Control Method For Manipulators Research Articles

Model-free sliding mode prescribed performance control of robotic manipulator based on new reaching law

The article proposes a model-free sliding mode prescribed performance control method for robotic manipulator. For accelerating the errors converging rate as well as bringing down the real-time control torque, an error-driven nonsingular fast terminal sliding mode is employed in this control method. Besides, a modified power reaching law is newly designed, which is able to offer better chattering elimination performance than traditional power reaching law. To solve the problem that the controller’s performance highly relies on the accuracy of robotic model, a model-free control idea is introduced where an ultra-local model is established and the estimation of time delay is used to approximate the unknown part of the ultra-local model. Considering the transient performance is a significant element that effects the using security and system stability, the technique of prescribed performance control is introduced to limit the state errors in a prescribed region. The closed-loop stability proof of the whole system is achieved according to Lyapunov’s stability theorem. The proposed controller’s superiority and feasibility compared with other controllers are demonstrated by numerical simulations.

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

This paper proposes a fixed-time neural network-based prescribed performance control method (FNN-PPCM) for robot manipulators. A fixed-time sliding mode controller (SMC) is designed with its strengths and weaknesses in mind. However, to address the limitations of the controller, the paper suggests alternative approaches for achieving the desired control objective. To maintain stability during a robot’s operation, it is crucial to keep error states within a set range. To form the unconstrained systems corresponding to the robot’s constrained systems, we apply modified prescribed performance functions (PPFs) and transformed errors set. PPFs help regulate steady-state errors within a performance range that has symmetric boundaries around zero, thereby ensuring that the tracking error is zero when the transformed error is zero. Additionally, we use a singularity-free sliding surface designed using transformed errors to determine the fixed-time convergence interval and maximum allowable control errors during steady-state operation. To address lumped uncertainties, we employ a radial basis function neural network (RBFNN) that approximates their value directly. By selecting the transformed errors as the input for the RBFNN, we can minimize these errors while bounding the tracking errors. This results in a more accurate and faster estimation, which is superior to using tracking errors as the input for the RBFNN. The design procedure of our approach is based on fixed-time SMC combined with PPC. The method integrates an RBFNN for precise uncertainty estimation, unconstrained dynamics, and a fixed-time convergence sliding surface based on the transformed error. By using this design, we can achieve fixed-time prescribed performance, effectively address chattering, and only require a partial dynamics model of the robot. We conducted numerical simulations on a 3-DOF robot manipulator to confirm the effectiveness and superiority of the FNN-PPCM.

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies, cyber-physical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks. While lots of benefits can be achieved with such a configuration, it also brings the concern of cyber attacks to the industrial control systems, such as networked manipulators that are widely adopted in industrial automation. For such systems, a false data injection attack on a control-center-to-manipulator (CC-M) communication channel is undesirable, and has negative effects on the manufacture quality. In this paper, we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model. Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack. The efficacy of the proposed method is validated via simulations.

Taylor‐based adaptive sliding mode control method for robot manipulators

AbstractThis paper develops a novel Taylor‐based adaptive sliding mode control method (ASMC) for robot manipulators. In the first new scheme, sliding mode control (SMC) is effectively enhanced using the Taylor expansion for achieving a less conservative sign‐function gain that enables chattering attenuation. After that, a new Taylor‐based adaptive SMC scheme is proposed without prior knowledge of the upper bound of uncertainty and the Taylor expansion coefficients. Then, a new Taylor‐based boundary layer ASMC scheme is proposed for chattering attenuation. As a fact, no chattering is expected to be observed as long as the sliding surface remains an invariant set. However, the sliding surface will not be an invariant set unless the sign‐function is precisely determined on the sliding surface. It is verified that the sign‐function cannot be precisely determined on the sliding surface due to the presence of uncertainty, hence, the chattering phenomenon. In the new Taylor‐based boundary layer ASMC scheme, the conventional ASMC is modified by removing the sign‐function in the vicinity of the sliding surface and using the Taylor expansion to estimate and compensate for the uncertainty. Compared to a time‐delay ASMC applied to a robot manipulator, the Taylor‐based ASMC scheme exhibits a less conservative sign‐function gain resulting in chattering attenuation.

