In this paper, a novel direct torque control (NDTC) scheme utilizing composite active vectors based on discrete duty ratio modulation method is presented for permanent magnet synchronous motor (PMSM) in industrial robot servo control system. The errors in the PMSM driven by conventional DTC (CDTC) are compensated by single active vector during each control period, leading to unsatisfied steady-state performance. To improve the operation performance of the industrial robot servo control system effectively, the errors in the PMSM are compensated independently in NDTC. Therefore, two active vectors should be applied to compensate flux error and torque error, respectively, namely flux active vector and torque active vector. In order to maintain the merit of the fast dynamic response in CDTC, the introduced active vectors should be selected from the fixed six active vectors supplied by a two-level voltage source inverter. To describe the scheme of NDTC clearly, the angle between the applied active vector and the stator flux linkage is used to represent the error compensational effect provided by the applied active vector, namely active angle. Additionally, the compensational effect is defined as active factor. Subsequently, the duty ratio value of the active vector applied time can be derived easily. To simplify the calculations of the duty ratio values, novel discrete calculation method for the active factor by hysteresis controllers is presented in this paper. The effectiveness of the proposed NDTC strategy is verified through the experimental results on a 100-W PMSM drive system.

In this paper, the wireless power transfer system (WPT) system with dynamic loads such as batteries is studied comprehensively. An integrated control technology of load estimation and power tracking of LCC compensated is proposed, which realizes load estimation, mode judgment and charging control at the transmitter, and standard load setting and decoupling control at the receiver. Based on the influence of reflection impedance on the output current of the inverter, a method of identifying coupling coefficient and equivalent load is proposed and a mathematical model is established. Receiver controller provides standard reference load for load estimation. Transmitter controller judges battery status according to the estimation of equivalent load and adopts double closed-loop control to regulate power and current. Receiver decouples control when battery charging voltage reaches the threshold, and providing mode conversion sign for transmitter controller to realize constant current (CC) and constant voltage (CV) charging of battery. Dual-sides integrated control scheme has no data communication between transmitter and receiver, so it can control independently, which reduces the complexity of the system and is suitable for different charging modes. The proposed controller is more efficient as it maintains a track current, and dynamically alters the pick-up characteristics to suit the load demand. Finally, the simulation and experimental results validate the feasibility of proposed control method, which realizes the estimation of the load and CC/CV charging of the battery. The proposed WPT system achieved the efficiency at 91.16% while delivering 2kW to the load with a vertical air gap of 150mm.

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. Note to Practitioners —The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.