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Reverse well drilling rig is an important support for realizing deep mineral resource extraction, and the precise control law of its power head is crucial for improving drilling efficiency and reducing safety accidents. In challenging environments and varying rock strata distributions at different well depths, Proportional-Integral-Derivative (PID) controller is used to control the power head of the reverse drilling rig, despite the simple control, flexible and convenient adjustment, but due to the fact that there are only three parameters regulated by the PID controller, the control is not precise enough and the adjustment accuracy is not high enough, Overcoming the drawbacks of PID control technology at a fundamental level can be an extremely challenging task, which results in the failure of real-time feedback and precise control of the power head of the reverse drilling rig and precise control. The research is based on the theory of wellbore engineering machinery, fractionalorder control technology and intelligent optimization algorithm. Using a combination of physical modeling, mathematical modeling and simulation test analysis, the intelligent optimization algorithm (Gray Wolf Optimization (GWO)) is used to parameterize the fractional-order PID (FOPID) controller and apply it to the rotational speed control of power head. The energy conservation relationship is coupled to the kinetic mathematical model of the power head. Laplace transform analysis is utilized to derive the integer-order mathematical transfer function linking the power head speed and the electric displacement signal. The fractional-order mathematical transfer function is subsequently determined using the least square method. With the help of MATLAB-Simulink software, build a model of electro-hydraulic proportional control system for countershaft drilling rigs, adopt six intelligent optimization algorithms to adjust the parametric fractional-order controller, and examine the impact of step disturbances on various control combination methods of electro-hydraulically coupled power head and countershaft drilling rig motors through the three dynamic indexes of standard variance, overshooting amount and stabilization time. Bode and Nyquist plots are used to determine that the system has good stability and robustness with a controller. The simulation analysis confirms the reasonableness and accuracy of the gray wolf algorithm (GWO) for adjusting the FOPID parameters, and the joint simulation with the help of AMEsim and Simulink software verifies the stability and timeliness of the variable pump’s response, and at the same time confirms that the control method of the gray wolf algorithm (GWO) has a very good control effect. This research establishes a solid theoretical groundwork for controlling the power head with precision and the development of wellbore engineering. It also serves as a guide for creating motion control methods for large mechanical equipment.

Motion control and energy-saving optimization are the research hotspots in the field of autonomous vehicles. This study takes four-wheel independent drive (4WID) electric vehicles (EVs) in CDC 2023 Challenge. Aiming to address the issues of desired speed tracking, vehicle body motion control, and energy consumption minimization posed by the challenge, the vehicle driving resistance was analyzed, and a vehicle longitudinal dynamics model was established. The extended state observer (ESO) is utilized to estimate the model error, thereby making the dynamic model approximate the real system. A controller combining a linear quadratic regulator (LQR) and Q-learning is designed. The total torque of the vehicle is obtained by LQR method, and then the torque is distributed to the four wheels by Q-learning, so as to realize the tracking of speed and course, and ensure the smooth operation and energy-saving control of the autonomous vehicles. Simulation results indicate that the proposed control strategy can achieve precise speed tracking control and outperforms both the PID and fuzzy rule controllers in reducing vehicle body motion and energy consumption.

In this paper, we leverage a reinforcement learning approach to address the motion control problem of Four In-Wheel Motor Actuated Vehicles aimed at achieving precise control while optimizing energy efficiency. Our control architecture consists of four adaptive Proportional-Integral-Derivative controllers, each assigned to an independent vehicle wheel. We train these controllers using an actor-critic framework in two standard driving scenarios: acceleration and braking, as well as a double lane-change maneuver. This method eliminates the need for a detailed mathematical model of the complex vehicle dynamics. Moreover, the adaptive mechanism enables controllers to dynamically adapt to varying operating conditions. After training, the resulting controllers are tested in unseen scenarios to validate their robustness and adaptability beyond the training environment. The testing results show that our controllers achieve precise velocity and trajectory tracking while maintaining low energy consumption.