Fixed-Time Sliding Mode-Based Active Disturbance Rejection Tracking Control Method for Robot Manipulators

This work investigates the issue of a hybrid trajectory tracking control algorithm (HTCA) for robot manipulators (RMs) with uncertain dynamics and the effect of external disturbances. Following are some proposals for achieving the control target. Firstly, to achieve the active disturbance rejection, we propose a uniform second-order sliding mode disturbance observer (USOSMDO) to obtain directly the lumped uncertainties without their prior upper-bound information. Secondly, a fixed-time singularity-free terminal sliding surface (FxSTSS) is proposed to obtain a fixed-time convergence of the tracking control error (TCE) without the singularity in the control input. Then, using information on the proposed USOSMDO, our HTCA is formed based on the FxSTSS and the fixed-time power rate reaching law (FxPRRL). The control proposal not only stabilizes with the global fixed-time convergence but also attains high tracking accuracy. In addition, the chattering problem also is handled almost completely. Finally, numerical simulations verify the effectiveness and advantages of applying the proposed HTCA to a FARA robot.

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

SummarySliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.

Event-Triggered Robust Optimal Control for Robotic Manipulators with Input Constraints via Adaptive Dynamic Programming

This paper proposes an event-triggered robust tracking control method for robotic manipulators with asymmetrical input constraints based on adaptive dynamic programming (ADP). First, the original robust tracking control problem is transformed into an optimal control problem for a nominal system with the help of a novel cost function design. Next, an event-triggered optimal control approach is proposed to solve this optimization problem and obtain the optimal solution to the Hamilton-Jacobi-Bellman (HJB) equation. The classical ADP framework is then used to approximate the optimal cost function and controller. Unlike existing weight adaptive laws, an additional term is introduced for removing the requirement of initial stabilization. Based on the Lyapunov theory, the stability of the original robotic system and the convergence of weight estimation are both guaranteed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control method.

Review of intelligent control methods for manipulators with rigid links

Robotic manipulators, which are mechanical devices with electronic control and several links, used to perform specific tasks under certain circumstances are considered. A detailed review of intelligent control methods implemented in robotic manipulator systems is presented. Keywords systems, control, robotics, automation, neural network, fuzzy logic, intelligent control

Neural Active Disturbance Rejection Adaptive Lateral Manipulation Control Method for Unmanned Driving Robot

In this article, a neural active disturbance rejection adaptive lateral manipulation control method for an unmanned driving robot (UDR) is proposed to realize accurate and stable steering and path tracking. Combined with a model of the manipulated vehicle and steering manipulator, an integrated dynamics model of the vehicle manipulated by a UDR is established. Taking the error of the body heading angle and the lateral error of the vehicle as input, an active disturbance rejection controller is designed; it includes a tracking differentiator, nonlinear state error feedback (NLSEF) device, and an extended state observer. To achieve a better performance, the combination mode of the NLSEF is adjusted adaptively by a radial basis function NN. The network is then initialized by a particle swarm optimization algorithm. Finally, the results of simulations and experiments show that the proposed method effectively improves the performance of stable steering and path tracking of the UDR.

Coordinated Compliance Control Method of Five-Axis Redundant Industrial Manipulator Based on Monocular Vision

In order to improve the accuracy of the five-axis redundant industrial robot arm in grasping static objects and shorten the grasping time, a coordinated compliance control method based on a monocular vision for the five-axis redundant industrial robot arm is proposed in this paper. Using the monocular vision ranging method, the three-dimensional coordinates of the target object in a base coordinate system of the five-axis redundant industrial robotic arm are calculated and object target positioning is achieved. According to the acquired object target position, the traditional Euler angle is used to calculate the actuator posture impedance at the end of the robotic arm, thereby realizing the coordinated compliant control of the five-axis redundant industrial manipulator. The simulation experiment results show that the proposed coordinated compliance control method for a five-axis redundant industrial manipulator based on monocular vision can successfully grasp the target object in the shortest time and has high practical value.

