This article presents a prescribed-performance-controller-based equivalent-input-disturbance approach (PEID) that enhances the disturbance-rejection performance of a Stewart platform. The PEID approach ensures that the disturbance-estimation error converges to a bounded region in a prescribed time. The bound of disturbance-estimation error can be chosen arbitrarily small, and the convergence time of disturbance-estimation error can be prescribed in advance. Stability conditions are derived by dividing the PEID-based control system into three subsystems, and a barrier Lyapunov function is used to design a prescribed-performance-controller-based compensator. An algorithm for designing system parameters devises state feedback and observer gains. The experiments conducted on a Stewart platform validate the effectiveness and superiority of the PEID approach.

A low-pass filter is essential for stabilizing strictly proper repetitive-control systems, but it inevitably degrades steady-state tracking accuracy due to gain attenuation and phase lag. This article presents a new filter design and optimization method that improves both transient response and steady-state accuracy in continuous-time repetitive-control systems. First, the gain and phase characteristics of conventional filter-based repetitive controllers are rigorously analyzed to reveal the relationship between filter parameters and tracking performance. Based on this analysis, a new filter structure is designed to precisely compensate for gain attenuation and phase delay, specifically at the fundamental frequency, by minimizing the error term without increasing the filter bandwidth. A guideline for selecting the filter parameters for varying periodic trajectories is also provided. In addition, according to the one-to-one mapping between control and learning behaviors and their respective gains, dual performance indices are constructed to account for tracking error and control effort across multiple learning cycles. A multiobjective optimization framework is then developed to directly tune these gains subject to stability constraints, achieving an optimal balance between rapid transient convergence and control energy efficiency. Experimental results validate the effectiveness and superiority of the design.

This article presents a barrier-function-based equivalent-input-disturbance approach (BEID) that enhances the disturbance-rejection performance of control systems. A barrier-function-based compensator remains the EID-estimation error within an arbitrarily small region. A satisfactory disturbance-rejection performance is prescribed without knowing the upper bound of disturbances. Stability conditions are derived by dividing the BEID-based control system into three subsystems. An algorithm for designing system parameters devises state feedback and observer gains. Experiments of a Stewart-platform control system validate the effectiveness and superiorities of the BEID approach.

This article presents a two-degree-of-freedom structure of a fuzzy repetitive-control system that accurately tracks periodic signals and effectively suppresses aperiodic disturbances based on an enhanced equivalent-input-disturbance (EEID) method. To make full use of the learning characteristic, a 2-D model of the fuzzy repetitive-control system is established to independently regulate the control and learning behaviors. The EEID approach decouples the design of the observer and the estimator by adding a flexible controller to the estimator. This makes it possible to simultaneously reduce the estimated and filtered errors, which is a contradictory problem in the conventional EID method. The two errors cannot be eliminated due to the lack of an internal model of the external disturbance. To actively cancel the negative influence of the two errors, a new observation-error compensator is added to the observer input to improve the disturbance-suppression performance. Two low-conservative linear-matrix-inequality stability conditions are derived using the Lyapunov–Krasovskii functionals and zero equations with free-weighting matrices. The nondominated sorting genetic algorithm II and the particle swarm optimization algorithm are used to optimize the parameters of the controllers for the tracking and disturbance-suppression systems based on the stability conditions and performance indexes. The effectiveness and superiority of the design are verified by numerical simulation and comparison results.

The web transportation system processes the long flexible materials called web during transportation, where the system typically comprises an unwind/rewind roller, guide rollers, drive rollers coupled onto servo motors, and a processing part. In the system, web tension and transportation speed controls play an important role in improving product quality. On the other hand, web wrinkles caused by roller misalignment become a severe problem in web processing. The misalignment is difficult to avoid because many guide rollers are used in the system. This paper demonstrates a feasibility study of web wrinkle detection based on image processing and convolution neural network (CNN) during web transportation. The prototype used in this study can generate arbitrary misalignment between rollers by connecting a drive unit to a guide roller. The camera is used to capture the wrinkles on the web. The web wrinkles are detected using a CNN based on the captured image. The validity of the proposed approach has been verified by conducting the experiments using the prototype.