In this paper, the vehicle position points obtained by multi-sensor fusion are taken as the observed values, and Kalman filter is combined with the vehicle kinematics equation to further improve the vehicle trajectory. To realize this, mathematical principles of deep reinforcement learning are analyzed, and the theoretical basis of reinforcement learning is also analyzed. It is proved that the controller based on dynamic model is better than the controller based on kinematics in deviation control, and the performance of the controller based on deep reinforcement learning is also verified. The simulation data show that the proportion integration differentiation (PID) controller has a better tracking effect, but it does not have the constraint ability, which leads to radical acceleration change, resulting in unstable acceleration and deceleration control. Therefore, the deep reinforcement learning controller is selected as the longitudinal velocity tracking controller. The effectiveness of lateral and longitudinal motion decoupling strategy is verified by simulation experiments.

This paper presents the development of a digital signal processor (DSP) based motion controller for a linear servo motor system. The linear motor system employs a moving magnet non-commutated DC linear servo motor as the actuator, and a linear variable differential transformer (LVDT) as the position feedback transducer. It is ideal for short stroke, high accuracy and high speed closed loop servo applications. The controller hardware is based on a TI TMS320LF2407A DSP. The system architecture and motion control strategies are presented. An index motion is implemented using this system. The experimental results with discussions are also given.

In this paper, a self-adaptive PD (SAPD) is employed for motion control of omni-directional robots. The method contains a PD controller that can be tuned online using a fuzzy logic system (FLS). Fast and accurate positioning is one of significant challenges in robot platforms. In addition, some uncertainties have adverse effects on traditional control system's performance during the robot's motion. Slow responses, low accuracy and instability are the most important drawbacks of widespread controllers in presence of uncertain dynamics. Since the fuzzy algorithm can deal with uncertainties and nonlinearities, the proposed method can tackle the mentioned problems. The controller is designed based on an uncertain model and implemented on a four wheeled omni-directional fast robot. The novelty of this article is proposing an enhanced version of well-known gain scheduling PD controller to improve positioning performance of the robot in different circumstances. Experimental results show that the method can provide a desirable performance in the presence of uncertainties.

This paper introduces a relay-based approach to control the motion of robot joints with friction and gravity load. This approach uses relay feedback tests to estimate the disturbances including Coulomb friction, viscous friction, and the gravity load. The relay feedback tests take the robot joint angular as the feedback signal and identify the friction value and the gravity load at the same time. A control scheme is then presented including a feedforward friction compensator and a feedforward gravity compensator based on the estimated results. With the disturbances properly compensated, the proposed approach improves the tracking performance of a robotic system. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

This paper presents a momentum-based control framework for floating-base robots and its application to the humanoid robot “Atlas”. At the heart of the control framework lies a quadratic program that reconciles motion tasks expressed as constraints on the joint acceleration vector with the limitations due to unilateral ground contact and force-limited grasping. We elaborate on necessary adaptations required to move from simulation to real hardware and present results for walking across rough terrain, basic manipulation, and multi-contact balancing on sloped surfaces (the latter in simulation only). The presented control framework was used to secure second place in both the DARPA Robotics Challenge Trials in December 2013 and the Finals in June 2015.

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

This paper presents a control scheme for the humanoid robot TEO’s elbow joint based on a novel tuning method for fractional-order PD and PI controllers. Due to the graphical nature of the proposed method, a few basic operations are enough to tune the controllers, offering very competitive results compared to classic methods. The experiments show a robust performance of the system to mass changes at the tip of the humanoid arm.

This paper presents the development of a piano-playing robot in order to provide people a means of entertainment. The design and development of this project includes two parts: the design of a dexterous hand for manipulating a piano and a linear motion control system. The paper first discusses the design of dexterous hand. Then, a motion control solution is determined, a linear railing along with a rack and pinion gear are applied to produce the linear motion. Also, in order to improve the control performance of the linear motion system, the Extended Kalman Filter (EKF) Algorithm is adopted. Finally, a flow chart of the control system is presented. Experimental results show that the piano robot can play designated notes.

