Manipulators In Task Space Research Articles

Adaptive control of BLDC driven robot manipulators in task space

AbstractIn this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.

Task space control of the robot manipulators with adaptive fuzzy global fast terminal sliding mode control in presence of dynamic and kinematic uncertainties

SummaryBasic features such as convergence time and speed, number‐action control coefficients, free chattering, and proof of stability are significant in the design process and sliding mode control (SMC) efficiency. In this article, we propose an adaptive fuzzy global fast terminal SMC (AFGFTSMC) to handle the mentioned features in the task space control of the robot manipulator in the presence of dynamic and kinematic uncertainties. First, perturbed joint space dynamic equations of the system are transferred to task space, and a broad range of uncertainties are considered there. Then, a global fast terminal SMC (GFTSMC) is proposed for robot manipulators in task space, in which a flexible sliding surface improves the convergence time. Next, to have an intelligent adjustment of the sliding surface coefficients, which leads to a much faster convergence rate, a fuzzy approximator with just seven fuzzy rules is presented. In the following, to access the boundaries of the existing uncertainties, an adaptive fuzzy approximator is proposed, which has five fuzzy rules and only one adaptive law, increases the system's robustness, and eliminates the effect of chattering. Mathematical proof shows that the task space closed‐loop control system under the proposed AFGFTSMC and in the presence of dynamic and kinematic uncertainties has a finite‐time global asymptotic stability. The theoretical evidence and simulation results, which are conducted on a 2‐link robot manipulator, confirm the good efficiency of the proposed controller.

A Motion Planning Method for Robots Based on DMPs and Modified Obstacle-Avoiding Algorithm

This paper addresses the motion planning of the manipulator in task space. To improve the overall trajectory performance, a special motion planning method based on DMPs (Dynamic Movement Primitives) and the modified obstacle-avoiding algorithm is proposed. The proposed method solves the problems of trajectory jitter and inability to avoid obstacles in some scenarios, which are faced by the scheme of steering angle. Besides, it improves the retention of teaching intentions, reduces the loss of free space, and helps the system adapt to the dynamic environment. At the theoretical level, the convergence of the target state has been proved using Lyapunov stability theory-based analysis. The availability of the proposed method is validated and analyzed by performing a series of numerical simulations and Baxter robot experiments. The results indicate that the proposed method can provide reliable solutions for motion planning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —From the perspective of demonstration learning, this paper aims to elevate the motion planning of the manipulator in task space, especially in improving the obstacle avoidance performance. The existing DMPs-based motion planning algorithm uses the scheme of steering angle to achieve obstacle avoidance, and the obstacle is regarded as a mesh of points on the boundary. The obstacle-avoiding performance is limited. How to combine the obstacle-avoiding algorithm with the DMPs-based motion planning algorithm more effectively, so as to simultaneously achieve retaining teaching intentions as much as possible, still faces challenges. This paper designs a modified obstacle-avoiding algorithm, which solves the problems of trajectory jitter and inability to avoid obstacles in some circumstances, and improves the retention of teaching intentions. The modified obstacle-avoiding algorithm is also applied to the dynamic environment. The proposed method satisfies Lyapunov stability theory-based analysis, and the experiments on Baxter robot verify the feasibility.

A Finite-time Adaptive Fault Tolerant Control Method for a Robotic Manipulator in Task-space With Dead Zone, and Actuator Faults

A Finite-time Adaptive Fault Tolerant Control Method for a Robotic Manipulator in Task-space With Dead Zone, and Actuator Faults

Adaptive Neural-Network Control of MIMO Nonaffine Nonlinear Systems With Asymmetric Time-Varying State Constraints.

In this paper, a novel robust adaptive barrier Lyapunov function (BLF)-based backstepping controller has been proposed for a class of interconnected, multi-input-multi-output (MIMO) unknown nonaffine nonlinear systems with asymmetric time-varying (ATV) state constraints. The design involves a neural-network-based online approximator to cope with uncertain dynamics of the system. To tune its weights, a novel adaptive law is proposed based on the Hadamard product. A theorem has also been proposed to have the bounds on virtual control signals beforehand. This theorem eliminates the need for tedious offline computation for the feasibility condition on the virtual controller in BLF-based controller design. To overcome the problem of unknown control gain in the nonaffine system, Nussbaum gain has been used during the design. A simulation study on the robot manipulator in task space has been performed to illustrate the effectiveness of the proposed methodology.

Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space

End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this article. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed-loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented.

