Control Method For Manipulators Research Articles

Adaptive impedance control method for manipulator based on radial basis function

Purpose In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments. Design/methodology/approach The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property. Findings Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%. Originality/value This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Time-Synchronized Convergence Control for n-DOF Robotic Manipulators with System Uncertainties.

A time-synchronized (TS) convergence control method for robotic manipulators is proposed. Adversely to finite-time control, a notion of time-synchronization convergence is introduced based on the ratio persistence property, which can ensure that all system components converge simultaneously in a finite time. Firstly, a robust disturbance observer is constructed to be compatible with the time-synchronized control framework and precisely estimate system uncertainties. Furthermore, we design a (finite) time-synchronized controller to ensure that all states of the robotic manipulator simultaneously converge to an equilibrium point, irrespective of initial conditions. Stability analysis shows the feasibility of the proposed TS control method. At last, simulations are performed with a two-link rehabilitation robotic system, and the comparison results indicate its superiority.

Event-triggered learning-based robust tracking control for robotic manipulators with uncertain dynamics and non-zero equilibrium

This paper presents a novel optimal tracking control method for robotic manipulators (RMs) with system uncertainties and non-zero equilibrium points. The existing tracking control methods typically require the precise system dynamics and assume the existence of a zero equilibrium point, which may not always be true in practice. To overcome this issue, a new cost function with respect to the first derivative of system states and a known upper bound on the system’s mismatched disturbances is designed, and then an optimal control problem of RMs is formulated. Furthermore, to solve this optimization, an event-triggered critic learning control framework is proposed to estimate the solution of Hamilton–Jacobi–Bellman equation, reducing the computational burden. In the critic learning control designs, novel weight updating laws related to system stability information are proposed to train the critic neural networks, which eliminates the initial stabilization requirement. Theoretical analysis shows that the stability of the closed-loop system in the sense of uniform ultimate boundedness is ensured. It should be emphasized that the proposed control method offers a simplification of the tracking controller design process and removes the restriction on system dynamics. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Abstract Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton–Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

Interval uncertainty-oriented impedance force control for space manipulator with time-dependent reliability

For specific missions of spacecraft including on-orbit assembly, docking, grasp, etc., the presence of uncertainties leads to difficulties in the analysis of impedance force control systems and evaluations of safety for space manipulators. Therefore, in this study, a novel interval uncertainty-oriented impedance force control method for space manipulators with time-dependent reliability was established. The proposed method can accurately and rapidly characterize uncertainties. Additionally, it can reflect the reliability of a control system based on an overall time history. Once the uncertainties of the dynamic models and state responses are known, their uncertainty bounds can be obtained using an established non-probabilistic propagation method with high accuracy. An interval process model and the first passage theory are employed in the non-probabilistic time-dependent model. A joint reliability index is established based on the single reliability indexes, which can more efficiently evaluate the control system reliability than the individual indexes. A numerical example related to space manipulator is used to study the developed impedance force control method and verify the effectiveness of the method. The results from the response curves reveal validity of the proposed method, while the reduction in time proves efficiency of the method.

An intelligent control method for a flexible-link manipulator with uncertain backlash and model uncertainty

This study examines a new intelligent control method for a single-link flexible manipulator that addresses backlash and model uncertainty. First, a smooth backlash inverse model is constructed to mitigate backlash nonlinearity. Subsequently, a ‘disturbance-like’ term is formulated to recharacterize the coupled term composed of external disturbances and model uncertainty. A new adaptive controller is proposed to compensate for the unknown ‘disturbance-like’ term. Using the proposed control method, the stability of the system is evaluated using the direct Lyapunov theory, ensuring uniform ultimate boundedness. Finally, numerical simulations and experiments are conducted using the Quanser platform. The numerical simulation and experimental results show that the proposed control can ensure a faster convergence rate and effectively reduce actuator input chattering.