General compensation control method of flexible manipulator driven by tendon-sheath mechanism

Flexible manipulator has been widely used because of its flexibility. In addition, the tendon-sheath mechanism has the advantages of compact structure and strong flexibility, and it can be used as the driving transmission mode of the flexible manipulator. However, the nonlinear and hysteretic characteristics of the tendon-sheath mechanism directly affect the motion accuracy of the manipulator. Firstly, the kinematic and static models of the flexible manipulator are analyzed considering the influence of multi section coupling. In addition, the mechanical transmission process of the tendon-sheath mechanism is analyzed. On this basis, a general feedforward compensation method for the flexible manipulator is established. The proposed model-based compensation control method can be applied to all flexible manipulators, and it is of great significance to promote the practical application of the manipulator.

Simulation Design of a Live Working Manipulator for Patrol Inspection in Power Grid

The distribution line network is the electric power infrastructure directly facing the users, with the characteristics of large coverage and complex network, and its operation safety is directly related to the stability and reliability of the power supply system, which is the key link to ensure the safety of power supply and the reliability of residential electricity consumption. In order to realize the autonomous obstacle avoidance and autonomous navigation of the live working manipulator for inspection and maintenance of the power grid equipment, a mobile manipulator intelligent control method combining SumTree-weighted sampling and deep deterministic policy gradient (DDPG) is proposed. Firstly, the traditional DDPG algorithm is improved to optimize the action value function of Q-learning to get a better control strategy, and the weighted sampling technique is used to add priority to each sample in the replay buffer, which improves the learning speed and accelerates the convergence speed. The corresponding environmental state space is designed, and simulation experiments are conducted to verify the proposed manipulator control method. Simulation results demonstrate that the proposed method performs better than traditional DDPG and DQN methods in obstacle avoidance and navigation tasks, with faster convergence, better path planning ability, and lower offset cost, which can provide theoretical and technical references for realizing fully autonomous power grid inspection operations.

Variable Time Magnetization Current Trajectory Control Method for Variable Flux Memory Machines

Magnetization state (MS) manipulation is crucial to the comprehensive performance improvement of variable flux memory machine (VFMM) drive system. This article proposes a novel MS manipulation control method wherein variable time magnetization current trajectories can be adaptively controlled to reduce the MS manipulation losses. A fuzzy proportional integral (PI) feedforward current controller is newly structured to track the trajectories, improving the current tracking ability. First, dynamic MS manipulation processes are simulated and analyzed deeply, in which the variable machine parameters are calculated by finite element method (FEM) and fitted with polynomial formulas. Based on these, the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">di</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dt</i> of magnetization current pulse trajectory, namely the shortest MS manipulation time, can be adaptively acquired to reduce the MS manipulation losses at different running conditions. Then, a fuzzy PI feedforward current controller is developed by combining the feedforward and fuzzy logic controls, ensuring the real current tracks its reference well at different speed and load conditions, particularly in the MS manipulation process. Finally, the effectiveness and feasibility of the proposed MS manipulation method are verified by experimental results on a hybrid-magnetic-circuit VFMM (HMC-VFMM) prototype.

Dynamic analysis and sliding mode control method of 5-DOF manipulator

Background: The five degree of freedom (5-DOF) manipulator greatly improves the machining efficiency and accuracy because of its high flexibility. They see wide application in various automation fields. The dynamic analysis and modeling of manipulators is of great significance to improve the working accuracy of a manipulator. Methods: For the robot task of sheet metal bending, a 5-DOF manipulator based on sliding mode control strategy is designed in this paper. Firstly, the dynamics of the 5-DOF manipulator is analyzed and the dynamic equation is established. Secondly, based on the principle of sliding mode control, a proportional integral (PI) sliding mode control method for 5-DOF manipulator based on nominal model is proposed. Finally, the sliding mode control simulation experiment of 5-DOF manipulator is carried out to verify its stability. Results: The 5-DOF manipulator with PI sliding mode control has a good control effect by overcoming the influence of modeling error due to its strong robustness, and effectively realizes good control stability. Conclusions: The experimental results show that the 5-DOF manipulator has good response speed and stability. The results also suggest that the manipulator can be widely used in complex scenarios such as medical surgery or industrial production line with high safety requirements.

Recursive recurrent neural network: A novel model for manipulator control with different levels of physical constraints

AbstractManipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators. Due to physical limitations and actuation saturation of manipulator joints, the involvement of joint constraints for kinematic control of manipulators is essential and critical. However, current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints, and to the best of our knowledge, methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported. In this study, for the first time, a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints, and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders. The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution. Simulation results further demonstrate the effectiveness of the proposed method in end‐effector path tracking control under different levels of joint constraints based on the Kuka manipulator system. Comparisons with other methods such as the pseudoinverse‐based method and conventional recurrent neural network method substantiate the superiority of the proposed method.

