Search

This paper investigates the problem of a decoupled disturbance compensator (DDC) based stochastic sliding mode control (SMC) design for discrete linear time-invariant systems. In the developed method, the actual disturbance is estimated using the DDC method to design the control input and stabilize the system. The proposed method allows the disturbance compensation to be modified separately from the control input, since the two inputs of system are completely decoupled. To demonstrate the effectiveness of the DDC based discrete-time stochastic SMC, its performance is compared with that of the existing methods using the simulation results. Finally, experimental results are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a deep reinforcement learning (deep RL) method for simultaneously designing several notch filters in complex industrial servo systems. Notch filters are highly effective for suppressing resonances in motion control systems and are widely utilized in industry. However, severe limitations exist in complex servo systems because there are many vibration modes that are difficult to identify. In such cases, several notch filters must be used, but the task of tuning these filters involves lengthy empirical procedures by well-experienced engineers. To automate this tuning process, this paper proposes a novel design method that can design several notch filters simultaneously for the first time. In this method, a deep deterministic policy gradient (DDPG) algorithm with a vector stability margin as the reward function is utilized to find filter parameters in the frequency domain. The proposed method simultaneously finds a set of many parameters for several notch filters that are optimal with respect to stability. Using a real industrial servo system that has multiple resonances, it is demonstrated that the proposed method effectively finds the optimal parameters for several notch filters and successfully suppresses multiple resonances to provide desired performances.

This article presents a novel method of dealing with disturbances and control saturation in the framework of discrete-time sliding mode control (DSMC). A DSMC with decoupled disturbance compensator (DDC) was developed for SMC to recover the loss of stability in the discrete-time domain. However, windup phenomena occur in both the switching function and the disturbance estimate when control saturation occurs, and the method cannot guarantee stability. The article develops a new method for maintaining stability under both disturbances and control saturation by incorporating a novel auxiliary state into the DSMC with DDC method. The auxiliary state prevents the windup phenomena, and the developed new method preserves the asymptotic convergence property of the disturbance estimation error dynamics and the stability of the sliding mode dynamics. Also, the error states are stable under bounded disturbances and control saturation. Simulations and experiments are performed on a commonly used industrial servo system to demonstrate the effectiveness of the proposed method.

This paper presents a three-axis biomimetic gyroscope, mimicking the haltere of Diptera. Diptera use a club-shaped mechanosensory organ called the haltere to get the three-axis angular velocity information, namely roll, pitch and yaw axes, for flight control. One pair of halteres is physically connected to the wings of Diptera that vibrate in antiphase to the flapping wings in ambient air. They sense the Coriolis force and relay angular velocity information to the Diptera. As an alternative to the conventional micro-electro-mechanical system gyroscopes which are widely used in robotics, many research groups have attempted to mimic the haltere. However, no previous study succeeded in measuring all three-axis components of angular velocity, due to various shortcomings. In this paper, we developed the first three-axis haltere-mimicking gyroscope. Two perpendicularly positioned haltere-mimicking structures that can vibrate at a 180° amplitude were mechanically integrated into a robot actuator. Two accelerometers, placed at the tip of each structure, were employed to measure the Coriolis force. The performance of the novel biomimetic gyroscope was measured in all rotational directions, using a motion capture system as the ground truth. One-axis input experiments were performed 240 times at different input magnitudes and directions, and the measured orientation error was less than ±2.0% in all experiments. In 80 three-axis input experiments, the orientation error was less than ±3.5%.

The stray charges induced on ion trap surfaces can push trapped ions away from a pseudopotential null point. This can cause the micromotion of the trapped ions that in turn results in ion heating. One of the causes of stray charges is laser scattering on dielectric surfaces. The amount of laser-induced stray charges can be related to the area of the dielectric surface exposed to the ions. In this paper, stray charges are intentionally created at various dielectric surfaces on a microfabricated ion trap chip using a laser, and the resulting ion motion is measured. The ion movements are estimated by comparing ion positions before and after laser irradiation. When the laser irradiates a dielectric surface with an area of approximately 1,750 μm2 exposed to the ion, the ion moves approximately −4.8 μm in the axial direction. When the laser irradiates another dielectric surface with an area of approximately 140 μm2 exposed to the ion, the ion moves approximately −0.6 μm. The ratio between the ion movements corresponds to the ratio between the areas of each dielectric surface directly exposed to the ion normalized to the distance between the ion and the laser-induced stray charges. The results indicate the importance of reducing the area of dielectric surfaces that are directly in the line of laser paths.

