Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Abstract

Model-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for controlling SISO nonlinear time-varying systems. The parameters in SISO-CFMFAC must be carefully tuned before use, as inappropriate parameters may lead to poor control performance. However, up to now, parameter tuning has been a time-consuming and laborious task. In this paper, a new approach called SISO-CFMFAC-LSTM is proposed for parameter self-tuning of SISO-CFMFAC based on long short-term memory (LSTM) neural network. To evaluate the performance of the proposed methodology, qualitative and quantitative comparisons with other existing control algorithms are carried out. Six individual performance indices, namely, the root mean square error (RMSE), the integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of the control signal (IAVU), the maximum overshoot (MO), and the imprecise control ratio (ICR), are introduced for quantitative comparison. The experimental results demonstrate that the proposed SISO-CFMFAC-LSTM achieves the best performance in all indices, indicating that it is an effective control method for SISO nonlinear time-varying systems.

An Improved Partial-Form MFAC Design for Discrete-Time Nonlinear Systems With Neural Networks

“…In practical application scenarios, as the amount of data grows and the controlled systems become more complex, ordinary neural networks such as BP neural networks can no longer tune parameters accurately, which are easy to fail into local optimum [29]. In previous research work [30], it has been found that the LSTM neural network can estimate the parameters to be tuned in the MFAC in real time, and its optimization effect on the MFAC is more obvious than that of the BP neural network, which proves the superiority of LSTM neural network and the necessity of introducing it to tune essential parameters online of partial-form MFAC. Moreover, for partial-form MFAC, PG's change will also become complicated when the controlled system is strongly nonlinear.…”

An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems

“…If only the projection method of MFAC is utilized to calculate PG values alone, the estimated values may significantly deviate from the ideal values, impairing the control performance. As previous research has found, the PG values of MFAC remain the initial constant values during part of the time interval in the three-tank system simulation [ 27 ]. This demonstrates that when dealing with control problems with significant time delays, the default PG estimation projection algorithm in MFAC has a certain probability of triggering the reset mechanism, resulting in the method failing to capture the nonlinear properties of the controlled system.…”

“…Parameters and have been shown to be significantly important in the design of FFMFAC by several studies [ 24 , 25 , 27 ].These studies emphasize the significance of fine-adjusting these parameters in response to changes in the controlled system, with theoretical analysis and simulation findings indicating how improper parameter selection can impact the stability of the controller, resulting in reduced control performance. Furthermore, it should be stressed that PG values should be precisely estimated in order to realize the FFMFAC.…”

“…In the actual industrial production process, with the rapid increase in industrial data and the more complex controlled system, the difficulty of employing feedforward propagation neural networks to perform online parameter adjustment is increasing [ 26 ]. It has been demonstrated in prior study [ 27 ] that LSTMs [ 28 ] can adjust parameters online in the compact-form MFAC, and it has a stronger optimization influence on the compact-form MFAC than the BP neural network. In addition, considering the fact that FFMFAC has the most parameters to be adjusted among MFAC variants, the amount of calculation required to adjust these parameters is considerable.…”

Simple Neural Network Compact Form Model-Free Adaptive Controller for Thin McKibben Muscle System

“…In [18], CFMFAC which has characteristics of straightforward operation is combined with a parameter self-tuning mechanism using backpropagation neural networks (BPNN). However, by combining a CFMFAC with a neural network with long-term short-term memory architecture, authors in [19] has shown better tracking performances compared to [18]. Unfortunately, many adaptive weights were used which increases the computational load.…”