Daisy-Chained Sensor Architecture for Cost-Efficient Linear Motion Systems

Precise position control with an accuracy of 10 μ was impossible by mechanical means such as screw movement used so far. This is because continuous movement couldn't be expected due to sliding friction from a microscopic point of view. Also, it was impossible to avoid backlash even though mechanical pressure by spring may be adopted. The authors have developed practically precise control with an accuracy of 0.2 μ by a new method utilizing the electromagnetic force. In the new equipment, dynamic characteristics are also excellent because the damping time can be designed independently of characteristic frequency by electrical damping and because resistance to deviation from exact position can be selected widely by electrical feedback from a laser interferometer. For example, 66 g-wt is necessary to move it 0.1 μ in the case of only 0.56 W electromagnet, and the time taken for it to become stationary is less than 100 msec at the present stage.

Magnetic Fluid Seal for Linear Motion System with Gravity Compensator

Repetitive sliding mode observer for disturbance estimation of a linear motor system

To meet the challenge of excellent tracking performance even under complicated nonlinear disturbance, a desired compensation neural network adaptive robust control (DCN-NARC) strategy is proposed for an industrial linear motor motion system. The proposed control scheme seriously concentrates on attenuating the effects of various model uncertainties and unknown nonlinearities, such as inertia load variation, force ripple, measurement noise, and external disturbances. Particularly, a three-layer radial basis function neural network (RBFNN) is employed to compensate the unknown nonlinearities. Online parameter adaptation is then utilized to reduce the effect of parametric uncertainties and to update the neural network weights. In addition, to facilitate the controller gain tuning process and attenuate the effect of measurement noise, the desired trajectory states are used in the adaptive model compensation following the desired compensation concept. The residual modeling error and unknown uncertainty are handled through certain robust control term. Theoretically, the proposed DCNNARC can achieve not only prescribed transient performance but also excellent tracking accuracy in general. Comparative experiments are conducted on an industrial linear motor system, and the results consistently demonstrate that the proposed scheme can achieve excellent tracking performance with remarkable disturbance rejection ability even under complicated disturbances. The proposed strategy also could be extended to other industrial control applications.

This work studies the initial-rectification adaptive iterative learning control design for the linear motor systems with unknown input deadzone. For overcoming the nonzero initial error of iterative leaning control, an initial-rectification trajectory is constructed. By making the system state follow the initial-rectification trajectory along the iteration axis, we can get the necessary accurate tracking during the presented time interval. Robust adaptive learning control approach is employed to compensate for complicated uncertainties and disturbances.

For linear motor system, which is an electromechanical multi-parameter, strong coupling, multi-variable, strong non-linear system, the fact that the system control is difficult to carry out is becoming increasingly prominent. This paper proposes an analysis of relative weights of various factors for thrust fluctuations in permanent magnet synchronous linear motor (PMSLM) based on the Fuzzy Analytic Hierarchy Process, (FAHP). First, the paper analyzes the complexity of PMSLM and thrust fluctuations causes the relevance and variability, pointing out how to reduce the linear motor thrust fluctuation analysis of the existing problems. Then, the paper introduces the theory of fuzzy analytic hierarchy theory and its application characteristics and steps. Finally, the application of fuzzy analytic hierarchy process is proposed to analyze linear motor thrust fluctuation factor weights. The results show the end effect of the thrust fluctuations, the ripple disturbance, magnet distribution and cogging effects. This result can be used as a key intervention to reduce the thrust fluctuation fluctuations for PMSLM.

Nanotechnology is based on a combination of many technologies such as high precise positioning and force control, especially magnetic recording, biotechnology and semiconductor industry require the utilization of nanotechnology. Until now there have been various actuator systems proposed, but the structure models have only working distances of either under a millimeter or over ten millimeters. Structure models with working distance ranging several millimeters has been designed a little. Therefore we are proposing a new structure design of actuator that would allow us to build actuator systems with working distances between those parameters. This new actuator consists of a voice coil motor and a new guide with an elastic support mechanism. The elastic support mechanism (ESM) consists of a special spring which is restricted to moving in only one direction. This new ESM does not cause any lost motion, mechanical play or friction with motion. Since characteristically voice coil motor thrusts and displaces the elastic support mechanism linearly, highly precise positioning and force control can be realized using a simple controller. We will evaluate the force control method from the displacement of ESM. This paper will provide basic data for developing future Nano-Actuator systems.

Fluidic soft actuators have been widely used for applications where compliance is desirable – such as in delicate robotic manipulation. As their operation is based on pressurization, fluidic actuators typically rely on bulky, rigid pumps, valves, and pressure regulators for control. This dependence on rigid components hinders the development of compact and fully-soft robots. Soft regulation systems designed for control of these actuators have been recently developed based on soft valves using dielectric elastomer actuators, but precise control has not been achieved. In this work, we leverage these valves to introduce a soft regulation system capable of precise closed-loop position control of a soft hydraulic actuator with multiple controllers. We also achieve open-loop trajectory tracking based on a data-driven model of the fluidic system. Finally, we combine the valve system with wearable strain sensors to create the first teleoperated fluidic circuit where the sensor, actuator, and regulation system are all soft. This work presents control strategies for fluid-driven actuators with soft sensors and regulation systems, showing the potential for future all-soft motion control of soft hydraulic robots.