Interval-based optimal trajectory tracking control method for manipulators with clearance considering time-dependent reliability constraints

The soaring operating performance of robotic manipulators renders the significance of advanced controller design. However, the uncertainty in practice would unavoidably lead to the failure of manipulators. Meanwhile, the general statistical analysis to uncertainty may be too complicated or tricky and to virtually realize. In this paper, a non-probabilistic uncertainty quantification approach is proposed to simplify the process of system uncertainties. The corresponding response range is attended by collocation method with advantages upon precision and efficiency. Later, a time-dependent reliability (TDR) evaluation is presented by the first-passage theory. The controller design of manipulator system is built with the widely used PID control with the constraint of TDR to ensure manipulator's normal operation under the trajectory tracking course. The proposed method innovates in simplification and precision of uncertainty quantification and response evaluation, as well as the non-probabilistic time-dependent reliability constraints which well suit for controller design for manipulator systems. Three numerical examples are presented to prove the function and effectiveness of the proposed design approach.

Magnetic Micro-Driller System for Nasolacrimal Duct Recanalization

Primary acquired nasolacrimal duct obstruction (PANDO) is the commonest cause of obstructive tearing and can lead to infections including dacryocystitis, cellulitis and postoperative endophthalmitis. External or endoscopic dacryocystorhinostomy (DCR) is the current standard treatment of PANDO. However, DCR is technically challenging, invasive, and not without risks, especially epistaxis and recurrent obstruction. We designed and constructed a magnetic micro-driller system for nasolacrimal duct (NLD) recanalization as a minimally invasive alternative to conventional treatment. The drilling microrobot consists of a 3D printed helical tip and a permanent magnet. A magnetic actuation system with three electromagnets is developed to well accommodate the human anatomy and generate three-dimensional dynamic magnetic fields along the NLD. To navigate the microrobot safely inside the NLD, the micro-driller is integrated with a guidewire and a feed drive system to control its motion. Furthermore, a force sensor is installed on the proximal end of the guidewire to report the force signal, through which the state of the micro-driller can be detected and the control input can be decided. Our results show that the guide-wired microrobot can navigate through the NLD phantom and remove the blockage automatically. The proposed strategy provides a microrobot-assisted endoluminal solution for NLD recanalization with improved safety by introducing the magnetic manipulation and force control method.

Adaptive robust tracking RBF neural networks control for industrial robot minipulators based on backstepping

This present study proposes a design and the analysis of the novel adaptive robust neural networks (ARNNs) based on the backstepping control method for industrial robot manipulators (IRMs). In this research, the ARNNs controller has combined the advantages of Radial Basis Function neural network (RBFNN), the robust term, and adaptive backstepping control technique without the requirement of prior knowledge. The RBFNN is used in order to approximate the unknown function to deal with external disturbances and uncertain nonlinearities. In addition, the disturbance of system is compensated by the robust Sliding Mode Control (SMC). All the parameters of ARNNs are determined by the Lyapunov stability theorem, are tuned online by an adaptive training law. Therefore, the stability, robustness, and desired tracking of the performance of ARNNs for IRMs are guaranteed.

Recurrent neural networks as kinematics estimator and controller for redundant manipulators subject to physical constraints

Redundant manipulators could be efficient tools in industrial production as a result of their dexterity. However, existing kinematic control methods for redundant manipulators have two main disadvantages. On one hand, model uncertainties or unknown kinematic parameters may degrade the performance of existing model-based control methods subject to joint limits. On the other hand, existing model-free control methods ignore the existence of joint limits although they do not need to know kinematic models of redundant manipulators. In this paper, a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task. Besides, a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulators. Then, a primal dual neural network, which is employed to solve the QP problem, and the GND model are integrated towards developing a model-free control method constrained by joint angle and velocity limits for redundant manipulators. The visual sensory feedback is fed to the two neural networks. The efficacy of the proposed control method is demonstrated by extensive simulations and experiments, and the merits of the proposed method are also substantiated by comparisons.