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This paper proposes an adaptive sliding mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding mode surface. The MROPIO estimates unknown disturbances and provides feedforward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

This paper proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot's absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot's pose accuracy and showed better performance than previous techniques.

Rectification is a standard process in every system that requires multiviews. Existing algorithms largely work on similar field of view (FoV) cases where the two views are mostly identical. Dissimilarities between different FoVs can generate unexpected errors during the optimization process, resulting in a large amount of rectification errors and unwanted geometric distortion. In this study, we present a full pipeline to rectify uncalibrated images captured by cameras that have dissimilar FoVs under the constraints of geometric distortion. The proposed method contains two main parts: Field of View Neutralization and Rectification with Adaptive Geometric Constraints. The Field of View Neutralization module estimates the transformation matrix to compensate for the imbalance between different views. In addition, this module improves the overall quality of correspondences by removing misleading feature matching pairs. Finally, by applying an adaptive optimization process with the geometric constraints involved, our method addresses the overdistortion issue while maintaining small rectification errors. Extensive experiments are conducted to demonstrate the robust performance of the proposed method. Besides the existing datasets, we provide our dissimilar FoVs dataset with multiple baselines to examine the performance. Our method outperforms the existing algorithms in terms of both rectification errors and geometric distortion rates.

Stereo vision systems in autonomous vehicles can come with camera sensors with different models and lenses to detect objects at long and short ranges. The intrinsic/extrinsic parameters of the camera sensors, such as the focal length and distortion parameters used for calibration, are not provided. In this article, we present a novel rectification method for uncalibrated stereo cameras based on their corresponding feature points. To obtain reliable key/feature points and descriptors from stereo images, we propose a deep neural network, called a multilevel feature pooling network. The model processes images individually to learn both local and semiglobal features, from which the location and descriptor of each pixel in the image are estimated. We also introduce a method to balance the zoom effect caused by the different focal lengths of the camera lenses and rectify the images by estimating homography matrices for stereo images based on their refined corresponding feature points.

This article proposes a method to improve the accuracy of a vernier absolute magnetic encoder. The encoder consists of a master and a nonius multipolar magnetic track. Sinusoidal signals from the master and nonius tracks are used to infer the absolute information. Unfortunately, these signals are contaminated by nonideal factors such as different amplitudes, dc-offsets, phase shifts, and random noise. Moreover, harmonics existing in the encoder signals distort the vernier principle and significantly affect the accuracy of the encoder. To address these problems, the present article proposes an efficient method with three main parts. The first is an observer phase-locked loop (OPLL), which is used to estimate the phase and eliminate the nonideal factors. The second is nonlinear phase compensation, which is used to correct the vernier principle that deviated due to the existing harmonics. Finally, a pole pitch compensation method is introduced to modulate the master phase angle from the OPLL to eliminate the harmonic distortion. The proposed method can eliminate the nonideal factors, harmonic distortion and improve the accuracy of the encoder. All the proposed methods were implemented on an ARM STM32F407ZG. The experimental results confirm the validity of the proposed method for practical applications.

This article presents a portable smartphone-based laser measurement system (SLM) for measuring gaps and flushes on a car body. The system is designed to replace conventional gauges that are used by human operators. The developed device consists of a smartphone and an off-the-shelf laser-line projector held together by a three-dimensional–printed structure. It captures images of a laser line scattered on target surfaces, identifies the extreme points on the laser profiles, and obtains gap and flush measurements with minimal uncertainty. Since the smartphone is used as the operational unit, the measurement data can be stored and allows the capability for data transfer with other storage locations. Experiments were performed on the specimens and real cars to validate the application of the developed system. The measurement uncertainty on a real car was reported as $\pm$ 0.201 mm for gap and $\pm$ 0.154 mm for flush.

This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two permanent magnets: an edge-located multipolar magnet (MPM) and a center-located bipolar magnet, in which the signal-processing accuracy of the MPM is crucial for achieving high accuracy of the entire encoder. However, the MPM signals are disturbed by nonidealities such as dc offsets, amplitude mismatch, low- and high-order harmonics, and random noises. In this paper, the HR approach investigates the characteristics of nonidealities of the phase detector and rejects them by using gradient descent. In addition, the DPLL remains robust by maintaining a zero steady-state error during a phase jump, a constant frequency, and a ramp frequency. The proposed method is simulated with MATLAB software and implemented in ARM STM32F407ZG. The obtained results demonstrate efficient performance. This method can be applied to any use of quadrature sinusoidal signals, such as in power grids and in permanent-magnet synchronous motor phase detection.