This article investigates the finite-time command-filtered backstepping control problem for dual- motor servo systems. The advantages of the finite-time controller include fast convergence and high robustness, which can improve the dynamic and steady control performance of the system. However, the controller often contains nonlinear and exponential terms increasing the computational complexity. Consequently, for the convenience of practical applications, the command-filtered technique is utilized to decrease the computational burden causing by repeated derivatives in the backstepping process. Besides, LuGre friction model is used to describe the system friction and a fuzzy logic system is designed to handle it. Only one adaptive parameter is estimated, which makes it suitable for industrial applications. Finally, the stability of the closed-loop system is proved and two experiments are carried out to show the advantages of the proposed scheme.

This paper presents an autonomous parameter design method for a cascade structure feedback (FB) controller in industrial precision servo systems with resonance modes, considering a time and frequency domain optimization. In conventional autonomous design methods, parameters of a cascade structure FB controller are optimized by solving a frequency domain optimization problem to expand the control bandwidth and satisfy the desired stability margins. Therefore, a desired positioning response that is generally defined in time domain is not necessarily realized by the designed FB controller. The proposed method combines a time domain optimization problem to a conventional frequency domain optimization problem for improving the time domain positioning response. The effectiveness of the proposed method is demonstrated through an example FB controller design for a galvanometer scanner, in comparison with the conventional autonomous design method based on only the frequency domain parameter optimization.

The automotive industry is at the forefront of the smart, connected and autonomous vehicle (SCAV) revolution to improve social mobility with safety and road utilization. Also, more and more countries and cities have announced restrictions on future internal combustion vehicle sales or use for cleaner transport. Hence, together with urgent demands for highly energy-efficient transport systems, the need to make them safer, greener, and smarter has been increasing rapidly in recent years. As a result, the development of intelligent transportation system (ITS) underpinned by advanced propulsion and innovative control systems is known as a feasible solution to address all of these challenges.

In the position control system using the piezoelectric actuators, a feedback control system is indispensable to improve the positioning/tracking accuracy by compensating for nonlinearities in the actuator. However, a high-resolution displacement sensor poses the problems of cost and installation space. In this paper, a feedback control system without the displacement sensor is designed by combining displacement estimation based on capacitance detection with vibration control by a bridge circuit. The superimposed signal with a high frequency component is injected to the actuator. Based on the voltage and the current generated by the injection signal, the capacitance is computed by performing the discrete Fourier transform. The displacement is estimated using linear relationship between the capacitance and the displacement. In addition, a minor-loop is designed to suppress the resonant vibration using a detected signal through the bridge circuit. The designed control system is evaluated by conducting numerical simulations and experiments using a piezoelectric cantilevered actuator.

A hybrid state observer design is presented herein to estimate sway-angle and angular velocity in trolley systems with pendulum. In general, anti-sway control for trolley systems with pendulum such as overhead cranes are designed based on sway-angle signals detected by angular sensors. Opposed to that, a state observer without those sensors is proposed to estimate the sway-angle of the pendulum. A standard linear continuous feedback observer causes estimation error owing to the system nonlinearity and modeling error. This paper proposes a hybrid state observer incorporating discrete sensor signals. In the hybrid state observer, the estimation performance is improved by correcting the state of the system based on discrete signals. The effectiveness of the hybrid state observer is verified by conducting experiments using a downscaled testbed of a trolley system with pendulum.

This paper proposes a novel 2-Degree-of-Freedom (2-DoF) control system with decoupling compensators that remove interactions between axes of a lightweight two-link robot. The robot is modeled as a pair of coupled two-mass systems, and additional compensators are then applied to decouple the links, i.e. make the controlled object become two uncoupled systems. Afterwards, 2-DoF cascade control systems are constructed for the new decoupled plants, where feedforward (FF) compensators are designed based on coprime factorization method. In addition, the controller parameters are automatically varied to match with the robot's posture. The effectiveness of the proposed design has been verified through experiments with different motions of a prototype.