In this study, a unified motion planner with low level controller for continuous control of a differential drive mobile robot under variable payload values is presented. The deep reinforcement agent takes 11-dimensional state vector as input and calculates each wheel’s torque value as a 2-dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, intersection navigation solutions that uses deep-RL methods, have not been considered with variable payloads. This study is focused specifically on service robotic applications where payload is subject to change. In this study, deep-RL-based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent successfully navigates to target with 98.2% success rate in test time with unseen payload masses during training. Another agent is also trained without payload randomization for comparison. Results show that our agent outperforms the other agent, that is not aware of its own payload, with more than 40% gap. Our agent is also compared with the Time-to-Collision (TTC) algorithm. It is observed that our agent uses far less time than TTC to accomplish the mission while success rates of two methods are same. Lastly, the proposed method is applied on a real robot in order to show the real-time applicability of the approach.

In addressing the challenges of short-range spacecraft docking in the presence of obstacles and disturbances, it is critical to integrate guidance and motion control to ensure autonomous and reliable operation. Traditional methods that separate these two layers often struggle with accurately tracking predefined paths, increasing the risk of collisions. In light of this, a proposed scheme integrating guidance and control in an organic manner has been put forth. This scheme employs the rapidly-exploring random trees (RRT) algorithm within the guidance layer to generate a collision-avoidance trajectory for the control layer, efficiently navigating the spacecraft towards its target. Then the control layer implements a second-order output-constrained controller by adding a power integrator and a novel barrier Lyapunov function (BLF) together, to guarantee that the tracking error of the predefined trajectory remains bounded and the system asymptotically converges to the target. To account for tracking errors, obstacle radii are expanded during path planning through a dilation constant. Based on theoretical derivation and simulation experiments, the effectiveness and advancement of the proposed method are validated.

Motion control is a key aspect for the performance of wheeled mobile robots (WMR). There are several well known motion control methods for WMR, but usually those methods are very dependent from the robot physical constraints. One of the most commonly employed platforms to propose and demonstrate motion control algorithms is the differential drive platform and its variants. Every time a new motion control algorithm is proposed, some comparisons with traditional algorithms are usually made, but there are no set of benchmarks globally accepted to assess the performance of motion control algorithms. This work tries to make some contributions in this area, analyzing the problem and proposing a set of benchmarks to be used in the evaluation of mobile robots' motion control algorithms.

One interesting problem that urban search and rescue (USAR) robots face is the process of climbing stairs. In this paper, an algorithm for the autonomous stair climbing is presented, using only pitch and roll angles, as measured by an accelerometer sensor. A skilled human operator is required to climb stairs manually, therefore, doing so autonomously allows for a more efficient robot operation in search and rescue scenarios. Tests were made using RAPOSA, a tracked wheels USAR robot, and results have shown that the proposed control algorithm was capable of climbing several kinds of stairs. An empirical evaluation comparing it with human teleoperation showed an overall more reliable and faster operation in the majority of the tests. This difference is even more significant when the human operator is limited to the robot's eyes.

This article presents an application for dynamic simulation of legged robots based on a physics engine. In the presented application an iterative solver is supported by analytical equations of the dynamics and software modules for collision detection, environment modeling and visualization. The presented application of the simulator allows for development and verification of control algorithms before their implementation on the real robot.

Smart intelligent robotic fish has shown promising advantage in underwater searching. This paper addresses the smart robotic shark design and control issues with multi-sensors. In particular, we propose a new design of a two-link mechanism robotic shark equipped with gyroscope, pressure sensor, infrared sensor, and light sensor. Then three-dimensional motion control, depth control, autonomous obstacle avoidance, and light navigation are developed. In particular, a bio-inspired Central Pattern Generator (CPG) based control method is adopted to smoothly control the robotic shark's locomotion in all the above realization. All motion control methods are implemented in real time with a hybrid control system based on embedded microprocessor (STMicroelectronics STM32F407). Latest aquatic experiments demonstrate a fairly good result in improving the robotic shark's intelligence. The developed scheme affords an alternative to smart robotic fish design in complex underwater environments.