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This paper presents XXY stage alignment with an image feedback system consisting of two charge-coupled devices (CCDs) and a proportional–integral–derivative (PID) image servo system tuned by particle swarm optimization (PSO). The initial stop values for the PSO algorithm often cause problems in calculation. A long short-term memory (LSTM) deep lear...
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Citations
... Many studies have used LSTM networks to address various problems related to time-series data. The in-lab visual image recognition system introduced in [4] was constructed to record the displacement of the XXY platform. Feedback control from a CCD imaging system was used to reduce positioning errors. ...
... The time-domain state of the PID controller for the XXY stage is expressed as follows: (9) Figure 5. Image identification procedure [4]. ...
... Configuration of the XXY stage system and illustration for the Y-axis motion in the experiment[4]. ...
With the rise of Industry 4.0 and artificial intelligence, the demand for industrial automation and precise control has increased. Machine learning can reduce the cost of machine parameter tuning and improve high-precision positioning motion. In this study, a visual image recognition system was used to observe the displacement of an XXY planar platform. Ball-screw clearance, backlash, nonlinear frictional force, and other factors affect the accuracy and reproducibility of positioning. Therefore, the actual positioning error was determined by inputting images captured by a charge-coupled device camera into a reinforcement Q-learning algorithm. Time-differential learning and accumulated rewards were used to perform Q-value iteration to enable optimal platform positioning. A deep Q-network model was constructed and trained through reinforcement learning for effectively estimating the XXY platform’s positioning error and predicting the command compensation according to the error history. The constructed model was validated through simulations. The adopted methodology can be extended to other control applications based on the interaction between feedback measurement and artificial intelligence.
... Neural Network based systems [17] and fuzzy based systems [18] are used in almost all area to improve the system performance. A special kind of scheme in deep learning, namely LSTM network [19] is used to effectively overcome the issues in the time series data [20]. An improved STL-LSTM model [21] is used for bus passenger traffic during the covid-19 pandemic situation. ...
The paper reports a combination of the deep learning technique and bayesian filtering to effectively predict the passenger traffic. The architecture of the model integrates the particle filter with the LSTM network. The time series sequential prediction is best achieved using LSTM network while Markovian behaviour is well extracted using Bayesian (Particle Filter) filters. The temporal and spatial features of the traffic data are analyzed. Three relevant temporal variations viz., morning, noon and post noon patterns are identified after the histogram analysis. These patterns are statistically modelled and the integrated model is used to accurately predict the passenger flow for the next thirty days, facilitating, the bus scheduling for that period. The experimental results proved that the proposed integrated model with coefficient of determination (R2) value of 0.88 is functional in predicting the passenger traffic even when the training data set size is small.
In this study, visual recognition with a charge-coupled device (CCD) image feedback control system was used to record the movement of a coplanar XXY stage. The position of the stage is fedback through the image positioning method, and the positioning compensation of the stage is performed by the image compensation control parameter. The image resolution was constrained and resulted in an average positioning error of the optimized control parameter of 6.712 µm, with the root mean square error being 2.802 µm, and the settling time being approximately 7 s. The merit of a long short-term memory (LSTM) deep learning model is that it can identify long-term dependencies and sequential state data to determine the next control signal. As for improving the positioning performance, LSTM was used to develop a training model for stage motion with an additional dial indicator with an accuracy of 1 μm being used to record the XXY position information. After removing the assisting dial indicator, a new LSTM-based XXY feedback control system was subsequently constructed to reduce the positioning error. In other words, the morphing control signals are dependent not only on time, but also on the iterations of the LSTM learning process. Point-to-point commanded forward, backward and repeated back-and-forth repetitive motions were conducted. Experimental results revealed that the average positioning error achieved after using the LSTM model was 2.085 µm, with the root mean square error being 2.681 µm, and a settling time of 2.02 s. With the assistance of LSTM, the stage exhibited a higher control accuracy and less settling time than did the CCD imaging system according to three positioning indices.