In high-performance industrial mechatronic servo systems, control parameter tuning is necessary for achieving the desired positioning response to satisfy target control specifications. Auto-tuning, in particular, is a promising technology for efficient fine-tuning and is gaining popularity in industrial servo systems. However, fine-tuning of feedback (FB) control parameters is generally difficult since an aggressive parameter search could lead to the deterioration of control stability. Herein, we present the concept of advanced auto-tuning method considering a stable parameter area and an efficient calculation method of a stable parameter area to address auto-tuning of an FB controller. In this paper, the proposed calculation method of a stable parameter area is extended to an FB controller, which comprises a PID controller and some resonant compensation filters, and the calculation time and accuracy are comparatively evaluated with a full search-based calculation method.

This paper presents a high precision modeling approach for a multi-axis robot considering interference force between axes based on a dynamic model of a multi-axis robot. It is well-known that interference force affects positioning performance especially in high speed positioning motion. In order to overcome the problem, it needs a high precision model which can reproduce actual motion of a multi-axis robot. In this paper, a combined modeling approach which consists of a two-mass model and a dynamic model is proposed, where each axis is basically modeled as a two-mass model to reproduce mechanical vibrations due to elastic mechanism of the reduction gear. Interference force between axes is theoretically modeled based on a dynamic model of a multi-axis robot. The inertial term of interference force is added as disturbance at motor-side, while the velocity term of interference force and gravity force are added as disturbance at load-side. The effectiveness of the proposed modeling approach has been verified based on a comparative study with a conventional model approach based on a two-mass model only.

This paper presents an efficient hybrid optimization method to design a high-performance cascade structure feedback (FB) controller for a mechatronic system with resonant vibration modes. The proposed method combines sequential quadratic programming and a genetic algorithm (GA) in order to obtain the parameters of the cascade structure FB controller. The proposed method makes it possible to obtain a wide-bandwidth FB controller that satisfies the specified stability margins and sensitibity function gains for resonant modes in a short design time. This paper describes the hybrid optimization method and design procedure for the cascade structure FB controller in detail. In addition, it shows how the effectiveness of the proposed method was evaluated through frequency-domain simulations using a laboratory galvano scanner, in comparison with the conventional GA-based optimization method.

This paper aims to develop a predictive sliding mode controller (PSMC) for a class of nonlinear SISO systems. The PSMC is designed as the combination of an adaptive sliding mode controller (ASMC) and an adaptive grey predictor (AGP). The ASMC is designed to ensure robust tracking performance of a nonlinear system which is represented by advanced fuzzy sets. Meanwhile, the AGP is used to estimate impacts of system uncertainties and disturbances on the performance in order to regulate the ASMC adaptive gain to minimize the control efforts while improving the control quality. Real-time experiments are carried out to validate the effectiveness and applicability of the proposed control scheme.

This paper presents a friction model-based frequency response analysis (FRA) method, which gives a precise linear mechanical dynamics model to design effective controllers and analyze accurate control characteristics for frictional servo systems. As well known, frequency-domain identification approaches using a sine sweep are widely used to obtain linear dynamics. However, nonlinear friction in the mechanism varies the apparent frequency-domain characteristic of the linear dynamics due to the nonlinearity. The proposed FRA estimates effective excitation thrust for actual linear dynamics in the sine sweep movement, by means of a friction model as well as a phase delay model. Theoretical analyses show that the proposed FRA can identify the correct linear dynamics, preventing influence of nonlinear friction as well as phase delay properties included in a plant system. The effectiveness of the proposed FRA is verified through theoretical analyses and experiments both in frequency and time domains, in comparison to two conventional FRA methods.

This paper presents a sensorless positioning control approach for piezo-actuated systems, using a simple external circuit. To achieve high-precision positioning with a piezoelectric actuator, a feedback control system using a high-resolution position sensor in incorporated, to cancel nonlinearities and disturbances. However, sensorless control is desirable to reduce cost and space utilization in micro- and nano-positioning systems. In this study, a simple external circuit using a capacitor and resistances is used to indirectly estimate the displacement of the actuator. A position feedback controller is designed based on the estimated displacement signal. Vibration detection is achieved based on a self-sensing technique using a virtual bridge circuit, where a position estimation circuit is used. A minor feedback loop is designed to suppress the resonant vibration modes based on the detected signal. The proposed sensorless control approach was verified by conducting experiments using a commercial piezo-actuated stage system.

