A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability.

A Novel Decentralized Fixed-Time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

This paper presents a novel decentralized fixed-time tracking control approach, which realizes the advantages of modular robot manipulators (MRMs) with fixed-time convergence, strong robustness, and high tracking performance. First, to estimate the total uncertainty of MRMs, the fixed-time observer based on the extended state is developed. Then, combined with the disturbance observer, a novel decentralized control method based on a fixed-time control strategy was devised to accomplish global fixed-time convergence of MRMs. And, stability analysis based on Lyapunov is utilized to obtain the fixed-time stability as well as convergence time of MRMs. Finally, numerical analysis and experiment respectively verify the excellent tracking ability of the presented decentralized fixed-time tracking control.

Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

A filter algorithm for receiver tracking loops assisted by inertial information

In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy

Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.

Multi-Virtual-Vector Model Predictive Current Control for Dual Three-Phase PMSM

This paper proposes a multi-virtual-vector model predictive control (MPC) for a dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to regulate the currents in both fundamental and harmonic subspace. Apart from the fundamental α-β subspace, the harmonic subspace termed x-y is decoupled in multiphase PMSM according to vector space decomposition (VSD). Hence, the regulation of x-y currents is of paramount importance to improve control performance. In order to take into account both fundamental and harmonic subspaces, this paper presents a multi-virtual-vector model predictive control (MVV-MPC) scheme to significantly improve the steady performance without affecting the dynamic response. In this way, virtual vectors are pre-synthesized to eliminate the components in the x-y subspace and then a vector with adjustable phase and amplitude is composed of two effective virtual vectors and a zero vector. As a result, an enhanced current tracking ability is acquired due to the expanded output range of the voltage vector. Lastly, both simulation and experimental results are given to confirm the feasibility of the proposed MVV-MPC for DTP-PMSM.

Research on Aiming and Tracking Ability Evaluation Model of Laser-guided Missile

Laser guidance is a continuous process, during which a lot of operational data and corresponding changes are generated. Aiming at the live-fire drill shooting training and assessment evaluation of a certain type of laser-guided missile, this paper analyzes the principle of laser guidance, studies the weighting system of the information field based on the importance of characteristic intervals, integrates and processes the data and outputs the aiming effect picture and sheet, and constructs the aiming and tracking ability evaluation model. Stationary targets and moving targets are tested respectively, and the verification model can realize the calculation of laser information field, the integration of coordinate data, the output of aiming effect diagram, and the evaluation of the shooter’s aiming and tracking ability expressed in a percentage system. The model realizes data collection, integration and processing based on MATLAB software, and builds graph and table output ports, which can realize low delay processing of a large amount of short-term data.

Decentralized Phase Shedding with Low Power Mode for Multiphase Converter

With a multiphase converter, the phase-shedding function dedicated to maximizing the power efficiency, in a manner that is dependent on the load current, is always provided by a centralized controller that induces a Single Point of Failure (SPOF). The objective of this study is to obtain a decentralized control approach to implement this function by removing any SPOF. The method consists of using identical local controllers, each associated with a converter phase, that communicate with each other in a daisy-chain structure. Instead of measuring the global output current to determine the optimal number of active phases required, each local controller measures its own leg current and takes a local decision based on threshold crossing management and inter-controller communications. Functional simulations are carried out on a 5-leg 12 V/1.2 V 60 W multiphase converter supplying a modern microcontroller. They demonstrate that the number of active phases is well adjusted, in a dynamic manner, depending on the load current level. Specific events such as load current inrush or the start-up sequence are analyzed to guarantee optimal transient responses. A maximum power efficiency tracking ability is also demonstrated. Finally, it is shown that this control strategy allows phase shedding to be implemented using as many phases as desired, in a modular manner, thereby avoiding any centralized processing.

Modeling and Design of an Aircraft-Mode Controller for a Fixed-Wing VTOL UAV

Vertical takeoff and landing (VTOL) is an essential feature of unmanned aerial vehicles (UAVs). On the one hand, VTOL can expand and enhance the applications of UAVs; yet, on the other hand, it makes the design of control systems for UAVs more complicated. The most challenging demand in designing the control system is to achieve satisfactory response sharpness of fixed-wing UAVs to control commands and ensure that the aircraft mode channels are effectively decoupled. In this work, a six-degree-of-freedom (6-DoF) model with forces and moments is established based on the aerodynamic analysis, which is carried out through computational fluid dynamics (CFD) numerical simulation. The improved proportional derivative (PD) controller based on the extended state observer (ESO) is proposed to design the inner-loop attitude control, which increases the anti-interference ability for internal and external uncertainty of the UAV system. The motion equations of the UAV are established and divided into independent components of longitudinal and lateral motion to design the outer loop control law under minor disturbance conditions. A total energy control system (TECS) for the longitudinal height channel is proposed, which separates speed control and track control. L1 nonlinear path tracking guidance algorithm is used for lateral trajectory tracking so as to improve curve tracking ability and wind resistance. Effectiveness of this approach is proved by actual flight experiment data. Finally, a controller based on angular velocity control is designed to prevent the attitude and head reference system (AHRS) from malfunctioning. Its effectiveness is verified by the response test of the control system.