Intelligent Servo Research Articles

Mathematical Models and System of Intelligent Servo for High-Efficiency Electrical Discharge Assisted Arc Milling on Difficult-to-Cut Materials

Difficult-to-cut materials, such as superalloys and titanium alloys, have been increasingly used in aerospace and other fields. However, commonly used mechanical milling methods for difficult-to-cut materials have problems, including low processing efficiency and severe tool wear. Electrical discharge assisted arc milling (EDAAM) is a novel high-efficiency machining method proposed by the authors to solve these processing problems. The machining efficiency achieved by EDAAM when processing difficult-to-cut materials is much higher than that achieved by mechanical milling and conventional electrical discharge machining (EDM) milling. However, if EDAAM adopts the traditional EDM servo control method, it will easily form deep and large discharge craters on the surface of the workpiece, which may lead to high machining errors and cause the workpiece to be scrapped. To ensure normal processing, low-energy discharge parameters that may reduce the processing efficiency have to be adopted. To address the above issues and achieve the optimal machining condition of difficult-to-cut materials, mathematical models and system of intelligent servo for EDAAM are proposed. An EDAAM discharge information dataset is obtained, and the intelligent recognition and classification method for discharge states is proposed. The EDAAM intelligent servo control system is proposed based on mathematical models created. The experimental results of superalloy Inconel 718 by EDAAM show that, compared with the nonintelligent servo control system, the intelligent servo control system can increase the material removal rate by up to ten times, reduce the relative electrode wear rate by 37%, and avoid the scrapping of the workpiece.

Intelligent Servo Control Strategy for Robot Joints With Incremental Bayesian Fuzzy Broad Learning System

Intelligent servo control significantly reduces the need to adjust control parameters, and is, therefore, widely used in robot joint control. However, existing intelligent servo control strategies for robot joints have problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability. To solve these problems, this article proposes a servo control strategy for robot joints that is based on the incremental Bayesian fuzzy broad learning system (IBFBLS). First, we construct an intelligent servo control strategy with broad learning system on the basis of fuzzy rules to achieve good self-learning and generalization abilities. Second, the learning parameters of the control strategy are optimized by Bayesian inference to achieve precise joint servo control. Finally, the convergence of the control strategy is enhanced by combining it with Lyapunov theory to constrain the learning parameters of the proposed control strategy. The feasibility and superiority of the proposed control strategy are verified by simulation to compare it with existing intelligent servo control methods. In addition, experiments are conducted using robot joint test bed. Both the simulation and experiments verify that the proposed servo control strategy outperforms other servo control methods with respect to tracking accuracy, stability, and convergence. The root-mean-square error in servo control of robot joints was 0.012 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , which has been reduced by 55.56 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared with the current state of the art.

Intelligent Backstepping Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network

An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.

Intelligent Servo Tuning of High-Speed Machine Tools Using Circular Test

PID control and multi-loop control schemes are widely used in machine-tool computer numerical control servo systems. However, fast and efficient tuning methods for solving mismatches are uncommon. Servo mismatch is an important source of error owing to its effect on positioning accuracy. A circular test using a double ball-bar is used to access such errors. This study proposed a simple, model-free, and inexpensive approach without add-on measurement to solve the servo mismatch system by measurement of simultaneous movements in two axes. Based on the proposed features, our approach is established by iterative tuning using Lagrange interpolation polynomials for the position and velocity loops parameters. The system performance can be improved to iteratively minimize the mismatch of servo systems by tuning the servo parameters of multi-axis feed drive systems. The proposed approach is based on the linear scale signals of iterative circular tests, which avoids using expensive measurement instruments. The experimental results demonstrate the accuracy of feed drive system diagnosis and the performance improvement (mismatch is improved from 0.67 to 0.01ms).

