Precision Motion Control Research Articles

Time-Delay Control Scheme with Adaptive Fixed-Time Convergent Super-Twisting Fractional-Order Nonsingular Terminal Sliding Mode for Piezoelectric Displacement Amplifier

Piezoelectric displacement amplifiers (PDAs) have been widely used in precision positioning fields. However, the inherent hysteresis and creep nonlinear effect of piezoelectric actuators (PEAs) and time-varying lumped disturbances bring extreme challenges to the precise motion control of PDAs. Although various control schemes based on PEAs have been developed and have shown significant results. However, due to the high sensitivity of precision positioning to environmental variations, the development and identification of accurate models and the control timeliness often become obstacles in engineering. To realize precise motion control of PDAs under complex lumped disturbances, a new time-delay control scheme (AFSTA-FONTSM) using an adaptive fixed-time convergent super-twisting algorithm (AFSTA) and a fractional-order nonsingular terminal sliding mode (FONTSM) is proposed. Specifically, the time-delay information obtained by time-delay estimation technology is used to estimate the lumped dynamic characteristic of the system, thus establishing a simple control framework without a system dynamic model. FONSTM is constructed as a sliding mode manifold, and satisfactory error dynamic characteristic is obtained. A new AFSTA is designed as the reaching law in the sliding mode phase. AFSTA has fixed-time convergence when the upper bound of lumped disturbances exists, which ensures the control timeliness. Benefiting from the newly designed adaptive algorithm, the upper bound value of lumped disturbances is no longer needed to determine the control gains, which effectively prevents overestimation of the control gains. Correspondingly, the convergence time of AFSTA is estimated, and the stability of the closed-loop system is analyzed by the Lyapunov theory. Three existing time-delay control schemes, namely MSTA-FONTSM, AMSTA-FONTSM, and ASTA-FONTSM are selected, and four scenes are designed for comparative experiments. The experimental results show that MSTA-FONTSM has the worst control performance among the four control schemes. For the step, and continuous cosine trajectories with periods of T=1s and T=2s, the root-mean-square error of the proposed AFSTA-FONTSM is reduced by 56.86%, 54.03%, and 50.24% compared with MSTA-FONTSM. For disturbance experiments under different loads, the control performance of the proposed AFSTA-FONTSM is still superior to the other three control schemes without load.

Selective synthesis of tightly- and loosely-twisted metallomacrocycle isomers towards precise control of helicity inversion motion

Molecular twist is a characteristic component of molecular machines. Selectively synthesising isomers with different modes of twisting and controlling their motion such as helicity inversion is an essential challenge for achieving more advanced molecular systems. Here we report a strategy to control the inversion kinetics: the kinetically selective synthesis of tightly- and loosely-twisted isomers of a trinuclear PdII-macrocycle and their markedly different molecular behaviours. The loosely-twisted isomers smoothly invert between (P)- and (M)-helicity at a rate of 3.31 s–1, while the helicity inversion of the tightly-twisted isomers is undetectable but rather relaxes to the loosely-twisted isomers. This critical difference between these two isomers is explained by the presence or absence of an absolute configuration inversion of the nitrogen atoms of the macrocyclic amine ligand. Strategies to control the helicity inversion and structural loosening motions by the mode of twisting offer future possibilities for the design of molecular machines.

Modeling and Simulation of the Stepping Motor Operation Curve

To improve the open-loop control performance of stepper motors, research was conducted on their operating curves. The acceleration-velocity characteristic equations of three common operating curves were established and compared with the torque frequency characteristic curves of stepper motors. Based on this, an improved segmented correction fitting S-shaped (SCFS) curve design method based on a high torque utilization sine curve was proposed to solve the problem of rotor blockage and overshoot caused by uneven acceleration during the operation of stepper motors. In the simulation of the stepper motor load motion system, the SCFS curve was compared with common curves. The simulation results showed that the position tracking error was reduced by approximately 82% compared to the trapezoidal curve and approximately 70% compared to the exponential curve. Research has found that this method is suitable for the design of high-precision motor operating curves and has a high application value in the field of precision motion control.

