Synchronous reference frame phase locked loop (SRF-PLL) is widely used for grid synchronization and control in grid-connected applications. A major problem with SRF-PLL is how to improve its dynamic performance and filtering capability under complex grid conditions such as unbalance, distortion, and dc offset mixing without affecting its phase tracking performance and stability. In order to achieve this goal, a novel second-order generalized integrator (NSOGI) and a modified NSOGI (MNSOGI) with dc offset rejection capability based on NSOGI are proposed, and then, an effective hybrid filter is designed and incorporated into the inner loop of a SRF-PLL. The proposed hybrid filter is a combination of a dual MNSOGI and a moving average filter (MAF), it can block the fundamental frequency negative sequence component, dc offset component, and the rest of harmonic components in the SRF-PLL input three-phase voltages at the same time with a simple complexity. The proposed PLL in this paper has a faster transient response due to reducing the window length of the MAF. A small-signal model of the proposed PLL is derived. The stability is analyzed, and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through simulation experimental results.

In the new energy grid-connected power generation system, accurately extracting the grid synchronization signals such as the frequency, phase and amplitude of the grid voltage is the basis for effective control. Aiming at the requirements for detecting grid synchronization signals under unbalanced, harmonics and DC offset voltage mixed conditions, a dual second-order complex coefficient filter with DC offset rejection capability (DSOCCF _dc ) is proposed, combining the approach of moving average filter (MAF), a novel hybrid filter in dq-frame is designed and on the basis of this design a new synchronous reference frame phase locked loop (SRF-PLL) design approach based on the hybrid filter is proposed. The proposed approach employs moving average filter (MAF) to block the high-frequency harmonics in the grid voltage, and uses DSOCCF _dc to separate the fundamental frequency positive and negative sequence and reject DC offset. It can accurately extract the synchronization information of the grid fundamental frequency positive sequence. After simulation and experiment verification, it can be confirmed that the proposed PLL can quickly and accurately lock the properties of the grid voltage under adverse grid condition, and also have high detection accuracy and strong robustness to frequency fluctuations.

The phase-locked accuracy of conventional phase-locked method is reduced when the grid voltage contains fundamental frequency negative sequence(FFNS) component, harmonic component, and DC offset component. Aiming at this problem, a novel adaptive notch filter (NANF) is proposed, and a dual NANF (DNANF) structure is designed to eliminate the FFNS component and extract the fundamental frequency positive sequence(FFPS) component based on NANF. Furthermore, dcDNANF with DC offset rejection capability is proposed by adopting DNANF. Then a novel hybrid filter in dq-frame is designed by combining dcDNANF and the cascaded delay signal cancellation operator filter whose delay parameters are 4 and 24 in dq-frame (dqCDSC4,24). Meanwhile, a new SRF-PLL design method is proposed based on the novel hybrid filter. This proposed method employs dqCDSC4,24 to separate the positive and negative sequences of the voltage and eliminate the high frequency harmonics in the grid voltage, and uses dcDNANF to reject the DC offset, so as to achieve the accurate acquisition of the fundamental voltage information under distortion and unbalanced grid. Simulation and experimental results show that compared with the conventional SRF-PLL methods, the proposed method can obtain faster phase tracking speed, better phase-locked effect, fast dynamic response, and better stability.

Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to minimize the deviation between the prediction error and the control target by optimizing the control algorithm. The robust H-infinity controller can maintain stability in the face of system model uncertainty and external disturbances. The controller designed in this paper divides the attitude control loop into the following two parts: the angle loop and the angular velocity loop. The angle loop, serving as the main control loop of the attitude control, employs the robust H-infinity controller to process the angle of the quadrotor UAV and then transmits the processed value to the MPC controller. This approach reduces the computational load of the MPC controller. Simultaneously, by optimizing the algorithm, MPC minimizes the prediction error and the deviation from the control target. Simulation experiments confirm that the proposed algorithm improves the stability of the UAV attitude under random airflow disturbance conditions, while also achieving accurate tracking of the UAV’s position.

Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method.

The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability.