Control Of Robot Manipulators Research Articles

Adaptive Visual Control for Robotic Manipulators with Consideration of Rigid-Body Dynamics and Joint-Motor Dynamics

A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

A Novel Recursive Algorithm for the Implementation of Adaptive Robot Controllers

In this paper, a novel recursive and efficient algorithm for real-time implementation of the adaptive and passivity-based controllers in model-based control of robot manipulators is proposed. Many of the previous methods on these topics involve the computation of the regressor matrix explicitly or non-recursive computations, which remains as the main challenge in practical applications. The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previously proposed methods, presented in a unified mathematical framework suitable for understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in terms of real-time computational efficiency. Computational results show that the proposed Adaptive Newton-Euler Algorithm significantly reduces the computation time of the control law per cycle time in the implementation of adaptive control laws. In addition, using the dynamic simulation of an industrial robot with 6-DoF, trajectory tracking performances of the adaptive controllers are compared with those of non-adaptive control methods where dynamic parameters are assumed to be known.

Design and control of the Six-DOF robotic manipulator

This paper presents the building process and simulation of a robotic manipulator. Robotic manipulators are used in various scenarios, such as industry and agriculture. It would be of particular interest to learn how to systematically design and control a manipulator to follow a planned trajectory. Hence, a CAD model is developed in this paper. There is a rough finite element analysis for two cases in this study, which could easily find out how the product reacts to real-world forces and vibration. In order to solve real-world problems safely and effectively, this effort intends to build the mechanical design of the manipulator by SOLIDWORKS and simulate trajectory tracking capability using MATLAB.

CONCEPT OF APPLICATION OF CONTROL ALGORITHMS FOR MANIPULATION ROBOTS TO PERFORM COMPLEX TECHNOLOGICAL OPERATIONS IN INDUSTRY

The article is devoted to the study of control algorithms for manipulation robots to perform complex technological operations at enterprises in order to optimize working process time. In today’s business environment, in order to maximize efficiency of production management in highly competitive environment, enterprises need to constantly improve work process automating methods using robots and artificial intelligence. Authors of this article take an in-depth look at the real-world challenges that manipulation robots face in performing complex operations in industrial environment. The work discusses various approaches to control manipulation robots. These approaches include adaptive control method, program control method, sensor control method and collaborative control method. Application of artificial intelligence in the control of manipulation robots allows making decisions based on analyzing of large data amounts, learning from experience and adapting to the environment.

Event-triggered based predefined-time tracking control for robotic manipulators with state and input quantization

Event-triggered based predefined-time tracking control for robotic manipulators with state and input quantization

Neuroadaptive‐based fixed‐time control for robotic manipulators with uniform prescribed performance under unknown disturbance

AbstractAchieving faster convergence, smaller transient overshoots, and higher steady‐state tracking accuracy is essential to improve the efficiency, robustness, and applicability of robotic manipulators. This article introduces an innovative adaptive fixed‐time uniform prescribed performance controller for the manipulator facing model uncertainties and unknown disturbances. Initially, by designing a modified prescribed performance function inspired by variable superposition, this study redefines the unified prescribed performance control problem into a simplified parameter selection problem. This approach allows for the incorporation of varied performance metrics within a singular control scheme, addressing both transient and steady‐state performances concurrently without shifting control frameworks. Then, to alleviate computational demands, an adaptive neural network employing a single‐parameter weight update technique compensates for uncertainties of the manipulator dynamic model. Additionally, a disturbance observer is designed to mitigate the impact of non‐parametric disturbances. Moreover, integrating fixed‐time theory with the Lyapunov stability analysis method guarantees the convergence of all error signals to a near‐zero compact neighborhood at a fixed time. Finally, the advantages and comprehensive performance of the proposed method are confirmed by numerical simulations and real‐world experiments.

Partial Potential Energy Shaping Control of Torque-Driven Robot Manipulators in Joint Space

Partial Potential Energy Shaping Control of Torque-Driven Robot Manipulators in Joint Space

Adaptive finite-time sliding mode control of robot manipulators with possible actuator failures

Adaptive finite-time sliding mode control of robot manipulators with possible actuator failures

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.

Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

Single-network based adaptive dynamic programming optimal control for robotic manipulator with joint limitation under input saturation

Single-network based adaptive dynamic programming optimal control for robotic manipulator with joint limitation under input saturation

Point-to-point virtual model control of a flexible joint robotic manipulator using trajectories of motion

This paper considers the use of trajectories of motion as a means of addressing the problem of unwanted oscillations and improving steady-state accuracy in the point-to-point motion of a flexible joint robotic manipulator using a novel third-order Poynting–Thomson virtual model control. A differentially flat model of the manipulator was exploited for classical controller design. The trajectories of motion were generated through a Newton interpolation procedure. The conditions for motion from rest to rest were derived and used to generate the trajectories required to steer the manipulator from one static point to another. Simulation and laboratory experiments confirmed that with the use of the trajectories of motion, rest-to-rest displacement is achieved without the unwanted swings about the final position. A marginal improvement in trajectory tracking performance was also verified by comparing it with the classical controller and state feedback controller. Moreover, the new controller showed better external disturbance rejection than the classical flat controller.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

A mismatched composite disturbance observer‐based adaptive tracking controller for robotic manipulators

AbstractThe paper is devoted to the adaptive tracking control based on the mismatched disturbance observer for motor‐driven robotic manipulators. A novel mismatched disturbance observer is introduced, which does not make bounded derivative assumptions on mismatched disturbances in the robotic manipulator system. Additionally, a new adaptive tracking controller for a motor‐driven robotic manipulator based on the mismatched disturbance observer is designed, which guarantees that all closed‐loop signals are globally uniformly bounded and the tracking error is asymptotic stable. Finally, the simulation comparisons are carried out between the proposed and existing control strategies to demonstrate the superiority of the developed controller.