In robotics, the problem of precision in position control of the different motor systems requires the evaluation of multiple variables in real time in order not to cause instability in the robot due to processing time. In this work, a methodology based on automatic learning of motor systems is proposed in order to be able to generate control actions in advance of their measurement and processing, for which a second-order system and its response to disturbances such as step signals are proposed.

When naval vessels moving on the sea, the carrier air wake disturbance will arise. The carrier air wake makes the landing environment of carrier aircraft fair adverse, which puts forward a high demand for the high precise attitude control of carrier based aircrafts. In this paper, high precise robust attitude control problems for carrier based aircrafts with the carrier air wake disturbance are investigated. Firstly, the small disturbance linearization method is introduced to transform the nonlinear system into a linear one, which is a necessary procedure for aircraft system analysis and controller design. Secondly, the model of the carrier air wake disturbance is established and its effects on the aerodynamic parameters are analyzed in detail. Then the effects are transformed into the augmented disturbance in the system. Thirdly, a nonlinear extended state observer is utilized to estimate the disturbance. Besides, a robust adaptive attitude controller with integral term is proposed to achieve high precision attitude tracking. The stability of the closed-loop system is prove based on Lyapunov theory. Finally, a numerical example is provided to demonstrate the effectiveness of the obtained results.

Practical Highly Precise Position Control of Direct Drive Torque Motors

The direct drive of the permanent magnet linear synchronous motor (PMLSM) requires excellent disturbance rejection capability to achieve precise position control. This article proposes an improved adaptive linear active disturbance rejection control (IALADRC) to improve trajectory tracking and disturbance rejection capability. First, we explain the difference between proportional–integral–derivative (PID) control and LADRC in terms of trajectory tracking and disturbance rejection. Unlike PID, which uses the integral controller to reject disturbances, LADRC uses the extended state observer (ESO) to decouple reference tracking and disturbance rejection. This simplifies the PMLSM control system to a second-order system, which in turn inspired the proposal of the improved LADRC (ILADRC). ILADRC reconstructs the disturbance from the position error using the inverse closed-loop model and injects it into the control output, resulting in better control performance. Due to the complex disturbance existing in the motion process of PMLSM, an improved adaptive ESO (AESO) is proposed to strike a balance between disturbance estimation and noise reduction. The ILADRC and the improved AESO are combined to form the IALADRC. Comparative experiments show that the IALADRC has excellent disturbance rejection capability and significantly improves the position control accuracy of the PMLSM system.

Owing to the dynamic imaging system, the trajectory model plays a very important role in the geometric processing of high resolution satellite imagery. However, establishing a trajectory model is difficult when only discrete and noisy data are available. In this manuscript, we proposed a general robust trajectory model, the penalized spline model, which could fit trajectory data well and smooth noise. The penalized parameter λ controlling the smooth and fitting accuracy could be estimated by generalized cross-validation. Five other trajectory models, including third-order polynomials, Chebyshev polynomials, linear interpolation, Lagrange interpolation and cubic spline, are compared with the penalized spline model. Both the sophisticated ephemeris and on-board ephemeris are used to compare the orbit models. The penalized spline model could smooth part of noise, and accuracy would decrease as the orbit length increases. The band-to-band misregistration of ZiYuan-3 Dengfeng and Faizabad multispectral images is used to evaluate the proposed method. With the Dengfeng dataset, the third-order polynomials and Chebyshev approximation could not model the oscillation, and introduce misregistration of 0.57 pixels misregistration in across-track direction and 0.33 pixels in along-track direction. With the Faizabad dataset, the linear interpolation, Lagrange interpolation and cubic spline model suffer from noise, introducing larger misregistration than the approximation models. Experimental results suggest the penalized spline model could model the oscillation and smooth noise.

Recently, linear motor has gained popularity to be used in a linear motion system. Compared to the traditional system that uses a rotary motor coupled with a motion translation system, the linear motor offers a better dynamic performance by producing a direct linear motion and eliminates mechanical limitations. Recently, due to greater performance, most researchers are focused on the design and development of the permanent magnet linear motor (PMLM). Various types of PMLMs were designed and their performances were compared to each other. Generally, the performances of the PMLMs were compared using thrust, F. However, the thrust was dependent on other parameters such as current, i, input power, p and volume, V mot . Therefore, in order to create a valid comparison, these parameters need to be normalized. Hence, other performance characteristics such as thrust constant, K f , motor constant, km and motor constant square density, G have been used as the performance index. In this paper, the performance comparisons of PMLM were discussed through an actual performance data from a commercialized PMLM. A limit performance index against volume, V mot was established for performance estimation and the benchmarking of the PMLM. Furthermore, comparisons of different types of PMLM were also discussed.

The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naïve Bayes, random forest, and logistic regression. Compared to other Naïve Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.