Autonomous nanorobots with powerful thrust under dry solid-contact conditions by photothermal shock

Nanorobotic motion on solid substrates is greatly hindered by strong nanofriction, and powerful nanomotors‒the core components for nanorobotic motion‒are still lacking. Optical actuation addresses power and motion control issues simultaneously, while conventional technologies with small thrust usually apply to fluid environments. Here, we demonstrate micronewton-thrust nanomotors that enable the autonomous nanorobots working like conventional robots with precise motion control on dry surfaces by a photothermal-shock technique. We build a pulsed laser-based actuation and trapping platform, termed photothermal-shock tweezers, for general motion control of metallic nanomaterials and assembled nanorobots with nanoscale precision. The thrust-to-weight ratios up to 107 enable nanomotors output forces to interact with external micro/nano-objects. Leveraging machine vision and deep learning technologies, we assemble the nanomotors into autonomous nanorobots with complex structures, and demonstrate multi-degree-of-freedom motion and sophisticated functions. Our photothermal shock-actuation concept fundamentally addresses the nanotribology challenges and expands the nanorobotic horizon from fluids to dry solid surfaces.

External Bias Field Control of Domain Wall to Skyrmion Conversion in a Typically Designed Magnetic Nanotrack

Understanding the evolution and tunable control of skyrmion dynamics is crucial for the effective development of next generation Racetrack Memory (RM) devices. In this study, we present the micromagnetic simulation demonstrating precise control of skyrmion motion in a magnetic nanotrack composed of two (three) narrow and a wider nanochannels under the influence of spin transfer torque (STT) and out of plane bias field. Our results reveal that out of plane magnetic field assists in efficient conversion of Domain wall (DW) pair to skyrmion and results in the achievement of uniform velocity for each skyrmion. Furthermore, a thorough analysis of individual and total energy terms during the DW pair to skyrmion conversion provides detailed insight into the role of bias field in overcoming the pinning potential at the junction of the narrow and wide nanochannel. The ability to precisely control the skyrmion motion stabilized from a DW pair under the influence of an external bias field is of significant importance for developing skyrmion based memory devices.

Safe Machine-Learning-Supported Model Predictive Force and Motion Control in Robotics

Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible motion control to achieve safe yet high-performance operation. We propose a learning-supported model predictive force and motion control scheme that provides stochastic safety guarantees while adapting to changing situations. Gaussian processes (GPs) are used to learn the uncertain relations that map the robot’s states to the forces and moments. The model predictive controller uses these Gaussian process models to achieve precise motion and force control under stochastic constraint satisfaction. As the uncertainty only occurs in the static model parts — the output equations — a computationally efficient stochastic MPC formulation is used. analysis of recursive feasibility of the optimal control problem and convergence of the closed loop system for the static uncertainty case are given. Chance constraint formulation and back-offs are constructed based on the variance of the Gaussian process to guarantee safe operation. The approach is illustrated on a lightweight robot in simulations and experiments.

A Soft Sensor for Estimating Tire Cornering Properties for Intelligent Tires

Intelligent tire systems are promising solutions for achieving precise vehicle state estimations, localization, and motion control in the context of autonomous driving. Tire cornering properties, namely, lateral force, aligning moment, and pneumatic trail, are crucial factors that should be accurately estimated for vehicle dynamics control purposes. In this work, a soft sensor for estimating tire cornering properties based on intelligent tire and machine learning is developed. The intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread, which provides acceleration measurements from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$</tex-math> </inline-formula> directions. Partial least squares and variable importance in the projection scores (PLS-VIP) are used in the feature extraction of the acceleration signals over the contact patch. A Gaussian process regression (GPR) model is trained to predict the cornering properties with confidence intervals under different input conditions. Based on the variances in the GPR predictions and minimum mean-square error criterion, a data fusion method for pneumatic trail estimation is proposed. It is demonstrated that the developed GPR models for cornering properties and the data fusion method for pneumatic trail estimation have satisfactory accuracy and reliability. The experimental results show that the soft sensor proposed in this work is a strong candidate for further applications in the development of vehicle state estimation and control algorithms.