Distributed real-time control architecture for electrohydraulic humanoid robots

PurposeThis paper aims to develop an adaptable control architecture for electrohydraulic humanoid robots (HYDROïD) that emulate the functionality of the human nervous system. The developed control architecture overcomes the limitations of classical centralized and decentralized systems by distributing intelligence across controllers.Design/methodology/approachThe proposed solution is a distributed real-time control architecture with robot operating system (ROS). The joint controllers have the intelligence to make decisions, dominate their actuators and publish their state. The real-time capabilities are ensured in the master controller by using a Preempt-RT kernel beside open robot control software middleware to operate the real-time tasks and in the customized joint controllers by free real-time operating systems firmware. Systems can be either centralized, where all components are connected to a central unit or decentralized, where distributed units act as interfaces between the I/Os and the master controller when the master controller is without the ability to make decisions.FindingsThe proposed architecture establishes a versatile and adaptive control framework. It features a centralized hardware topology with a master PC and distributed joint controllers, while the software architecture adapts based on the task. It operates in a distributed manner for precise, force-independent motions and in a decentralized manner for tasks requiring compliance and force control. This design enables the examination of the sensorimotor loop at both low-level joint controllers and the high-level master controller.Originality/valueIt developed a control architecture emulating the functionality of the human nervous system. The experimental validations were performed on the HYDROïD. The results demonstrated 50% advancements in the update rate compared to other humanoids and 30% in the latency of the master processor and the control tasks.

Model adaptive reference tracking control for uncertain robotic manipulators with input disturbance

Model adaptive reference tracking control for uncertain robotic manipulators with input disturbance

Model free sliding mode control for serial robot manipulator: rigid and elastic joint robot

Model free sliding mode control for serial robot manipulator: rigid and elastic joint robot

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Gesture control is often used for the control of robotic manipulators. However conventional methods always focus on position control of robotic manipulators and use a fixed position to place gesture detection devices, which limits the flexibility to control and interact. This paper presents an interactive gesture control system based on Leap Motion Controller that can overcome these shortcomings. A coordinate transformation is performed between the position of the left palm detected by Leap Motion Controller and the tool center point (TCP) of the collaborative manipulator to obtain gesture data first. This gesture data is used to control the posture of a six-joint collaborative manipulator-Lite6. The position of gripper is controlled by grip level of the right palm. A controller is designed to manipulate a lift table on which Leap Motion Controller is placed to achieve adaptive ascending and descending movements to meet the needs of operators of different heights and arm spans. A modified Kalman filter is used to filter noise and smooth the signal captured by Leap Motion Controller. The experiments demonstrate that the system can operate stably and accurately control the position, posture, and gripper position of a collaborative manipulator in real time using human palms’ gestures.

Zeroing Neural Network With Coefficient Functions and Adjustable Parameters for Solving Time-Variant Sylvester Equation.

To solve the time-variant Sylvester equation, in 2013, Li et al. proposed the zeroing neural network with sign-bi-power function (ZNN-SBPF) model via constructing a nonlinear activation function. In this article, to further improve the convergence rate, the zeroing neural network with coefficient functions and adjustable parameters (ZNN-CFAP) model as a variation in zeroing neural network (ZNN) model is proposed. On the basis of the introduced coefficient functions, an appropriate ZNN-CFAP model can be chosen according to the error function. The high convergence rate of the ZNN-CFAP model can be achieved by choosing appropriate adjustable parameters. Moreover, the finite-time convergence property and convergence time upper bound of the ZNN-CFAP model are proved in theory. Computer simulations and numerical experiments are performed to illustrate the efficacy and validity of the ZNN-CFAP model in time-variant Sylvester equation solving. Comparative experiments among the ZNN-CFAP, ZNN-SBPF, and ZNN with linear function (ZNN-LF) models further substantiate the superiority of the ZNN-CFAP model in view of the convergence rate. Finally, the proposed ZNN-CFAP model is successfully applied to the tracking control of robot manipulator to verify its practicability.

Asymptotic consensus tracking control of robotic manipulators with disturbance and dead-zone via adaptive sliding mode control

This article aims at solving the difficulties of robotic manipulator systems with unknown disturbance and dead-zone in consensus trajectory tracking. For the purpose of accelerating the tracking process of leader and follower manipulators, a novel sliding mode control strategy is proposed. For compensating the influence of unknown lumped disturbance and control dead-zone, a novel adaptive law is designed for the construction of controller. With the help of adaptive sliding mode controller, based on the Lyapunov stability theory, the over-estimation is alleviated and the tracking errors converge to zero asymptotically. The simulations demonstrate the validity of the proposed strategy for consensus asymptotic trajectory tracking.