Study on Servo Control Method of Eye Movement Intention Based on Reinforcement Learning

Eye movement servo control has the advantage of non-contact and the eye movement is not affected by other limbs. By applying eye-tracking technology to rehabilitation research of people with movement disorders, the interaction between eye-tracking technology and computers will be gradually put into practice, which is equivalent to giving patients a pair of “invisible hands” that can give instructions freely. In this paper, a servo control method based on eye-movement coding technology is proposed to solve the problems of low accuracy, poor reliability, and insufficient human intention initiative in the field of eye-movement servo control of human-computer visual interaction. The human eye movement instruction is processed by coding and integrated with human eye movement intention to realize intelligent servo control of human eye intention. In order to improve the accuracy of eye movement servo control, this paper proposes an eye movement instruction coding processing method, which uses the trained AdaBoost fusion strong classifier algorithm and the landmark detector to detect the real-time algorithm in the video sequence to complete the eye location and blink determination. In this paper, an improved pupil location method based on multi-angle joint detection is proposed. The pupil location is completed by using the information of eye image points, and the digital coding model of eye command action is constructed, which provides an instruction base for eye movement servo control. In order to improve the reliability and initiative of eye-movement servo control, a reinforcement learning method combining eye-movement coded instruction and intention was proposed. By integrating the improved DDPG continuous control strategy reinforcement learning algorithm with eye movement intention instruction, the environmental status can be sensed in the human-computer interaction process and the optimal strategy of eye movement instruction selection can be learned.

A Learning-Based Stable Servo Control Strategy Using Broad Learning System Applied for Microrobotic Control.

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos' data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters' constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system.

TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically

Tracking aircraft and parachutes plays a vital role in airdrop experiments. It is necessary to study a parachute’s open state and flight trajectory. More scholars are looking into how to efficiently and accurately obtain parachute deformation data and trajectory data. At present, the actual data collection primarily involves experimenters holding high-definition high-speed cameras to track and shoot parachutes to obtain the image sequences of the parachutes during the airdrop process. However, these methods cannot obtain the trajectories of the parachutes and they are susceptible to interference from human factors. In this paper, we designed TuSeSy, an intelligent turntable servo system that can track the aircraft and parachutes in airdrop tests automatically. Specifically, TuSeSy generates the control commands according to the differences between the actual taken images and the inferred images by tracking algorithms (so as to actually track the target). In addition, we propose an effective multi-target tracking switch algorithm based on the image frame difference and optical flow, to achieve real-time switching from the aircraft to the parachute in an airdrop test. To evaluate the performance of TuSeSy, we conducted extensive experiments; the experimental results show that TuSeSy not only solves the problem of wrong target tracking, but it also reduces computational overhead. Moreover, the multi-target tracking switch algorithm has higher computing efficiency and reliability compared to other tracking switch approaches, ensuring the practical applications of the turntable servo system.

On Locomotion Control Using Position Feedback Only in Traversing Rough Terrains with Hexapod Crawling Robot

In this paper, we report on our results on improved locomotion control of small and affordable hexapod crawling robot using only position feedback from the utilized servo motors. Multi-legged robots represent complex mechanical systems with many degrees of freedom from which they can benefit in traversing rough terrains. However, the crucial ability of multi-legged robots is maintaining stable locomotion over irregularities of the terrain which makes the locomotion control complex and requires reliable and timely detection of the leg contact point with the ground. Such detection may require additional sensory equipment which can increase the cost of the multi-legged platform. Therefore, we focus on exploiting capabilities of nowadays intelligent servo motors with position feedback to develop a minimalistic set up in which the robot uses solely the position feedback of the servo motors to sense the ground reaction force. The first achievements enable a small hexapod crawling robot to navigate rough terrains using stable pentapod gait, where only one leg moves at a time, and five legs support the robot. Later on, we improved the locomotion control to enable faster locomotion using three simultaneously moved legs in the so-called tripod motion gait. This paper reports on further advancements with a faster control loop enabled by hardware based acceleration of the communication latency with the utilized Dynamixel AX12 servo motors that improve the locomotion capabilities of the robot. The reported results indicate the robot locomotion with the used adaptive motion gait is speeded up by a factor of 1.4 with the same stability in traversing the rough terrain of the experimental laboratory mock-up.

