Recently, a highly efficient midrange wireless transfer technology using electromagnetic resonance coupling has been proposed and has received much attention due to its practical range and efficiency. The resonance frequency of the resonators changes as the gap between the resonators changes. However, when this technology is applied in the megahertz range, the usable frequency is bounded by the industrial, scientific, and medical (ISM) band. Therefore, to achieve maximum power transmission efficiency, the resonance frequency has to be fixed within the ISM band. In this paper, an automated impedance matching (IM) system is proposed to increase the efficiency by matching the resonance frequency of the resonator pair to that of the power source. The simulations and experiments verify that the IM circuits can change the resonance frequency to 13.56 MHz (in the ISM band) for different air gaps, improving the power transfer efficiency. Experiments also verified that automated IM can be easily achieved just by observing and minimizing the reflected wave at the transmitting side of the system.

This paper discusses design and control of a prismatic series elastic actuator with high mechanical power output in a small and lightweight form factor. A design is introduced that pushes the performance boundary of electric series elastic actuators by using high motor voltage coupled with an efficient drivetrain to enable large continuous actuator force while retaining speed. Compact size is achieved through the use of a novel piston-style ball screw support mechanism and a concentric compliant element. Generic models for two common series elastic actuator configurations are introduced and compared. These models are then used to develop controllers for force and position tracking based on combinations of PID, model-based, and disturbance observer control structures. Finally, our actuator's performance is demonstrated through a series of experiments designed to operate the actuator at the limits of its mechanical and control capability.

A series elastic actuator (SEA) is a promising actuation method in force control applications that intelligently interacts with environments. The SEA is characterized by a spring placed between a load and an actuator, which is an electric motor in most cases. Since the spring plays the role of a transducer between position (i.e., the spring deflection) and force, it is able to control the output force (torque) precisely by utilizing typical position control methods. However, the force control performance of the SEA is considered to have limitations due to its elasticity, and thus, to be inferior to rigid actuators in terms of bandwidth. This paper proposes that the force control performance of the SEA can be improved by exploiting the dynamic model of the SEA. To this end, the SEA is modeled and analyzed utilizing the two-mass dynamic model, which is a well-known and widely accepted model of the flexible system. The disturbance observer and feedforward controller are introduced as the model-based control algorithms for the SEA to achieve the high-precision force control. In addition to high-bandwidth force control, the proposed controller can address the robust stability and performance against model parameter variance and exogenous disturbances. For the analytic and quantitative assessment of the proposed force control system, the dynamic characteristics of an SEA under various control algorithms are analyzed, and the experimental results are provided for an actual SEA system in this paper.

Robust estimation of vehicle states (e.g., vehicle sideslip angle and roll angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating sideslip angle and roll angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based sideslip angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle sideslip angle and roll angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig. 4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

Controlling an immeasurable state with an indirect control input is a difficult problem faced in traction control of vehicles. Research on motion control of electric vehicles (EVs) has progressed considerably, but traction control has not been so sophisticated and practical because of this difficulty. Therefore, this paper takes advantage of the features of driving motors to estimate the maximum transmissible torque output in real time based on a purely kinematic relationship. An innovative controller that follows the estimated value directly and constrains the torque reference for slip prevention is then proposed. By analysis and comparison with prior control methods, the resulting control design approach is shown to be more effective and more practical, both in simulation and on an experimental EV.

This paper presents a novel disturbance compensation scheme to attenuate periodic disturbances on repetitive motion using permanent magnet linear synchronous motors (PMLSMs), and this scheme is called the periodical adaptive disturbance observer. The scheme is based on assumptions that all measured states and disturbances are periodic and repetitive when the tasks executed by PMLSM motion systems have periodic and repetitive characteristics. In the proposed control scheme, a lumped disturbance is estimated by the classical linear disturbance observer (DOB) for the initial time period and stored in memory storages. It consists of parametric errors multiplied by states, friction force, and force ripple, and then, it is updated for each time period by the periodic adaptation law. This scheme requires no mathematical models of disturbances and adaptation laws of model parameters such as the mass of the mover and viscous friction coefficient. Also, it is possible to compensate for disturbances above as well as below the bandwidth of the Q-filter (LPF) of DOB. The effectiveness of the proposed control scheme is verified by various experiments that take into account varying frequency components of disturbances along the operating speed of a mover of PMLSM such as force ripple and friction force.