Motion Control Of Robot Manipulators Research Articles

Optimal Fully Actuated System Approach-Based Trajectory Tracking Control for Robot Manipulators.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

A hybrid HS-LS conjugate gradient algorithm for unconstrained optimization with applications in motion control and image recovery

This article presents a new hybrid conjugate gradient (CG) algorithm for solving unconstrained optimization problem. The search direction is defined as a combination of Hestenes–Stiefel (HS) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) quasi-Newton direction. In addition, the search direction is descent and bounded. The global convergence of the algorithm is obtained under the Wolfe-type and Armijo-type line searches. Numerical experiments on some benchmark test problems is carried out to depict the efficiency and robustness of the hybrid algorithm. Furthermore, a practical application of the algorithm in motion control of robot manipulator and image restoration is provided.

Implementation and Analysis of Motion Control Techniques on an Industrial 7 DOF Robotic Manipulator

With the quest for modernization in robotics motion control of robot manipulators is an area of active research. In motion control, inverse trajectory application is the most adopted solution which is further improved by considering the effect of external forces of the environment. The impact of external force involves dynamic modelling of the system and force control, impedance control, and admittance control techniques are adopted for the solution. This research implements the simulation and more precise visualization of the real-life manipulator under the rules of kinematics and dynamics on a 7 Degree of Freedom manipulator. The objective of this research is to implement motion control techniques: Forward Kinematics, Inverse kinematics, and Trajectory tracking using MATLAB Robotic System Toolbox and analyze the results obtained. These simulations can an effective tools for tuning the motion of robotics by varying the control parameter in simulation and then implementing them in physical hardware.

PID Tuning and Stability Analysis of Hybrid Controller for Robotic Arm Using ZN, PSO, ACO, and GA

This paper aims to tune and implement the Hybrid controller for the motion control of robot manipulators using different soft computing techniques. The hybrid controller is the combination of the PID (Proportional, Integral, and Derivative) and overwhelming controller. The different soft computing techniques such as Ziegler-Nichols (ZN), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) are utilized to tune the parameters of the PID. The simulation environment MATLAB has been used to obtain the results for different soft computing techniques, which are compared to obtain the best-tuned parameters. The results of the comparison have indicated that the GA performs exceptionally better than other optimization techniques. The Routh Hurwitz criterion is also used to analyze and validate the stability and efficacy of the proposed work. The tuned PID parameters are applied to a single-arm robot manipulator, which is interfaced by MATLAB Simulink. The results obtained through simulation have revealed that the proposed method is superior to traditional methods.

An intelligent sliding mode controller of robotic manipulators with output constraints and high‐level adaptation

AbstractFast transient responses and excellent steady‐state control performances with flexibility in control operation are core motivations to promote a vast of research in the robotic control sector nowadays. As a sequence, in this paper, we present a learning nonlinear controller for motion control of robotic manipulators with output constraints. The constrained control objectives are first transformed to new free variables using nonlinear synthetization profiles. Boundedness of the main control objectives within feasible ranges of the physical outputs is next strictly guaranteed under a simple sliding‐mode control policy. To improve the control performance, dominant terms of the system dynamics are learnt by a neural network with a nonlinear adaptation law. The systematic deviation remaining is then estimated by an adaptive disturbance observer. A novel high‐level adaptation rule is designed to maximize the learning ability and the control quality. Both an asymptotic control performance and estimation effectiveness are theoretically thoroughly proven by Lyapunov‐based analyses. Feasibility of the overall system is carefully investigated by intensive comparative simulations.

High-precision time synchronization chip design for industrial sensor and actuator network

Mission-critical (MC) sensor and actuator networks (SANs) are being applied widely for multiaxis motion control of robotic manipulators , particularly for performing MC tasks in smart manufacturing. One of the most challenging tasks in MC-SANs is the development of a precision time synchronization (PTS) protocol. A conventional precision time system uses the IEEE 1588 v2 standard [1] ; however, in MC-SANs, many factors need to be considered when designing an IoT computing system, such as robustness, cost-effectiveness, interoperability and scalability. This study proposes a high-precision time synchronization protocol based on Modbus sensors and actuator networks for an industrial fieldbus network. The PTS protocol design and hardware synthesis are conducted with a consistent and systematic design methodology. GRAFCET is used to model the individual functional modules and hierarchical behavior of the system. The behavior of each module is represented as a sequential–concurrent hybrid discrete event system . We apply high-level synthesis rules to design PTS hardware for a complex PTS system based on the VHSIC hardware description language (VHDL). The PTS hardware module has an embedded PTS algorithm with a proportional error correction model, and the time synchronization accuracy of the precision time protocol (PTP) chip is up to 42 ns. Experimental results demonstrate that our high-performance PTS controller chip can satisfy the requirements of multiaxis motion control and that it is more efficient than other protocols and products designed for similar industrial manufacturing applications.

Real-time motion control of robotic manipulators for safe human–robot coexistence

This paper introduces a computationally efficient control scheme for safe human–robot interaction. The method relies on the Explicit Reference Governor (ERG) formalism to enforce input and state constraints in real-time, thus ensuring that the robot can safely operate in close proximity to humans. The resulting constrained control method can steer the robot arm to the desired end-effector pose (or a steady-state admissible approximation thereof) in the presence of actuator saturation, limited joint ranges, speed limits, static obstacles, and humans. The effectiveness of the proposed solution is supported by theoretical results and numerous experimental validations on the Franka Emika Panda robotic manipulator , a commercially available collaborative 7-DOF robot arm. • Real-time predictive constrained control with Explicit Reference Governor. • Robotic arm avoids actuator saturations, limited joint and velocity constraints. • Guaranteed robot collision avoidance in static obstacle environments. • Empirical robot collision avoidance in highly dynamic environments. • Safe and performant human–robot coexistence with Franka Emika Panda.