Fractional order adaptive sliding mode control method of manipulator

A new controller (NAFOFTSMC) is designed and successfully applied to the manipulator system. Compared with traditional integer-order sliding mode control methods (IOSMC), the fractional order sliding mode control method (FOSMC) adopted additional degrees of freedom and, to some extent, weakens the drawback of sliding mode control chattering problem. Fractional-order adaptive rate compensation is utilized to compensate for the total disturbance set of the system. The new sliding surface reduces the derivative signals that are difficult to obtain due to external interference and other factors in the robotic arm system. It also reduces the unknown parameters for adjustment, increases the proportion term, and thus enables the system to have higher control accuracy and shorter adjustment time. While designing the controller, the stability is proved at the end of the fourth section. At the same time, this control method was compared and analyzed with IOSMC, simple FOSMC, and a Pre-improved controller (AFOFTSMC).

Adaptive neural network backstepping control method for aerial manipulator based on coupling disturbance compensation

The aerial manipulator, designed for complex aerial tasks, encounters multifaceted operational environments influenced by various internal and external disturbances. This paper introduces an adaptive neural network backstepping control technique fortified with coupling disturbance compensation to enhance the resilience of the aerial manipulator against these disturbances. Firstly, we propose a cutting-edge coupling disturbance feedforward compensator based on variable inertia parameters, which offers precise and prompt compensation for significant internal coupling disturbances without needing external sensors or alternative disturbance estimation techniques. Subsequently, radial basis function neural networks with an online adaptive weight updating mechanism are designed to estimate and counteract lumped disturbances stemming from unmodeled dynamics, uncertainties, and external factors in real-time. Utilizing the Lyapunov stability criteria, we validate that the aerial manipulator can reliably track desired trajectories under our proposed controller. Experimental results and simulations further underscore the effectiveness and superiority of our control approach.

An obstacle avoidance trajectory control method for robot biomimetic manipulators based on machine vision

An obstacle avoidance trajectory control method for robot biomimetic manipulators based on machine vision

Hysteresis Compensation-Based Intelligent Control for Pneumatic Artificial Muscle-Driven Humanoid Robot Manipulators With Experiments Verification

Pneumatic artificial muscles (PAMs), as a kind of soft actuators, can overcome compliance limitations of traditional rigid actuators to improve the adaptability of robots. However, some inherent strong nonlinearities and time-varying properties of PAMs, e.g., complex hysteresis and creep, may lead to a lot of control problems. In addition, PAM-driven systems are also faced with input constraints (e.g., deadzones, saturations, and unidirectional inputs), unknown/unmodeled dynamics and external disturbances, which badly degrade the control performance and even cause accidents. Therefore, this paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> hysteresis compensation-based immersion and invariance (I&I) adaptive fuzzy control method for PAM-driven humanoid robot manipulators, which can approximate the unknown functions and estimate the unknown parameters. To our knowledge, this is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> method for PAM-driven systems that compensates for system nonlinearities ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only</i> complex hysteresis, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">but also</i> input deadzones) by utilizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prior</i> information in inverse hysteresis models, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> estimates the unknown functions and parameters by designing a fuzzy update law and a parameter update law based on I&I methodology, respectively, which increases the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control frequency</i> of systems and improves tracking performance during high-speed motions. Finally, we apply the proposed approach on a self-built PAM-driven humanoid robot manipulator to validate its effectiveness and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Faced with the practical requirements of robots that interact closely with humans, improving the adaptability and compliance of robots by utilizing soft actuators, such as PAMs, can satisfy current demands. Moreover, PAMs also have many expective characteristics (e.g., high power density, light material, low costs, clean power, etc.), which makes PAMs occupy an important status in the field of soft robotics. However, unknown parameters/structures, strong nonlinearities, and input constraints, may badly degrade the control performance of PAMs. Based on the above characteristics, this paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> hysteresis compensation-based adaptive fuzzy controller for PAM-driven humanoid robot manipulators, which realizes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accurate tracking</i> control during high-speed motions by using inverse hysteresis models to compensate for strong nonlinearities in PAMs, and a fuzzy update law and a parameter update law based on I&I methodology are utilized to estimate unknown parameters/structures. In addition, the proposed controller can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> compensate for input deadzones by utilizing the hysteresis information, which improves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control frequency</i> of the manipulators, and can rapidly suppress tracking errors. Experimental results are provided to validate the effectiveness of the presented method. In the future, more effective compensation methods, such as rate-dependent hysteresis models, will further be carried out to compensate for system nonlinearities.