Model Predictive Control With Integral Compensation for Motion Control of Robot Manipulator in Joint and Task Spaces

The motion control of robot manipulators is a crucial problem concerning automatically controlled robots. In this work, the model predictive control method with an integral compensation (MPC-I), which compensates for the matched uncertainties due to unmodeled dynamics, is proposed to solve the trajectory tracking problem of robot manipulators in joint and task spaces. First, this paper decouples the joint variables of the robot manipulator using a computed torque control method. The MPC-I method is, thereafter, derived to realize the motion control of the robot manipulators in joint space. To realize the motion control of the robot manipulator in task space, the task space is, thereafter, converted into the joint space, in which the MPC-I method is executed, afterward, to control the robot. Furthermore, an MPC-I variation, in which the inverse kinematics is calculated indirectly, is proposed to achieve the motion control in task space. The novelty of this paper is to propose the MPC-I method and the method of converting task space to joint space with indirect inverse kinematics calculation. The former is suitable for the dynamic control of the robot manipulators in the joint space, and the latter can extend the MPC-I method to dynamic control in task space. To evaluate the performance of the proposed control method, motion control simulations are performed in the task and joint spaces, respectively. Simulation results and comparisons verify the effectiveness of the proposed control approach for the dynamic control of the UR5 robot manipulator.

Singularity Avoidance Control of a Non-Holonomic Mobile Manipulator for Intuitive Hand Guidance

Mobile manipulators are robot systems capable of combining logistics and manipulation tasks. They thus fulfill an important prerequisite for the integration into flexible manufacturing systems. Another essential feature required for modern production facilities is a user-friendly and intuitive human-machine interaction. In this work the goal of code-less programming is addressed and an intuitive and safe approach to physically interact with such robot systems is derived. We present a natural approach for hand guiding a sensitive mobile manipulator in task space using a force torque sensor that is mount close to the end effector. The proposed control structure is capable of handling the kinematic redundancies of the system and avoid singular arm configurations by means of haptic feedback to the user. A detailed analysis of all possible singularities of the UR robot family is given and the functionality of the controller design is shown with laboratory experiments on our mobile manipulator.

Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

A In this paper, an adaptive direct fuzzy control system is presented to control the robot manipulator in task space. It is assumed that robot system has unknown dynamic and uncertain kinematic. The control system and adaption mechanism are firstly designed for joint space tracking. Then by using inverse Jacobian strategy, it is generalized for task space. After that, to overcome the problem of Jacobian matrix uncertainty, an improved adaptive control system is designed. All the design steps are illustrated by simulations.

Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic

A In this paper, an adaptive direct fuzzy control system is presented to control the robot manipulator in task space. It is assumed that robot system has unknown dynamic and uncertain kinematic. The control system and adaption mechanism are firstly designed for joint space tracking. Then by using inverse Jacobian strategy, it is generalized for task space. After that, to overcome the problem of Jacobian matrix uncertainty, an improved adaptive control system is designed. All the design steps are illustrated by simulations.

Motion Control of a Nonholonomic Mobile Manipulator in Task Space

AbstractIn this paper, the motion control of a mobile manipulator subjected to nonholonomic constraints is investigated. The control objective is to design a computed‐torque controller based on the coupled dynamics of the mobile manipulator. The proposed controller achieves the capability of simultaneous tracking of a reference velocity for the mobile base and a reference trajectory for the end‐effector. The aforementioned reference velocity and trajectory are defined in the task space, such task setting imitates the actual working conditions of a mobile manipulator and thus makes the control problem practical. To solve this tracking problem, a steering velocity is firstly designed based on the first‐order kinematic model of the nonholonomic mobile base via dynamic feedback linearization. The main merit of the proposed steering velocity design is that it directly utilizes the reference velocity set in the task space without requiring the knowledge of a reference orientation. A torque controller is subsequently developed based on a proposed Lyapunov function which explicitly considers the coupled dynamics of the mobile manipulator to ensure the mobile base and end‐effector track the reference velocity and trajectory respectively. This proposed computed‐torque controller is able to realize asymptotic stability of both the base velocity tracking error and the end‐effector motion tracking error. Simulations are conducted to demonstrate the effectiveness of the proposed controller.

Learning Control of Robot Manipulators in Task Space

AbstractTwo important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end‐effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which “learns” the overall uncertainties in the robot manipulator dynamics. The stability of the closed‐loop system and asymptotic end‐effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in‐house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.

Learning dynamic models for open loop predictive control of soft robotic manipulators

The soft capabilities of biological appendages like the arms of Octopus vulgaris and elephants’ trunks have inspired roboticists to develop their robotic equivalents. Although there have been considerable efforts to replicate their morphology and behavior patterns, we are still lagging behind in replicating the dexterity and efficiency of these biological systems. This is mostly due to the lack of development and application of dynamic controllers on these robots which could exploit the morphological properties that a soft-bodied manipulator possesses. The complexity of these high-dimensional nonlinear systems has deterred the application of traditional model-based approaches. This paper provides a machine learning-based approach for the development of dynamic models for a soft robotic manipulator and a trajectory optimization method for predictive control of the manipulator in task space. To the best of our knowledge this is the first demonstration of a learned dynamic model and a derived task space controller for a soft robotic manipulator. The validation of the controller is carried out on an octopus-inspired soft manipulator simulation derived from a piecewise constant strain approximation and then experimentally on a pneumatically actuated soft manipulator. The results indicate that such an approach is promising for developing fast and accurate dynamic models for soft robotic manipulators while being applicable on a wide range of soft manipulators.