This paper presents a harsh-environment visual odometry method that is robust to the robot orientation as well as the camera movement in an outdoor environment. The accuracy of visual odometry in robots can be enhanced by using additional sensor measurements such as an encoder, gyroscope, and/or magnetometer. This strategy can even reduce the computational time. However, in an outdoor environment, the moving robot can experience vibration, which causes unsynchronized data fusion between the camera and additional sensors. This unsynchronized data fusion causes errors in the robot orientation, which can lead to unwanted large drift errors in localization. To overcome this problem, firstly two distinctively different characteristics of the gyroscope and the magnetometer are combined to estimate the robot orientation. The initial robot orientation is estimated by integration of the gyroscope input, and this initial robot orientation is corrected using the magnetometer data in bundle adjustment. Secondly, the poses of the robot and the camera are estimated separately, and these separately estimated poses of the robot and the camera are used in feature matching and bundle adjustment to reduce drift errors in localization in the outdoor environment. The performance of the proposed method is demonstrated using dataset-based experiments.

This paper presents a performance optimization methodology for the discrete-time sliding mode control (SMC) with the decoupled disturbance compensator (DDC) and the auxiliary state (AS) for an industrial position control system under control input saturation and disturbance. The discrete-time SMC with DDC and AS (SDA) method prevents windup phenomena in the switching function and the disturbance estimation error. Therefore, it provides robust performance under control input saturation, disturbances, as well as parametric uncertainties. However, it is difficult to relate several design parameters to the desired performance. In this paper, a systematical design framework for the discrete-time SDA method is developed. Based on the phase portrait of the error state, the error can be made to converge to zero with a fast speed and less oscillation under control input saturation by an offline tuning process. An optimization process is performed in terms of the peak value of the control input. Both numerical simulations and experimental results show the effectiveness of the developed tuning methodology for the discrete-time SDA method.

Air flow measurements provide significant information required for understanding the characteristics of insect movement. This study proposes a four-channel low-noise readout integrated circuit (IC) in order to measure air flow (air velocity), which can be beneficial to insect biomimetic robot systems that have been studied recently. Instrumentation amplifiers (IAs) with low-noise characteristics in readout ICs are essential because the air flow of an insect’s movement, which is electrically converted using a microelectromechanical systems (MEMS) sensor, generally produces a small signal. The fundamental architecture employed in the readout IC is a three op amp IA, and it accomplishes low-noise characteristics by chopping. Moreover, the readout IC has a four-channel input structure and implements an automatic offset calibration loop (AOCL) for input offset correction. The AOCL based on the binary search logic adjusts the output offset by controlling the input voltage bias generated by the R-2R digital-to-analog converter (DAC). The electrically converted air flow signal is amplified using a three op amp IA, which is passed through a low-pass filter (LPF) for ripple rejection that is generated by chopping, and converted to a digital code by a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC). Furthermore, the readout IC contains a low-dropout (LDO) regulator that enables the supply voltage to drive digital circuits, and a serial peripheral interface (SPI) for digital communication. The readout IC is designed with a 0.18 μm CMOS process and the current consumption is 1.886 mA at 3.3 V supply voltage. The IC has an active area of 6.78 mm2 and input-referred noise (IRN) characteristics of 95.4 nV/√Hz at 1 Hz.

