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In the multi-splitting transmission lines extreme power environment of ultra-high voltage and strong electromagnetic interference, to improve the trajectory tracking and stability control performance of the robot manipulator when conduct electric power operation, and effectively reduce the influence of disturbance factors on the robot motion control, this paper aims to presents a robust trajectory tracking motion control method for power cable robot manipulators based on sliding mode variable structure control theory.

Through the layering of aerial-online-ground robot three-dimensional control architecture, the robot joint motion dynamic model has been built, and the motion control model of the N-degrees of freedom robot system has also been obtained. On this basis, the state space expression of joint motion control under disturbance and uncertainty has been also derived, and the manipulator sliding mode variable structure trajectory tracking control model has also been established. The influence of the perturbation control parameters on the robot motion control can be compensated by the back propagation neural network learning, the stability of the controller has been analyzed by using Lyapunov theory.

The robot has been tested on a analog line in the lab, the effectiveness of sliding mode variable structure control is verified by trajectory tracking simulation experiments of different typical signals with different methods. The field operation experiment further verifies the engineering practicability of the control method. At the same time, the control method has the remarkable characteristics of sound versatility, strong adaptability and easy expansion.

Three-dimensional control architecture of underground-online-aerial robots has been proposed for industrial field applications in the ubiquitous power internet of things environment (UPIOT). Starting from the robot joint motion, the dynamic equation of the robot joint motion and the state space expression of the robot control system have been established. Based on this, a robot closed-loop trajectory tracking control system has been designed. A robust trajectory tracking motion control method for robots based on sliding mode variable structure theory has been proposed, and a sliding mode control model for the robot has been constructed. The uncertain parameters in the control model have been compensated by the neural network in real-time, and the sliding mode robust control law of the robot manipulator has been solved and obtained. A suitable Lyapunov function has been selected to prove the stability of the system. This method enhances the expansibility of the robot control system and shortens the development cycle of the controller. The trajectory tracking simulation experiment of the robot manipulator proves that the sliding mode variable structure control can effectively restrain the influence of disturbance and uncertainty on the robot motion stability, and meet the design requirements of the control system with fast response, high tracking accuracy and sound stability. Finally, the engineering practicability and superiority of sliding mode variable structure control have been further verified by field operation experiments.

When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios.

Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm.

Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm.

This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]).

First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error.

The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality.

An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

A welding robot is a complicated system with uncertainty, time-varying, strong coupling and non-linear system. It is more complicated if it is used in an underwater environment. It is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the classical proportional integral derivative control method to realize automatic tracking of the seam. The purpose of this paper is to suggest a novel method to deal with these issues.

To combine the advantages of active disturbance rejection control (ADRC) and sliding mode control (SMC) to improve the shortcomings of a single control method, a hybrid control method for an underwater welding robot trajectory tracking based on SMC_ADRC is proposed in this research work.

The simulation experiment of the proposed approach is carried out by Matlab/Simulink, and the welding experiment is recorded. The seam gets plumper and smoother, with better continuity and no undercut phenomenon.

The proposed approach is effective and reliable, and the system’s tracking performance is stable, which can effectively reduce chattering and improve system robustness.

The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.

This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.

The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.

A high-precision gyroscope is an important tool for accurate positioning, and the amplitude stability and frequency tracking ability of the drive control system are important and necessary conditions to ensure the precision of micro-electro-mechanical systems (MEMS) gyroscopes. To improve the precision of MEMS gyroscopes, this paper proposes a method to improve the amplitude stability and frequency tracking ability of a drive control system.

A frequency tracking loop and an amplitude control loop are proposed to improve the frequency tracking ability and amplitude stability of the drive control system for a MEMS gyroscopes. The frequency tracking loop mainly includes a phase detector, a frequency detector and a loop filter. And, the amplitude control loop mainly includes an amplitude detector, a low-pass filter and an amplitude control module. The simulation studies on the frequency tracking loop, amplitude control loop and drive control system composed of these two loops are implemented. The corresponding digital drive control algorithm is realized by the Verilog hardware description language, which is downloaded to the application-specific integrated circuits (ASIC) platform to verify the performances of the proposed method.

The simulation experiments in Matlab/Simulink and tests on the ASIC platform verify that the designed drive control system can keep the amplitude stable and track the driving frequency in real time with high precision.

This study shows a way to design and realize a drive control system for MEMS gyroscopes to improve their tracking ability. It is helpful for improving the precision of MEMS gyroscopes.

This paper aims to focus on the variable stick force-displacement (SFD) gradience in the active side stick (ASS) servo system for the civil aircraft.

The problem of variable SFD gradience was introduced first, followed by the analysis of its impact on the ASS servo system. To solve this problem, a linear-parameter-varying (LPV) control approach was suggested to process the variable gradience of the SFD. A H_∞ robust control method was proposed to deal with the external disturbance.

To validate the algorithm performance, a linear time-variant system was calculated to be used to worst cases and the SFD gradience was set to linear and non-linear variation to test the algorithm, and some typical examples of pitch angle and side-slip angle tracking control for a large civil aircraft were also used to verify the algorithm. The results showed that the LPV control method had less settling time and less steady tracking errors than H_∞ control, even in the variable SFD case.

This paper presented an ASS servo system using the LPV control method to solve the problem caused by the variable SFD gradience. The motor torque command was calculated by pressure and position feedback without additional hardware support. It was more useful for the electronic hydraulic servo actuator.

This was the research paper that analyzed the impact of the variable SFD gradience in the ASS servo system and presented an LPV control method to solve it. It was applicable for the SFD gradience changing in the linear and non-linear cases.

The rotorcraft technology is very interesting area since last few decades due to variety of applications. One of the rotorcrafts is the quadrotor unmanned aerial vehicle (QUAV), which contains four rotors mounted on an airframe with an onboard controller. The QUAV is a highly nonlinear system and underactuated. Its controller design is very challenging task, and the need of controller is to make it autonomous based on mission planning. The purpose of this study is to design a controller for quadrotor UAV for attitude stabilization and trajectory tracking problem in presence of external environmental disturbances such as wind gust.

To address this problem, the model predictive control has been designed for attitude control and feedback linearization control for the position control using the linear parameter varying (LPV) approach. The trajectory tracking problem has been addressed using the circular trajectory and helical trajectory.

The simulation results show the efficient performance with good trajectory tracking even in presence of external disturbances in both the scenarios considered, one for circular trajectory tracking and other for helical trajectory tracking.

The novelty of the work came from using the LPV approach in controller design, which increases the robustness of the controller in presence of external disturbances.

This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads.

In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated.

The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity.

In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads.

The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.