Large taper mechanism of HS-WEDM

Large taper (≥ ± 5°) high-speed wire electrical discharge machining can be affected by many factors. The positioning error of wire electrode caused by guide wheels and the run-out of guide wheels are particularly important. Moreover, dielectric fluid cannot wrap the wire electrode appropriately and flow into the machining region along with the wire electrode in tilt directions. As a result, the machining accuracy and surface roughness of a large taper-cutting workpiece are worse than those of straight cutting. In order to solve these problems, this paper presents the design and improvement of two types of six-bar linkage large taper-cutting mechanisms that can realize the guide of wire electrode and the tracking spray of dielectric fluid along with the incline of the wire electrode. The first type causes angular error but no wear, whereas the second one causes wear but no angular error. The former is applied to process experiments. The experimental results show that, compared with traditional mechanisms, this new approach decreases the roundness error of the workpiece from 80 to 40 μm and the surface roughness from 4.059 to 3.495 μm under the same processing parameters. In terms of multi cutting (skim cutting), the new mechanism reduces the roundness error of taper cutting (taper ±20°, thickness 40 mm) to 25 μm and the surface roughness to 1.670 μm. An idea of intelligent servo traverse guider based on this mechanism is also proposed in this study.

Image compensation using wavelet transform for tilt servo control in holographic data storage system

A holographic data storage system is very important in the division of a mass storage device. In this regard, tilt servo control is the main problem in the study of holographic data storage system. Tracking servo and tilt servo control are very important research area in holographic data storage system. In this paper, we propose two algorithms. The first algorithm is image compensation using the wavelet transform method and the second algorithm is intelligent servo control using fuzzy rules for the exact tilt control in the holographic data storage system. We need to obtain a good pattern in binary data using a CMOS camera. We have developed two step-by-step operations. Firstly, to obtain exact image data, image data compensation carried out by the wavelet transform method. Finally, we have realized an intelligence control model using fuzzy rules, which was generated by a subtractive clustering algorithm. Therefore, we control radial and tangential tilt servo control using fuzzy rules in a holographic data storage system and perform image data compensation by the wavelet transform method. Our system does not require responses in the tilt servo control system. Therefore, this system has the advantage terms of time. Consequently, the practical pattern of tilt servo control was found by an intelligence algorithm through image processing in the holographic data storage system.

Generation of fuzzy rules and learning algorithm for servo control in holographic data storage system

Today many media of information storage device are formed as disks. Hence, next generation removable data storage media are shaped as disk types too. The holographic data storage system also uses a disk type photopolymer media. And then, holographic data storage system is most advanced optical memory system. Tracking servo and tilt servo control are very important research in holographic data storage system. In this paper, we propose intelligent servo control by fuzzy rules in holographic data storage system. Hence, we have found pattern of tilt servo control in holographic data storage system through fuzzy system and genetic algorithm. Fuzzy rules were generated by genetic algorithm for controlling tilt servo. Therefore, we control tilt servo using fuzzy rules in holographic data storage system. Consequently, Image input---output patterns of tilt servo control was found by intelligence algorithm in holographic data storage system.

Tilt Servo Control by Intelligent Algorithm in Holographic Data Storage System

Tracking servo and tilt servo control are very important research in holographic data storage system. In this paper, we propose intelligent servo control by fuzzy rules in holographic data storage system. Hence, we have found pattern of tilt servo control in holographic data storage system through fuzzy system. Fuzzy rules were generated by subtractive clustering algorithm for controlling tilt servo. Therefore, we control tilt servo using fuzzy rules in holographic data storage system. Consequently, practical pattern of tilt servo control was found by intelligence algorithm in holographic data storage system.

Pattern analysis for tilt servo control in holographic data storage system

Today many media of information storage device are formed as disks. Hence, next generation removable data storage media are shaped as disk types too. The holographic data storage system also uses a disk type photopolymer media. And then, holographic data storage system is most advanced optical memory system. In 1948, Dennis Gabor made the insightful observation that the phase formation in a diffracted wavefront could be recorded by interference with a coherent background. It was not until the early 1960s with the invention of the laser that the technology became practical for storing and retrieving images, and the concepts of holographic data storage were established by van Heerden. In his seminal paper in 1963 on the theory of optical information storage in solids, van Heerden postulated that the recording of interference patterns in a three-dimensional medium could be used as a means of storing and retrieving information (Holographic data storage, Springer, New York, 2000). Tracking servo and tilt servo control are very important research in holographic data storage system. In this paper, we propose intelligent servo control by fuzzy rules in holographic data storage system. Hence, we have found pattern of tilt servo control in holographic data storage system through fuzzy system. Fuzzy rules were generated by subtractive clustering algorithm for controlling tilt servo. Therefore, we control tilt servo using fuzzy rules in holographic data storage system. Consequently, practical pattern of tilt servo control was found by intelligence algorithm in holographic data storage system (Holographic data storage, Springer, New York, 2000).

