In this study, in order to improve the accuracy of eccentric magnetic absolute encoders (EMAE), an angle calculation method that combines radial basis function neural networks (RBFNN) and third-order phase-locked loops (TOPLL) is proposed. EMAE consists of a simple multipole magnet, but the eccentricity causes periodic distortion in the signals, which degrades accuracy. In addition, the accuracy of magnetic encoders is degraded by nonideal components, such as amplitude mismatches, phase shifts, low- and high-order harmonics, random noise, and DC offsets. The proposed method uses amplitude normalization and RBFNN to reduce these disturbances. Moreover, the steady-state error that occurs when the ramp frequency is input is eliminated by using TO-PLL. The effectiveness of the proposed method was demonstrated through a simulation.
This paper proposes a high-response torque control method for a surface permanent magnet synchronous motor (SPMSM) with reduction gearing for interactive operation. In robots performing interactive tasks, backforward-drivability of the geared SPMSM is important. The backforward-drivability is defined as the ease of manipulating the load-side of the geared motor by external forces. Torsion torque control has been proposed to enhance this drivability. However, conventional torsion torque control is limited in response speed due to the high system order and reliance on a minor loop current controller. In this study, we propose a high-response sliding mode direct torsion torque control for the geared SPMSM modeled as a two-inertia resonant system. The proposed method achieves rapid response by eliminating the current controller and controlling inverter switches directly. Numerical simulations and experiments confirm that the proposed method provides high-response torsion torque control.
Two-inertia resonant systems are used to represent several motion control systems with resonance modes such as drivetrains and robot joints, offering a more flexible approach for their control compared to single-inertia representations. Controlling the transmitted torsion torque (also known as joint torque) to the load allows to effectively control the driven load of the system regardless of the resonant mode, which can be done by employing torque sensors. However, many real systems contain some degree of joint backlash, which causes a large, impulse-like shock on torque reversals due to the mechanical play of the joint elements, which can lead to undesired behavior and even destruction of the mechanical elements of the system being controlled. While torque sensors are able to sense whether the drive and load-sides are in contact or not, they don't give information on the relative angle between both sides, which is necessary for achieving shockless torque control. This paper introduces a novel nonlinear observer structure based on torque sensing that is able to estimate all the load-side states even when backlash occurs. This new observer is then implemented in a torsion torque control scheme that employs model predictive control during backlash gap traversings to achieve shockless control. Simulation results confirm the effectiveness of this method.
The variation in road conditions and vehicle speeds poses significant challenges in maintaining the desired stability and tracking accuracy of Autonomous Ground Vehicles (AGVs). Uneven, wet, or slippery road surfaces reduce frictional properties, while larger speed variations generate multidirectional motions within the vehicle, ultimately degrading its stability and tracking performance. Additionally, parametric uncertainties, such as cornering stiffness, affect the lateral dynamics and further compromise the tracking performance and stability of the vehicle. To address these challenges, this paper presents a polytopic Linear Parameter Varying (LPV)-based Linear Quadratic Regulator (LQR) controller. The polytopic LPV framework is introduced to handle variations and uncertainties in AGV parameters and assisting the LQR in minimising errors and ensuring yaw stability effectively. The performance of the polytopic LPV-LQR controller is analysed during single-lane and double-lane change manoeuvres considering varying AGV speeds and road conditions with uncertainty. Additionally, the proposed controller's performance is compared with a Robust Controller to provide further insights based on different metrics such as lateral position error, heading angle error, slip angle, and yaw rate. The results show that the proposed LPV-LQR controller is more capable of managing varying and uncertain parameters than the robust controller, facilitating satisfactory AGV tracking performance and stability at higher AGVs speed.
In the literature, fast or real-time magnetic tracking is conventionally achieved by using optimization or machine learning, which requires intensive computation, large computer memory usage and/or tedious data preparation. This could result in high cost and low adaptability of the tracking systems. To address these issues, a combined approach of using weighted coefficients and Monte Carlo algorithm is put forward and analyzed in this paper. The analysis is based on simulating the scenario of tracking a spherical magnetic marker with a diameter of 15 mm within a 200x200x200 mm(3) cuboid region, which is suitable for capsule endoscopy. The statistics of 10,000 case studies show that the approach achieves, averagely, 91.84% position tracking accuracy and 93.97% orientation tracking accuracy with a tracking frequency of 909.1 Hertz.