Fuzzy MTEJ controller with integrator for control of underactuated manipulators

Abstract This paper presents control of underactuated robot manipulators in task space utilizing novel algorithm called fuzzy modified transpose effective Jacobian (MTEJ) with integrator term which ensures tracking trajectory in task space with high-quality performance. Although non-model base MTEJ have been introduced before and evaluated for underactuated robots, this method has poor operation against non-linear factors in actuators and joints observed in practical tests like deadband, backlash or Coulomb damping. The contributions of this paper are in twofold. First, to introduce improved MTEJ algorithm with additional integrator term that is efficient to eliminate effects of mentioned factors or other source of steady state error (SSE). Second, using new fuzzy rules to manage its terms to stabilize system with better control properties, the controller is used to make the endeffector to both track a predefined trajectory or set on an exact point. Global stabilization of this control method is proved. Simulation and experimental results are offered which compared tracking performance of the improved fuzzy MTEJ with integrator to other methods for both tracking and point to point (P.T.P) control. Outcomes of these experiments reveal privileges of using fuzzy improved MTEJ in various areas like removing SSE with better control characteristics and low computational efforts.

An Adaptive Iterative Learning Control for Robot Manipulator in Task Space

In this paper, adaptive iterative learning control (AILC) of uncertain robot manipulators in task space is considered for trajectory tracking in an iterative operation mode. The control scheme incluces a PD controller with a gain switching technique plus a learning feedforward term, is exploited to predict the desired actuator torque. By using Lyapunov method, an adaptive iterative learning control scheme is presented for robotic system with both structured and unstructured uncertainty, and the overall stability of the closed-loop system in the iterative domain is established. The validity of the scheme is confirmed through a numerical simulation.

Task-Space Control of Robot Manipulators With Null-Space Compliance

In this paper, the problem of controlling a robot manipulator in task space, while guaranteeing a compliant behavior for the redundant degrees of freedom, is considered. This issue may arise in the case where the robot experiences an interaction on its body, especially in the presence of humans. The proposed approach guarantees correct task execution and compliance of the robot's body during intentional or accidental interaction in the null space of the main task, simultaneously. The asymptotic stability of the task-space error is ensured by using suitable observers to estimate and compensate the generalized forces acting on the task variables, without using joint torque measurements. Two different controller-observer algorithms are designed, and they are based on the task-space error and on the generalized momentum of the robot, respectively. The performance of the proposed algorithms is verified in experiments on a 7R lightweight robot arm.

Observer-based partial differential equation boundary control for a flexible two-link manipulator in task space

This study addresses the problem of trajectory control of a flexible two-link manipulator on the basis of the partial differential equation (PDE) dynamic model. One of the key contributions of this study is that a novel non-linear PDE observer is proposed to estimate distributed positions and velocities along flexible links, which cannot be achieved by the typical ordinary differential equation observer. In addition, the rigidity-flexibility coupling dynamics is decomposed using the singular perturbation approach, thus providing convenience for control design. Based on the proposed observer and the decoupled PDE model, a boundary control scheme is designed to regulate the end effector along reference trajectory in task space and suppress vibration simultaneously. The asymptotic stability of both the proposed observer and the control algorithm is validated by theoretical analysis and demonstrated by simulation results, respectively.

Control of robot manipulators in task-space under uncertainties using neural network

In this paper, neural network-based controller is designed for the tracking control of robot manipulators in task-space under uncertainties. Especially, this controller does not need prior information of the upper bound of the unstructured uncertainties. By adaptively estimating the upper bound using feedforward neural network, effects of unstructured uncertainties can be eliminated and asymptotic error convergence can be obtained for the closed-loop system. Simulation results are carried out for a two-link elbow robot manipulator to show the effectiveness of the control scheme.

Robust Backstepping Control of Robot Manipulator in Task Space with Uncertainties in Kinematics and Dynamics

This paper introduces a novel robust control approach for trajectory tracking of robot manipulator in task space. A new task space control scheme is proposed to overcome the uncertainties in robot dynamics and kinematics. A robust controller is designed based on Lyapunov method, using backstepping method. It is then proven that the closed loop system has global uniform ultimate boundedness stability. Modifications are given to derive a control law which is free of position and velocity of end-effector. The control approach is applied on a two-links elbow robot manipulator. The performance of proposed control laws are confirmed by simulations. Ill. 8, bibl. 13, tabl. 1 (in English; abstracts in English, Russian and Lithuanian).

Optimal configuration of dual-arm cam-lock robot based on task-space manipulability

SUMMARYIn this paper obtaining the optimal configuration of the dual-arm cam-lock (DACL) robot at a specific point is addressed. The objective is to optimize the applicable task-space force in a desired direction. The DACL robot is a reconfigurable manipulator formed by two parallel cooperative arms. The arms normally operate redundantly but when needed, they can lock into each other in certain joints in order to achieve a higher stiffness, while losing some degrees of freedom. Furthermore, the dynamics of the DACL robot is discussed and parametrically formulated. Considering the geometrical constraints at a given point in the robot's workspace, the optimum configuration for maximizing the cooperatively applicable force by dual arms is determined.