Selective memory recursive least squares: recast forgetting into memory in RBF neural network based real-time learning

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Selective memory recursive least squares: recast forgetting into memory in RBF neural network based real-time learning

In radial basis function neural network (RBFNN)-based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this article proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism. Different from the forgetting mechanism, which mainly evaluates the importance of samples according to the time when samples are collected, the memory mechanism evaluates the importance of samples through both temporal and spatial distribution of samples. With SMRLS, the input space of the RBFNN is evenly divided into a finite number of partitions, and a synthesized objective function is developed using synthesized samples from each partition. In addition to the current approximation error, the neural network also updates its weights according to the recorded data from the partition being visited. Compared with classical training methods including the forgetting factor recursive least squares (FFRLS) and stochastic gradient descent (SGD) methods, SMRLS achieves improved learning speed and generalization capability, which are demonstrated by corresponding simulation results.</p

Third-order integral sliding mode control of piezoelectric actuators based on rate-amplitude-dependent Prandtl-Ishlinskii model

Aiming at trajectory tracking control of piezoelectric actuators (PEAs), this article proposes a third-order integral sliding mode control (3-ISMC) based on rate-amplitude-dependent Prandtl-Ishlinskii (PI) inverse model feedforward (3-RAPI) scheme, which can achieve finite time convergence and avoid singular problems, while ensuring the continuity of the control signal. In this control scheme, a rate-amplitude-dependent PI (RAPI) model is proposed to describe the hysteresis characteristics of PEA, and the RAPI hysteresis inverse model is used to realize the feedforward control. The simulation results verify the improvement of the modeling accuracy of the RAPI model compared with the traditional PI model. In order to reduce the influence of modeling error and improve the robustness of the system, a 3-ISMC scheme based on integral non-singular fast terminal sliding mode surface is proposed. Simulation and experimental results demonstrate that the tracking performance of 3-ISMC is improved compared with the existing third-order integral terminal sliding model control (3-ITSMC). Finally, the composite control algorithm is realized by combining the RAPI hysteresis inverse model feedforward with the 3-ISMC algorithm. The experimental results further show that the control algorithm can track the input signal in a wide range of rate and amplitude

Five-axis contour error estimation based on multi-Information dynamic time warping

Contour accuracy is crucial for machining precision in five-axis computer numerical control (CNC) machining. This paper addresses the challenge of improving contour accuracy by proposing a novel contour error estimation and compensation method based on dynamic time warping (DTW). By incorporating time information and geometric characteristics of the machining path, the proposed method introduces a multiple-information fusion algorithm to define distance characteristics between the planned and actual trajectory sequences. This allows the calculation of a distortion path and the establishment of a mapping model between the two sequences. To mitigate the effect of DTW singularity on contour error estimation, a mapping model is established between line segments to determine reference points. The position contour error and the direction contour error of the five-axis tool are accurately estimated using segmented Hermite interpolation, and a spatial iterative learning framework is employed to compensate for them. Experimental results demonstrate the effectiveness of the proposed method in dealing with estimation errors in complex trajectories and its good performance in improving contour accuracy. Note to Practitioners —This paper proposes an effective strategy for the estimation of contour errors in five-axis machining. Currently, most methods for contour error estimation in five-axis machining are based on the nearest point principle. However, this approach fails to accurately estimate contour errors for complex trajectories with significant curvature variations, leading to ineffective contour error compensation in subsequent stages. Therefore, we introduce a contour error estimation algorithm based on DTW. This algorithm takes into account the time information and geometric features of the machining path. Experimental results validate the feasibility and advantages of this approach.</p

Five-axis contour error estimation based on multi-Information dynamic time warping

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Memory-enhanced neural network control of piezoelectric actuators with a rate-amplitude-dependent hysteresis model

Due to the presence of strong hysteresis nonlinearity, achieving robust and precise control of piezoelectric actuators (PEAs) is highly challenging. In this article, a novel rate-amplitude-dependent asymmetric Prandtl-Ishlinskii (RADAPI) model is proposed for modeling the hysteresis nonlinearity in PEAs and ultimately used for feedforward control based on its inverse model. Then, an uncertainty and disturbance estimator (UDE)-based controller using radial basis function (RBF) neural network is developed to address the issue of integral windup. To overcome the issue of passive knowledge forgetting, the selective memory recursive least squares weight update law is adopted. Moreover, the stability of the closed-loop system is demonstrated. A combined control scheme, incorporating RADAPI hysteresis inverse model feedforward compensation along with RBF-UDE based closed-loop feedback control, is devised to enhance the trajectory tracking accuracy of PEAs. Both theoretical analysis and experimental results are provided to validate the proposed control scheme.</p

Changes in POD activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.

<p>Changes in POD activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.</p

Changes in PAL activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.

<p>Changes in PAL activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.</p

Changes in PPO activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.

<p>Changes in PPO activity in tomato leaves 0 to 96 h after inoculation with <i>R. solanacearum</i>.</p

Nutrient content of tomato plants.

<p>The data were expressed as the mean ± standard deviation (n = 4). Different italicized letters within a column indicate significant differences as determined by T test (<i>P</i><0.05).</p