Ship dynamics model identification based on Semblance least square support vector machine

In recent years, with the rapid development of Maritime Autonomous Surface Ships (MASS) in the industry and academia, using system identification method to build the accurate ship dynamics model has become a hotspot in this area, which is critical to achieve precise and robust ship motion control for safety and adaptive navigation of MASS. Therefore, a non-parametric and robust two-phase system identification method is proposed. Firstly, the improved complete ensemble empirical mode decomposition is introduced to filter datasets collected from model-scale free-running model tests. Secondly, Semblance least square support vector machine (S-LS-SVM) is proposed for the ship dynamics modeling based on the LS-SVM with the state-of-the-art Semblance kernel function. Compared with the traditional method, the Root Mean Square Error (RMSE) of overall prediction on the test dataset improves by 20.82%, 7.68%, and 58.19% for surge, sway, and yaw speed of target ship model. According to maneuverability simulation, it is verified that this method has better generalization performance and higher accuracy for the prediction of the ship’s heading angle and trajectory.

An all-pass based internal model principle controller for galvanometer mirror steering

Galvanometer is a critical component for beam steering in additive manufacturing, profilometry, and medical imaging. In these applications, the quality of the process relies on the precision motion control of the galvanometer to either track or reject narrow-band signals. From the internal model principle, this can be accomplished by incorporating the dynamic model of the reference or disturbance signal in the feedback loop. This paper proposes an innovative internal model principle controller based on numerically robust all-pass filters. The main concept revolves around converting the controller design into a phase response design problem with all-pass filters. For the proposed approach, the targeted frequencies of the internal model can be arbitrarily placed without sacrificing the performance. Further, it produces lower-order controller and is robust against quantization effect. When combining with frequency estimation algorithm and adaptive filter, it can be readily applied to unknown and time-varying disturbance rejection. The advantages of the proposed method are demonstrated by comparing with conventional approaches through analysis, simulation, and experimentation.

A Passive Decomposition Based Robust Synchronous Motion Control of Multi-Motors and Experimental Verification.

Recently, with the trend of redundancy design, the importance of synchronous motion control of multiple motors has been emphasized in various fields such as automotive, construction, and industrial engineering. Therefore, this paper proposed a novel passive decomposition-based robust synchronous control strategy for a multi-motor system, guaranteeing that both the tracking error of each motor and the synchronous error between motors are ultimately and synchronously bounded, even under the presence of parametric uncertainty and unstructured external disturbance. Specifically, a passive decomposition is used to obtain the locked and shape systems from the original system, and then a sliding mode control system along with robust compensations is designed for each decomposed system to achieve the precise synchronous motion control of the n number of motors. Here, the controller for the locked system reduces the tracking errors of motors for a given desired trajectory, while the controller for the shaped system decreases the synchronous errors between motors. Furthermore, the control system is generally and conveniently formulated to adopt the arbitrary n number of motors that must track a given desired trajectory and be synchronized. Compared to other related studies, this work especially focused on increasing the robustness of the entire system using both high-order sliding mode control and two separate compensation terms for model uncertainty and unstructured external disturbance. Finally, to validate the effectiveness of the proposed synchronous control strategy, the extensive experimental studies on two/three/four-geared BLDC motors with a high dead-zone effect were conducted, and we also compared the synchronous control performance of the proposed control strategy with the other representative control approaches, a master-slave control scheme and an independent one to address the superiority of the proposed control system. Regardless of the number of motors, due to the robustness of the control system, it is found that the proposed control ensures the tracking and synchronous errors are less than 1 degree for the sine-wave trajectory while it guarantees that the errors are below 1.5 degree for the trapezoidal trajectory. This control approach can be widely and generally applied to the multiple motor control required in various engineering fields.