Development of X-Y Servo Pneumatic-Piezoelectric Hybrid Actuators for Position Control with High Response, Large Stroke and Nanometer Accuracy

This study aims to develop a X-Y dual-axial intelligent servo pneumatic-piezoelectric hybrid actuator for position control with high response, large stroke (250 mm, 200 mm) and nanometer accuracy (20 nm). In each axis, the rodless pneumatic actuator serves to position in coarse stroke and the piezoelectric actuator compensates in fine stroke. Thus, the overall control systems of the single axis become a dual-input single-output (DISO) system. Although the rodless pneumatic actuator has relatively larger friction force, it has the advantage of mechanism for multi-axial development. Thus, the X-Y dual-axial positioning system is developed based on the servo pneumatic-piezoelectric hybrid actuator. In addition, the decoupling self-organizing fuzzy sliding mode control is developed as the intelligent control strategies. Finally, the proposed novel intelligent X-Y dual-axial servo pneumatic-piezoelectric hybrid actuators are implemented and verified experimentally.

Application of visual servo-control X– Y table in color filter cross mark alignment

A low-cost PC-based control X– Y table is constructed with AC servo motors and ball screw mechanisms to achieve long-range micro-positioning. This X– Y mechatronics system is integrated with a machine vision system to construct a visual based motion control system for industrial positioning applications, especially for the optical inspection purpose. Although the visual based servo system has the vision information to guide the servomechanism motion, it cannot obtain accurate positioning objective, especially, for the micro–nano positioning. It still needs a good servo controller to monitor the object step change after receiving the motion command from the machine vision system. Here, a new intelligent model-free controller was proposed to achieve quick transient response and precise steady state accuracy. The integration of this intelligent servo system and a machine vision system is implemented on the LCD color filter cross mark alignment application. The dynamic positioning performance and robustness of this control system are evaluated based on experimental results.

G1301-1-6 高菜自動揉み機の開発(生産加工・工作機械一般)

Cress is one of the chief products in KUMANO. However, the kneading process of cress by hand requires very hard work, and this causes the low-efficiency of this product. So, we are going to develop the auto-kneading machine for cress so as to increase the productivity. At first, we set the designing condition to get the high quality kneaded cress. Productivity of machine itself is enough as same as hand work, because the problem is decrease of manpower. Secondly, to get the high quality kneaded cress, machine must be able to knead with various motions. So, we employed an intelligent servo motor system called 'Cool Muscle'. Machine is constructed with conveyer, 6-roller, and pantograph unit, so as to get the analytic data easily. This machine is the prototype to get the most suitable mechanical kneading motion. After we get the suitable kneading motion, as next step, we will develop the commercial machine with low cost. Unfortunately, summer season is the season-off of Cress, so we can't perform the experiment. So, this paper explains the designing result of prototype of kneading machine.

A Small Biped Entertainment Robot

We propose a small biped entertainment robot prototype, Sony Dream Robot (SDR-3X) that realizes Motion Entertainment by entertaining people with its controlled dynamic motion performance. New key technologies developed for SDR-3X include the Intelligent Servo Actuator (ISA), the trine-actuator composed of a motor a built-in controller and a gear Another technology is Whole Body Coordinated Dynamic Motion Control. Both technologies realize dynamic, stable motion performance in dynamic biped walking, gymnastic motions and dancing. Human-rohot interactive performances are also described in this paper.

Hybrid FMS control architectures based on holonic principles

The escalation in processor technologies and the corresponding reduction in costs have enabled alternative FMS control architectures to be developed without the restrictions of “fixed machine controller boundaries”. These new architectures can be based upon the use of intelligent servo axes, which are desccribed in this article, as flexible numerical control (FNC). In current parlance, the FNC is a “part movement holon” within a manufacturing cell. The control architectures that can be derived from the FNC concept are referred to as hybrid architectures and share the emerging attributes of holonics. This article details the problems that arise in the scheduling and control of FMSs in the light of hybrid control architectures. A number of traditional scheduling approaches have been devised to cope with the scheduling of parts to discrete machines, but the problem here is to ascribe the processing (machining) of part features to axis groups. This article documents how two research programs, undertaken at the CIM Centre at Swinburne University of Technology in Hawthorn, Victoria, Australia, have endeavored to address the problem of hybrid architectures and their associated scheduling.