An obstacle avoidance trajectory control method for robot biomimetic manipulators based on machine vision

An obstacle avoidance trajectory control method for robot biomimetic manipulators based on machine vision

Metaheuristic adaptive control based on polynomial regression and differential evolution for robotic manipulators

Adaptive control of systems consists of the online adjustment of their control parameters to deal with parametric uncertainties and disturbances. Indirect Adaptive Control (IAC) of electromechanical systems and robotic manipulators based on metaheuristic optimization has been shown to be competitive compared to other classical and advanced control schemes. In this approach, the identification of system model parameters is stated as an optimization problem and then solved online by metaheuristics. Subsequently, the identified model is used in a second stage of online metaheuristic optimization to predict the system behavior and tune its control parameters. Therefore, this approach can be computationally expensive and unaffordable for systems with more complex dynamics whose models are just as complex. Due to the above, this work proposes an indirect adaptive control method for robotic manipulators based on differential evolution (DE) optimization, which aims to reduce the computational cost of the online identification stage by using Polynomial Regressors (PR) whose adjustment can be performed through a closed-form solution. The proposed method is tested in simulation for a 2 d.o.f. robotic manipulator and the results are compared against the original indirect adaptive control approach based on metaheuristic optimization and also a controller optimized offline. The post-hoc pair-wise Friedman tests confirm that the proposed approach provides competitive results compared to the original approach, with significant savings in computational resources.

Active 6 DoF Force/Torque Control Based on Dynamic Jacobian for Free-Floating Space Manipulator

Abstract In-orbit capture of a non-cooperative satellite will be a major challenge in the proposed servicing and active debris removal missions. The contact forces between the manipulator end-effector and the elements of the target object will occur in the grasping phase. In this paper, an active 6 Degrees of Freedom (DoF) force/torque control method for manipulator mounted on a free-floating servicing satellite is proposed. The main aim of the presented method is to balance the relation between end-effector position and force along each direction in the Cartesian space. The control law is based on the Dynamic Jacobian, which takes into account the influence of the manipulator motion on the state of the servicing satellite. The proposed approach is validated in numerical simulations with a simplified model of contact. Comparison with the classical Cartesian control shows that the active 6 DoF force/torque control method allows to obtain better positioning accuracy of the end-effector and lower control torques in manipulator joints in the presence of external forces.

Safe and compliant control of redundant robots using superimposition of passive task-space controllers

Animals are capable of robust and reliable control in unstructured environments, where they effortlessly overcome the uncertainty of interaction and are capable of exploiting singularities. These conditions are a well-known challenge for robots due to the limitations of projected dynamics, which requires accurate modelling and is susceptible to singularities. This work proposes a compliant passive control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy without requiring any knowledge of the robot dynamics. The proposed control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimisation is used). This method leverages and introduces a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. The experimental results demonstrate that the proposed method achieves sub-centimetre tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information.

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. PI sliding mode control method has the advantages of solving system deviation problem, eliminating steady-state error, strong adaptability, good robustness, sliding mode design and easy implementation. 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.

Trajectory planning and control of large robotic excavators based on inclination-displacement mapping

This paper proposes a trajectory planning and control method for heavy-duty manipulators based on inclination-displacement mapping. In this method, inclination sensors are employed to detect the excavator's orientation. By establishing a mapping relationship between joint inclination and cylinder displacement, the cylinder displacement is indirectly derived. Subsequently, considering the typical excavation operation process, the spatial trajectory of the excavator bucket is planned. The displacement of the driving cylinder is determined using kinematic inverse solutions and cubic polynomials. The cylinder displacement is then controlled using a position-velocity (PV) strategy. This method is tested on a 95-ton large excavator, and the results demonstrate several advantages, including easy installation, low maintenance requirements, smooth trajectory planning, and high tracking accuracy. These qualities make it suitable for the demands of intelligent operations in large excavators. Thus, this paper offers valuable insights for the intelligent transformation of heavy excavators.