For high-fidelity quantum operations in ion traps, it is important to maintain the secular frequency of the trapped ions at a constant value. The radial secular frequency is proportional to the amplitude of the radio frequency (RF) signal applied to ion traps. Owing to the changes in the ambient temperature of a helical resonator and the minute vibration of the optical table, the amplitude can vary. Recently, a method for reducing the fluctuation in the RF signal amplitude, using a commercial universal proportional-plus-integral (PI) controller, has been introduced, which, in turn, reduces the secular frequency drift of the trapped ions. The method improves the capability to maintain the secular frequency at a constant value. However, the structure of the controller is fixed, thus, the control method cannot be changed to suit different experimental conditions, and the different feedback configuration cannot be implemented to increase the resolution. In this paper, we develop a field-programmable gate array (FPGA)-based feedback controller that allows the implementation of various automatic control methods and feedback configurations. In our experiments, the fluctuation in the amplitude of the RF signal was 1.806% using a commercial universal PI controller. The fluctuation was reduced to 0.099% using the developed FPGA-based PI controller, and to 0.102% using the developed FPGA-based lag compensator. By employing the developed FPGA control method, many other automating control methods can be applied to achieve a stable and high-performance control of the secular frequency.

We present a compact optical design for a scalable trapped ion quantum processor employing a single high numerical aperture lens for the excitation of ions and collection of photons, both of which are essential for remote entanglement generation. We verified the design by performing a quantum interference experiment between two photons generated by two sets of the proposed design and observed a 82(3) % suppression of coincidence within 8.13 ns time window when the two photons became indistinguishable. This design can be extended for the simultaneous generation of multiple pairs of entangled qubits with existing fiber-array devices.

This paper designed a new robust velocity control method for industrial servo systems. Exogenous disturbances and control input saturation are considered by using the discrete-time Sliding mode control with Decoupled disturbance compensator and Auxiliary state (SDA) method. The discrete-time SDA method preserves the original stability of the sliding mode dynamics as well as the stability of the disturbance estimation error dynamics under both control input saturation and disturbance. Due to these attributes, the discrete-time SDA method has been utilized in various control applications. In this paper, the discrete-time SDA method is newly designed for velocity control applications. Based on the error dynamics of the discrete-time SDA method, the gain of the auxiliary state is set to "1". This design provides the stability of velocity error, and there is nearly zero overshoot under control input saturation. Experiments are performed to demonstrate the performance of the designed control method.

An outdoor environment is challenging for the localization of a mobile robot. For robust visual odometry, accurate feature matching and triangulation are essential. The features extracted from the windows of buildings and car surfaces lead to wrong triangulation results due to reflective features. The landmarks at short-distances affect the feature matching performance and the landmarks at long-distances cause triangulation errors. Inaccurate feature matching and triangulation error lead to the localization error of the robot pose. In this paper, an outdoor monocular visual odometry using the pre-trained depth estimation network and semantic segmentation network is proposed. By using the pre-trained semantic segmentation network, a semantic label is predicted for every pixel. Also, by using the pre-trained depth map estimation network, the depth of every pixel is predicted. Using semantic constraints for feature matching and depth constraint for triangulation, the accuracy of these procedures is enhanced. Additionally, pose graph optimization is performed on every estimated robot pose and landmark position. The performance of the proposed method is evaluated using dataset-based experiments. The experiments showed that the proposed algorithm is more accurate than the visual odometry algorithm that uses Oriented FAST and rotated BRIEF (ORB) features.

This paper presents an effective performance shaping methodology of discrete-time sliding mode control (DSMC) with disturbance observer (DOB) based on the analysis of error dynamics. DSMC with DOB exhibits excellent tracking performance and robustness subject to external disturbances and unknowns including uncertain parameters and unmodelled dynamics. In addition, DSMC with DOB can be easily implemented in various control applications attributed to its simple structure and discrete-domain representation. However, the performance cannot be easily predicted by the controller parameters of DSMC with DOB, and much tuning effort is required to achieve the desired performance. This paper derives a relationship between the performance and the controller parameters of DSMC with DOB using a disturbance-to-error transfer function. In addition, a tuning methodology for shaping the performance of DSMC with DOB is also suggested. Finally, the developed analysis is demonstrated experimentally in a commercial linear motor system.