This paper proposes a controller design approach to achieve the high reproducibility of earthquake acceleration waveforms in multi-axis shaking table systems. The target shaking table system has an shaking mechanism to excite vibrations in multiple directions, where the cooperation control of each actuator is required to realize precise table motion. In order to conduct an accurate seismic test using the target multi-axis shaking table, a disturbance observer used to improve disturbance suppression capabilities is designed for the feedback control system of each actuator. In addition, an acceleration feedback compensation is added as the minor-loop, to suppress the resonant gain peak in the actuator, because the resonant vibration restricts the expansion of the observer bandwidth. The effectiveness of the designed control system is evaluated by conducting experiments using a test model.

Fast and precise positioning of a multi-axis industrial robot is important solved problems in order to achieve higher productivity and cost saving. In this paper, the state feedback control approach is proposed in order to reduce mechanical vibration of the robot arm with posture changes of the arm. The control system consists of a variable 2DOF controller and a state feedback of the estimated load acceleration. In the variable 2DOF controller, the feedforward compensators are designed based on a coprime factorization description for the plant system, where the model parameters in the compensators are varied corresponding with posture change of the arm. In the state feedback, in order to avoid additional cost introducing the accelerometer to the load, the load acceleration is estimated by the variable state observer, where the plant parameters in the state observer are varied corresponding with the posture change of the arm to fix the estimation performance. The effectiveness of the proposed controller has been verified by the experiments using a prototype.

This paper presents an iterative learning control (ILC) design technique for the fast and precise positioning of galvano scanners with parameter fluctuations. ILC scheme is well-known as a strong control approach for high-precision motion control of mechatronic systems. However, since ILC is heavily sensitive to modeling errors and uncertainty, resonant frequency fluctuations in the galvano scanner greatly affect stability of ILC and deteriorate the learning control performance. In this study, therefore, an ILC approach using zero phase error notch filters is presented to improve the control performance with the ILC stability robustly satisfied. Effectiveness of the proposed ILC has been verified by numerical simulations using a galvano scanner.

The paper introduces the novel discrete feedback control called here impulse-based. It is believed that the proposed control scheme can be an adequate alternative to other existent robust control methods, like sliding-mode or bang-bang control, when solving the motion control problem with damping uncertainties. The impulse-based control paradigm is introduced while been close related to the well-known impulsive hybrid systems. The stable control convergence is shown for the bounded but unknown positive damping. Also the design of appropriate control parameters is provided in the closed analytic form. Numerical simulation example demonstrates the main control principles and helps to understand its advantages and drawbacks. An experimental case study of the motion control with nonlinear frictional uncertainties in vicinity to position settling discloses the practical relevance of the proposed method.

In this paper, we address the identification and design of the sensorless torsion control of elastic robotic joints which has been proposed in our previous work. We discuss some practical aspects of the proposed method which should be accounted for a ready-assembled robotic manipulator at hand. The series of identification experiments required for determining the model-based components is designed and accomplished under constraints of the joint actuation and load motion. The efficiency of sensorless torsion control is shown experimentally for both, trajectory tracking at constant velocity and positioning of the joint load. The experiments are performed on the laboratory setup of a stand-alone elastic joint with inertial and gravity loads. We also comment on the applicability of the proposed method to standard robotic manipulators, while assuming that the joint output is available during the identification.

This paper presents a novel proportional-integral-derivative (PID) controller design considering the robust stability against plant perturbations for the fast and precise positioning control of mechatronic systems. Since parameter fluctuations in plant mechanisms and/or actuators, due to temperature variations, aged deteriorations, etc., generally deteriorate the motion performance as well as the system stability, an improvement in disturbance suppression capability of feedback (FB) control system is a general and important index to provide robust properties against the fluctuations. In this study, therefore, a circle condition-based PID controller design considering the robust stability is presented as well as a genetic algorithm (GA)-based optimization process of a PID-type FB controller with resonance compensation filters, to balance a trade-off between the disturbance suppression and the system stability. Effectiveness of the proposed approach has been verified by numerical simulations using a laboratory prototype of galvano scanner.