Clean and Precise Motion Control Systems

Clean and Precise Motion Control Systems

Self-sensing intelligent microrobots for noninvasive and wireless monitoring systems

Microrobots have garnered tremendous attention due to their small size, flexible movement, and potential for various in situ treatments. However, functional modification of microrobots has become crucial for their interaction with the environment, except for precise motion control. Here, a novel artificial intelligence (AI) microrobot is designed that can respond to changes in the external environment without an onboard energy supply and transmit signals wirelessly in real time. The AI microrobot can cooperate with external electromagnetic imaging equipment and enhance the local radiofrequency (RF) magnetic field to achieve a large penetration sensing depth and a high spatial resolution. The working ranges are determined by the structure of the sensor circuit, and the corresponding enhancement effect can be modulated by the conductivity and permittivity of the surrounding environment, reaching ~560 times at most. Under the control of an external magnetic field, the magnetic tail can actuate the microrobotic agent to move accurately, with great potential to realize in situ monitoring in different places in the human body, almost noninvasively, especially around potential diseases, which is of great significance for early disease discovery and accurate diagnosis. In addition, the compatible fabrication process can produce swarms of functional microrobots. The findings highlight the feasibility of the self-sensing AI microrobots for the development of in situ diagnosis or even treatment according to sensing signals.

Constrained Motion Control of an Independent Metering System with Uncertain Loads

Independent metering systems (IMSs) have been applied and researched in mobile machinery due to their advantages of reduced throttling energy losses and remarkable advances under negative load through decoupling actuator throttling control. Although IMSs have the control flexibility to deal with negative workloads, the control performance of the IMSs is challenged by uncertain loads in mobile operations, limiting the control accuracy. In addition, if the motion reference is improperly specified and exceeds the constraints, the pressure of the actuator may oscillate significantly and potentially result in control instability. In this study, a constrained adaptive robust control strategy is proposed for an IMS. An adaptive robust control strategy is designed for the meter-in and meter-out throttling to achieve precision motion control despite the nonlinearities and uncertainties of the electro-hydraulic IMS. The value of the uncertain workload is estimated in real-time and utilized in the model-based controller to improve control accuracy. In addition, a constrained trajectory planning approach is presented to handle out-of-constraint references and ensure motion tracking performance. This effectively prevents pressure fluctuations caused by the inappropriate reference.

Precision motion control for electro-hydraulic axis systems under unknown time-variant parameters and disturbances

This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters, mismatched and matched disturbances. Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only, the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters. Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way. With skillful integration of the backstepping technique and the adaptive control, a synthesized controller framework is successfully developed for electro-hydraulic axis systems, in which the coupled interaction between parameter estimation and disturbance estimation is avoided. Accordingly, this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances. Also, a nonlinear filter is designed to eliminate the “explosion of complexity” issue existing in the classical back-stepping technique. The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured. Finally, contrastive experiment results validate the superiority of the developed method as well.

Calibration and optimization of FSR based smart walking assistance device

The movement of the legs is a very important activity and routine exercise in human life for the fundamental necessity of mobility. Few smart walking assistance (SWA) devices have recently been developed to support walking for older people and osteoarthritis patients. A smart walking assistance device was developed using force-sensitive resistors (FSRs) to support mobility. Precise motion control of the walker wheel is necessary to direct the SWA device in the intended direction. Due to the inaccuracy in motor manufacturing and its internal resistances, the motors and respective attached wheels run at different speeds even though the electrical input signal is the same. The asynchronization in the motors attached to the wheels leads to deviation in the intended path of the user. It is very important to follow the exact path by SWA as per the requirement and safety of the user. In this paper, the speed asynchronization issue of the SWA device is addressed. The asynchronization problem is solved using mathematical modeling followed by implementing effective speed control techniques to provide speed synchronization in all the driving motors of SWA. Further, the power consumption of SWA is also analyzed. It has been found that the proposed SWA device, after calibration and optimization with pulse width modulation (PWM) mapping performed a successful operation with a power-saving of 27.62% at no-load and 77.33% at load conditions.

An image-based visual servoing control method for UAVs based on fuzzy logic

Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.