Absolute magnetic encoders (AMEs) use two magnets: a ring multipolar magnet (MPM) generating high-resolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of MPM signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the MPM. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, this paper presents an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate dc-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.

A magnetic encoder (ME) is a kind of sinusoidal encoder using magnetic effects that is currently utilized in many industrial control systems because it has many advantageous characteristics: low cost, simple structure, works in harsh environments, high reliability, and so on. The signals generated by an ME are always disturbed by noises; therefore, these signals are not ideal. The challenge is to achieve the highest resolution and to get the maximum operating speed as well as to use the most cost-effective hardware. To solve this problem, this paper humbly proposes an effective approach, which contains two parts: The main part is “compensating the noisy signals of MEs” by applying a new proposed method called the advanced adaptive digital phase-locked loop (AADPLL), while the second is a pulse interpolator which generates high-resolution quadrature pulses. The AADPLL algorithm provides a robust filtering characteristic to eliminate the noises and improve the accuracy of the ME's input signals. It also takes advantage of tracking high-speed input signals without time lag, unlike the traditional filters. Additionally, the computation burden is significantly reduced in this algorithm to allow it to be easily implemented in a low-cost processor. The pulse interpolator is based on an existing idea that extracts high-order sinusoids from the original ME signals. However, a new scheme is presented to achieve higher resolution per period with smaller noises affecting the output pulses. Both parts are mainly implemented in a unique hardware platform using a low-cost digital signal processor, such as the TMS320F2812, combined with a small-size field-programmable gate array. This method has already been applied to control a linear motor without using an expensive optical linear encoder. Practical results are provided to demonstrate the effectiveness of the proposed method.

We present a low-speed-damping controller for a stepper motor using artificial neural networks (ANNs). This controller is designed to remove nonlinear disturbance at low speeds. The proposed controller improves the stepper motor performance at less than the resonance speed of the stepper motor system. Due to its ability to learn, the proposed controller can adapt to different resonant speed ranges without any identification process for system parameters. Conversely, we also introduce the implementation of an ANN-based controller, online backpropagation learning, and a microstep driver on a single field-programmable gate array. An implementation and experimental results are conducted to verify the feasibility and the effectiveness of the proposed controller.

This paper presents the design and implementation of a multiple-axis motion control chip using a field-programmable gate array (FPGA). This multiple-axis motion control chip is designed to control a multiple-axis motion system such as a robotic arm manipulator or a computer numerical control machine. The proposed motion control chip has many functions. These include velocity profile generation, interpolation calculation, inverse kinematics calculation, proportional-integral-derivative control, feedback count, pulse integration, data conversion, clock generation, and external interfacing. These functions are designed using the VHSIC hardware description language and implemented on an FPGA according to the electronic design automation design methodology. This allows for a highly sampled, accurate, flexible, compact, low-power, and low-cost motion control system. The detailed design of the proposed motion control chip is presented. A multiple-axis motion control system using this chip is implemented, and its performance is measured. The multiple-axis motion control system is implemented on a platform consisting of a chip-based multiple-axis motion controller, analog ac servo drivers, a selective compliant assembly robot arm robot, and a host personal computer.

Many techniques for the acceleration and deceleration of industrial robots and computer numerical control (CNC) machine tools have been proposed in order to make industrial robots and CNC machine tools perform given tasks efficiently. Although the techniques selecting polynomial functions can generate various acceleration and deceleration characteristics, the major problem is the computational load. The digital convolution techniques are more efficient than the techniques selecting polynomial functions. However, neither velocity profiles of which the deceleration characteristics is independent from the acceleration characteristics nor those of which the acceleration interval is different from the deceleration interval can be generated by the digital convolution techniques. This paper proposes a generalized approach for generating velocity profiles that cannot be generated by the digital convolution techniques. According to the desired characteristics of acceleration and deceleration, each set of coefficients is calculated and is stored. Given a moving distance, and acceleration and deceleration intervals, a velocity profile having the desired characteristics of acceleration and deceleration can be efficiently generated by using these coefficients. Several velocity profiles generated by the proposed technique are applied to one single-axis control system.