Research and analysis of manipulator control method based on deep learning

Aiming at the stability and accuracy of grasping objects, this paper studies the adaptive neural network control and learning of manipulator control system with unknown system dynamics. First, a stable adaptive neural network (NN) controller is designed, and the unknown closed-loop system dynamics of the manipulator is approximated by using radial basis function (RBF) neural network. The LSTM long and short memory algorithm is introduced, and the RBF based hybrid neural network model is constructed. The LSTM algorithm is used to design the input gate, forgetting gate and output gate structures to suppress the gradient expansion problem during coordinate data training, and accurate trajectory correction instructions are given. Lyapunov stability theorem is used to analyze stability. Partial persistent excitation (PE) conditions of some internal signals in the closed-loop system are satisfied in the control process of tracking the circular reference trajectory. Under the PE condition, the proposed adaptive NN controller can accurately identify the dynamic uncertainties of the manipulator in the stability control process. Then, a new NN learning control method is proposed, which effectively uses the knowledge learned without re adapting to the unknown manipulator control system dynamics to achieve closed-loop stability and improve control performance. The effectiveness of the proposed method is studied through simulation.Keyboard

High-Precision and Modular Decomposition Control for Large Hydraulic Manipulators

It is difficult to achieve a high-precision motion control in hydraulic manipulators due to their structural redundancy, strong coupling of closed-chain structures, and flow–pressure coupling. In this paper, a high-precision motion control method for hydraulic manipulators is proposed based on the traditional virtual decomposition control (VDC). The method proposed avoids an excessive virtual decomposition of the hydraulic manipulator and requires fewer model parameters than the traditional VDC. Further, the control precision improved by combining an adaptive real-time update of the inertial parameters. Compared with MBC, the proposed control method improved the motion accuracy of the hydraulic manipulator by more than 40% and 20% under elliptical and triangular trajectories. The simulation results showed that the proposed control method reduced the maximum position errors in Cartesian space by 90.4%, 86.8%, 23.6%, and 44.3% compared with PID and model-based control (MBC) in the absence of disturbances. The maximum position error in Cartesian space was reduced by 76.5% compared with that of MBC in a simulation with external disturbances. It can be seen from all the simulation results that with the proposed control method, the position error of the manipulator was less than 50 mm. The proposed control method effectively improved the motion precision of the examined hydraulic manipulator.

A Model-Free-Based Control Method for Robot Manipulators: Achieving Prescribed Performance and Ensuring Fixed Time Stability

This paper addresses three significant challenges in controlling robot manipulators: improving response time, minimizing steady-state errors and chattering, and enhancing controller robustness. It also focuses on eliminating the need for computing the robot’s dynamic model and unknown functions, as well as achieving global fixed-time convergence and the prescribed performance for the control system. To achieve these objectives, a fixed-time sliding mode function is designed, which uses transformation errors to achieve prescribed control performance, with adjustments made to the maximum overshoot, convergence time, and tracking errors to keep them within predefined bounds. Additionally, a radial basis function neural network (RBFNN) is used to eliminate the need for knowledge of the robot’s dynamical properties and uncertain terms, which also reduces negative chattering. Finally, a novel fixed-time terminal sliding mode control (TSMC) algorithm is developed for robot manipulators without using their dynamical model. The fixed-time stability of the control system is thoroughly demonstrated by applying Lyapunov criteria and conducting simulations on a robot manipulator to showcase its effectiveness.

Reinforcement Learning-Based Fixed-Time Trajectory Tracking Control for Uncertain Robotic Manipulators With Input Saturation.

A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed RL control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor NN is used to generate the control strategy and the critic NN is used to evaluate the execution cost. A new nonsingular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear antiwindup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on the Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proven. Simulation and experimental results show the effectiveness and superiority of the proposed control law.