Motion tracking of a piezo-driven cell puncture mechanism using enhanced sliding mode control with neural network

This study deals with the design of a cell puncture mechanism (CPM) driven by a piezoelectric actuator to complete the cell puncture process. Then, a novel robust controller is proposed to realize precise CPM motion control. For the convenience of engineering application, the complex nonlinear hysteresis effect and unmolded term in CPM are summed up as an unknown term. Then, a simplified dynamic model is developed. Moreover, a robust controller is constructed by synthesizing the sliding mode control (SMC) with a proportional–integral–differential (PID)-type manifold and radial basis function neural network (RBFNN). The closed-loop stability and finite-time convergence of this controller are proven by the Lyapunov theory. With the self-learning ability of RBFNN, online estimation and real-time compensation of the unknown term can be realized. Moreover, the gain of the robust term in the controller can be reduced. Furthermore, the PID-type manifold improves the transient response speed of the controller and reduces the steady-state error of the system. The advantages of the PID-type manifold and RBFNN complement each another, thereby ensuring the excellent control quality of the proposed controller. Computer simulation results show the feasibility of the proposed simplified model and the robustness of the controller. These results can help improve the intelligence of CPM, particularly when this mechanism buffers from a sophisticated unstructured engineering environment.

Nonlinear robust adaptive precision motion control of motor servo systems with unknown actuator backlash compensation

In this article, the problem of high precision motion control for motor servo systems with modeling uncertainties and unknown actuator backlash is addressed. The combination of synthesized adaptive laws and continuous nonlinear robust term handles parameter uncertainties and system disturbances. The adaptive technique updates the unknown parameters of actuator backlash in real time and the backlash inverse function eliminates the backlash effect. Meanwhile, the designed controller without knowing the range of the disturbance upper bound but automatically estimates through the adaptive law, which improves the engineering practicability. Finally, the theoretical analysis proves the perfect asymptotic stability of the presented controller even with unmodeled disturbances and unknown actuator backlash. Extensive comparative experiments reveal the superiority of the presented method.

The Integrated Strategy of LIDAR Points’ Evaluation Localization and Lateral Tracking Optimization for a New Parking Robot's Tight Space Transportation

With the increase of car's number, the problem of insufficient parking space in cities is becoming more and more prominent. The high-density parking lots using parking robots can greatly improve space utilization. However, there is a great collision risk in the parking lot's tight space navigation. So, precise LIDAR localization and trajectory tracking control are the key technologies of autonomous driving along a predetermined path. Due to the laser points’ sparse character, the LIDAR points’ extracted features have distribution errors. In order to further optimize the LIDAR's localization, this article establishes the feature's error ellipsoid model, and the model's information and error entropies are calculated separately. The error entropy is utilized to optimize the feature-matching weight and improve the localization accuracy. Based on the higher localization result, a state-of-the-art lateral tracking model is proposed to address the challenges faced by the long parking robot. Then, the adaptive fuzzy control is designed to achieve precise control of wheel motion. Finally, an experimental platform is built to compare the effectiveness of different positioning and tracking algorithms. The comparison results show that the integration of laser evaluation localization and lateral tracking optimization algorithm can provides a collision-free improvement of 50% in the parking lot's tight spaces.

Nonlinearity Compensation and High-Frequency Flexibility Suppression Based RIC Method for Precision Motion Control Systems

For precision motion systems widely applied in industrial manufacturing equipments, it is critical to achieve both high trajectory tracking accuracy and superior disturbance rejection ability. In this article, a novel nonlinearity compensation and high-frequency flexibility suppression based real-time iterative compensation (RIC) method is proposed to achieve excellent tracking performance in practice. The unexpected nonlinearity and high-frequency flexible mode of the plant, which limits the achievable control performance, is first compensated and suppressed. Subsequently, a RIC method based on accurate linear prediction model is proposed to further reduce the tracking error by adding a compensation term to the initial reference trajectory. The trajectory compensation idea of RIC is comparative to remarkable iterative learning control (ILC), but the proposed RIC can online generate and adjust the compensation term during real-time motion without abundant offline iteration trials in ILC. This mechanism significantly enhances the robustness to trajectory variations and external disturbances. Comparative experiments carried out on a ball-screw-driven precision motion stage with full-closed loop position feedback validate the effectiveness and superiority of the proposed method for various trajectory tracking tasks. The proposed method outperforms ILC on tracking performance, and possesses the robustness to various disturbances and reference variations, which leads to industrial application significance.