinear MAS with disturbance based on the MFAC method.…”

Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

“…Since the time‐varying PJM in (8) is unknown, it needs to be estimated. Many time‐varying algorithms can be used to estimate the unknown , such as the modified projection (MP) algorithm [34], lazy‐learning‐based methods [13, 19], neural‐network‐based methods [35, 36], and the successive projection algorithm [25]. Among the above‐mentioned parameter estimation algorithms, the MP method offers strong parameter adaptability, since the estimated parameters can be updated in real‐time, and yields excellent experimental results.…”

Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation

“…Considering different length of the past input and output data being used in equivalent model transformation, the MFAC data models can be classified into the compact form dynamic linearization (CFDL) data model, the partial form dynamic linearization (PFDL) data model, and the full form dynamic linearization (FFDL) data model. Outside of the urban traffic control, MFAC has also been applied in wireless communication systems [24], implantable heart pumps [25], nonlinear distillation columns [26], and so on.…”