Dual loop method is a standard motion control strategy to account for nonlinearities between the motor and load and thus to improve the load positioning performance. However, when dispense with motor-side sensing, which can be cost and space saving, a single load-side feedback can lead to disturbing vibrations or even instabilities. In this paper, a semi-dual loop control of two-mass actuator system is proposed which uses the Luenberger state observer to feedback the motor-side velocity without sensing. A linear state-space model is identified in frequency-domain from the experimental data and serves for observer and control design. The observer-based inner control loop is conceptualized for stabilizing the motion system. The outer I-control loop is determined so as to achieve a precise load positioning. The designed control system is evaluated experimentally on the two-mass actuator with gear elasticities and serves as a prototype for future developments.

This paper presents a suppression approach of disturbances due to the centroid movement of specimen in shaking table systems. In the system, overturning moment of specimen as the disturbance, which is generated by centroid movement, generally deteriorates the motion performance of the table, resulting in the lower reproducibility for the desired earthquake acceleration. In order to compensate for the disturbance, a shaking table including the specimen is modeled using a multi-body dynamics software. Based on the simulator, compensation signals that can cancel the disturbance are generated by an iterative learning control on the simulation. The proposed approach has been verified by experiments using a laboratory setup.

This paper presents a vibration suppression approach for galvano mirrors in laser positioning systems. The systems require the fast and high-precision positioning of the mirror and should maintain the flatness of the mirror after positioning to ensure the control performance. However, resonant vibrations of the mirror, which are excited by the moment force during positioning, lead to residual vibrations after positioning, deteriorating the flatness of the mirror and laser manufacturing accuracy. In this paper, therefore, a vibration suppression approach is proposed by mounting a piezoelectric element on the mirror, where a multi-function of the piezoelectric element as an actuator and a sensor are applied to design the vibration suppression controller. The applicability of the proposed approach to industrial galvano scanners has been verified by performing experiments using a prototype.

This paper presents modeling and identification of a serial two-link manipulator considering joint nonlinearities for industrial robot control. In order to achieve the desired performance using model-based control approaches, it is important to obtain relevant models of both kinematics and dynamics including the nonlinear effects. The main nonlinear components which result in trajectory tracking errors of typical multi-axis industrial robot, are joint nonlinearities in each axis and dynamic coupling effects between different axes. A parametric modeling approach is presented to reproduce behaviors of a serial two-link manipulator with joint nonlinearities. Nonlinear stiffness, angular transmission errors, and friction in these two links are directly identified as joint nonlinearities. As a case study, this approach is applied for the serial two-link arm of a typical multi-axis industrial robot, which has low frequency vibration modes and significantly affect to the trajectory performance. Effectiveness of the modeling is verified by comparative studies with numerical simulations and experiments. Finally, a simple 2-degrees-of-freedom controller on the basis of the identified dynamic model has been applied to improve the performance of trajectory tracking and residual vibration suppression.

Our previous paper presented a method for reducing adhesion forces by oscillation and showed the adhesion state can be checked by analyzing the data obtained by laser displacement meter. However, there are several problems in this method. 1) The end-effector must be located at the specific point where laser displacement meter can measure oscillation. 2) The adhesion state can not be checked if something blocks the light/laser or the target leaves the measuring point. 3) The total system becomes very large. To resolve these problems, this paper firstly presents a method for checking the adhesion state by piezo-transducer. Next, to achieve more precise manipulation, we propose a method to deform the end-effector by adding DC input to the piezo actuator which is also oscillated simultaneously to reduce adhesion forces. Furthermore, we find that the first mode resonance frequency shifts with the increase of the pushing force applied to the object by the end-effector. Using the shift amount, we develop a method for checking the adhesion state.