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.
Highlights
The concept of the state space method proposed by Kalman in 1960 [1,2] marked the birth of the current control theory and method, which is called model-based control (MBC)
Linear–quadratic regulator (LQR) algorithm can obtain the optimal control law of state linear feedback to form a closed-loop optimal control [4]; robust control [5] can achieve robust performance and stability in the presence of bounded modeling errors; Lyapunov-based controller [6] is introduced to help ensure the stability of the adaptive controller; recursive sliding mode control [7] has the advantage of fast response and is sensitive to the physical parameter changes which have been used in practical control scenarios like automotive control [8] and linear motor control [9]
Chen and Lu [27] proposed a method for achieving model-free adaptive controller (MFAC) parameter self-tuning online by a back-propagation (BP) neural network; it was not compared with other intelligent parameter tuning algorithms
Summary
The concept of the state space method proposed by Kalman in 1960 [1,2] marked the birth of the current control theory and method, which is called model-based control (MBC). Chen and Lu [27] proposed a method for achieving MFAC parameter self-tuning online by a back-propagation (BP) neural network; it was not compared with other intelligent parameter tuning algorithms. Recurrent neural network (RNN) can be used to address this problem, as it has a stronger information processing ability for these controllers and performs better in parameter self-tuning work. This approach has not been applied to SISO-CFMFAC. A parameter self-tuning methodology named SISO-CFMFAC-LSTM which is based on the LSTM neural network with the system error set as input is proposed.
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