Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

The tendency of miniaturizing semiconductor products towards nano-size transistor in integrated chips has motivated this work on the semiconductor package. Consequently, Four Fuzzy PID controller architectures based on type 2 FLC are developed; the Interval Type-2 Fuzzy Logic PID, IT2FLC PID MOALO-based, IT2FLC PI-PD, and IT2FLC PI-PD MOALO controllers. These architectures are improved to overcome the inherent nonlinearity in X-Y table models and capacitate the uncertainties of the parameters and the disturbances. Both controllers are designed to improve the desired position specification at minimum settling time (Ts), rise time (Tr), overshoot through minimization of oscillation and friction rejection during tracking the desired position trajectory. The ant lion optimization (ALO) algorithm has been efficiently solved optimization problems with minimum parameters and execution time. Hence, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented to size the gains of the proposed controllers to get the desired position trajectory according to the required specification. A comparison with a related existing work shows minimal numerical values of improved transient specification response of Tr, Mp% and Ts for the MOALO- Based developed IT2 FLC PID and IT2 FLC PI-PD architectures. Observation of a higher Maximum Percentage of Enhancement settling time is noticed in both axes within the IT2FLC PI-PD architecture. Accordingly, transient performances of the four architectures have been significantly improved. The improvement is noticeable within the response of IT2FLC PI-PD architecture. The Maximum Percentage of Enhancement in the X-axis and Y-axis has been improved more than eight-fold and six-fold respectively using IT2FLC PI-PD architecture

Design of cross-coupled CMAC for contour-following – a reinforcement-based ILC approach

One of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logicbased RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC

PMLR

We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level

Precise Positioning Control Strategy of Machine Tools: A Review

In this article, a precise positioning control strategy for the nonlinearity of machine tools is thoroughly reviewed. Precise positioning is crucial in machine tools industry where nonlinear phenomenon must be considered. Therefore, this paper aims to review various techniques used to enhance the precision of nonlinearity of machine tools. In the introduction, a significant review of machine tools is discussed based on deadzone phenomenon and high bandwidth. After that, linear control strategies are reviewed involving Proportional-Integral-Derivative (PID) and Cascade P/PI controller. This is followed by nonlinear control strategies, Nonlinear PID (NPID), Adaptive NPID (ANPID), Feedforward NPID (FNPID), Adaptive Robust Controller (ARC), Nominal Characteristics Trajectory Following (NCTF) controller and lastly, the fuzzy and neural network control is then reviewed. Finally, conclusions are presented according to the past researches conducted. Further studies regarding the topic can be improved by the implementation of several additional modules such as deadzone and feedforward compensators and disturbance observer that focus on both disturbance forces such as cutting force and friction force

Precise Positioning Control Strategy Of Machine Tools: A Review

In this article, a precise positioning control strategy for the nonlinearity of machine tools is thoroughly reviewed. Precise positioning is crucial in machine tools industry where nonlinear phenomenon must be considered. Therefore, this paper aims to review various techniques used to enhance the precision of nonlinearity of machine tools. In the introduction, a significant review of machine tools is discussed based on deadzone phenomenon and high bandwidth. After that, linear control strategies are reviewed involving Proportional-Integral-Derivative (PID) and Cascade P/PI controller. This is followed by nonlinear control strategies, Nonlinear PID (NPID), Adaptive NPID (ANPID), Feedforward NPID (FNPID), Adaptive Robust Controller (ARC), Nominal Characteristics Trajectory Following (NCTF) controller and lastly, the fuzzy and neural network control is then reviewed. Finally, conclusions are presented according to the past researches conducted. Further studies regarding the topic can be improved by the implementation of several additional modules such as deadzone and feedforward compensators and disturbance observer that focus on both disturbance forces such as cutting force and friction force

A Stability Analysis for the Acceleration-based Robust Position Control of Robot Manipulators via Disturbance Observer

This paper proposes a new nonlinear stability analysis for the acceleration-based robust position control of robot manipulators by using Disturbance Observer (DOb). It is shown that if the nominal inertia matrix is properly tuned in the design of DOb, then the position error asymptotically goes to zero in regulation control and is uniformly ultimately bounded in trajectory tracking control. As the bandwidth of DOb and the nominal inertia matrix are increased, the bound of error shrinks, i.e., the robust stability and performance of the position control system are improved. However, neither the bandwidth of DOb nor the nominal inertia matrix can be freely increased due to practical design constraints, e.g., the robust position controller becomes more noise sensitive when they are increased. The proposed stability analysis provides insights regarding the dynamic behavior of DOb-based robust motion control systems. It is theoretically and experimentally proved that non-diagonal elements of the nominal inertia matrix are useful to improve the stability and adjust the trade-off between the robustness and noise sensitivity. The validity of the proposal is verified by simulation and experimental results.Comment: 9 pages, 9 figures, Journa

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs

Prediction and compensation of contour error of CNC systems based on LSTM neural-network

This paper proposes a contour error estimation and compensation method for computer numerical control (CNC) systems based on the long short-term memory neural network (LSTM-NN). This is achieved by performing modeling of each axis to predict the tracking error, calculating the actual trajectory, estimating the contour error, and modifying the reference trajectory. First, linear feature selection based on a simplified single-axis model and nonlinear feature selection based on a circular test are performed to achieve tracking error prediction. Then, a spline-approximation-based contour error estimation method is proposed to estimate the contour error between the reference trajectory and the predicted trajectory. Finally, contour error compensation is performed on the reference trajectory before it is run on CNC systems. The proposed method is validated through experiments on a three-axis CNC system