Actuation and Motion Control of Flexible Robots: Small Deformation Problem

AbstractThis paper introduces a new computational approach for the articulated joint/deformation actuation and motion control of robot manipulators with flexible components. Oscillations due to small deformations of relatively stiff robot components which cannot be ignored, are modeled in this study using the finite element (FE) floating frame of reference (FFR) formulation which employs two coupled sets of coordinates: the reference and elastic coordinates. The inverse dynamics, based on the FFR formulation, leads to driving forces associated with the deformation degrees of freedom. Because of the link flexibility, two approaches can be considered to determine the actuation forces required to achieve the desired motion trajectories. These two approaches are the partially constrained inverse dynamics (PCID) and the fully constrained inverse dynamics (FCID). The FCID approach, which will be considered in future investigations and allows for motion and shape control, can be used to achieve the desired motion trajectories and suppress undesirable oscillations. The new small-deformation PCID approach introduced in this study, on the other hand, allows for achieving the desired motion trajectories, determining systematically the actuation forces and moments associated with the robot joint and elastic degrees of freedom, and avoiding deteriorations in the vibration characteristics as measured by the differences between the inverse- and forward-dynamics solutions. A procedure for determining the actuation forces associated with the deformation degrees of freedom is proposed and is exemplified using piezoelectric actuators. The PCID solution is used to define a new set of algebraic equations that can be solved for the piezoelectric actuation voltages required to maintain the forward-dynamics oscillations within their inverse-dynamics limits. A planar two-link flexible-robot manipulator is presented to demonstrate the implementation of the joint/deformation actuation approach. The results obtained show deterioration in the robot precision if the deformation actuation is not considered.

A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems

In this article, we propose a hybrid conjugate gradient (CG) scheme for solving unconstrained optimization problem. The search direction is a combination of the Polak–Ribière–Polyak (PRP) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton scheme. Without the use of the line search, the search direction satisfies the descent condition and possesses the trust region property. The global convergence of the scheme for general functions under the Wolfe-type and Armijo-type line search is established. Numerical experiments are carried out on some benchmark test problems and the results show that the propose scheme is more efficient than other existing schemes. Finally, a practical application of the scheme in motion control of robot manipulator is also presented.

Deep regression of convolutional neural network applied to resolved acceleration control for a robot manipulator

This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

Two Improved Conjugate Gradient Methods with Application in Compressive Sensing and Motion Control

To solve the monotone equations with convex constraints, a novel multiparameterized conjugate gradient method (MPCGM) is designed and analyzed. This kind of conjugate gradient method is derivative-free and can be viewed as a modified version of the famous Fletcher–Reeves (FR) conjugate gradient method. Under approximate conditions, we show that the proposed method has global convergence property. Furthermore, we generalize the MPCGM to solve unconstrained optimization problem and offer another novel conjugate gradient method (NCGM), which satisfies the sufficient descent property without any line search. Global convergence of the NCGM is also proved. Finally, we report some numerical results to show the efficiency of two novel methods. Specifically, their practical applications in compressive sensing and motion control of robot manipulator are also investigated.

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.

Modified extreme learning machine based motion control of robotic manipulators

Precise trajectory tracking is a difficult task due to partially known and unknown dynamics and the disturbances present in the system. For improving the tracking performance of the robotic manipulator, this work proposes a novel PSO optimized kernel based Extreme Learning Machine (PSO-KELM) learning algorithm in which PSO is used to get the optimal values of the free kernel-parameters in KELM. The simulation results represent the good generalized performance and PSO-KELM outperforms the KELM and ELM based control techniques for the manipulator trajectory tracking. Comparative analysis of the proposed control schemes have been done with NN and SVM based controllers and various ELM based variants for the trajectory tracking problem in robotic manipulator.

Design of Fractional-Order Lag Network and Fractional-Order PI Controller for a Robotic Manipulator

Abstract Motion control of robotic manipulators is frequently realized by independent control of the DC motors actuating robot joints. Namely, nonlinearities, coupling between actuators and other complex dynamics are neglected if high gear ratios between the actuators and robot joints are considered. This paper proposes a fractional-order lag network or a fractional-order PI controller to control the position of the actuators shafts. The introduced fractional compensators are designed by using the symmetrical optimum principle and by parameters optimization or by frequency-domain loop shaping, respectively. Simulation results and frequency response show effectiveness and robustness of the approach.

Adaptive Model-Free Control With Nonsingular Terminal Sliding-Mode for Application to Robot Manipulators

An adaptive model-free control with nonsingular terminal sliding-mode (AMC-NTSM) is proposed for high precision motion control of robot manipulators. The proposed AMC-NTSM employs one-sample delayed measurements to cancel nonlinearities and uncertainties of manipulators and to subsequently obtain sufficiently simple models for easy control design. In order to maintain high gain controls even when the joint angles are close to the reference target values and accordingly achieve high precision and fast response control, a nonlinear sliding variable is also adopted instead of a linear one, asymptotically stabilizing controls by guaranteeing even a finite-time convergence. In addition, sliding variables are reflected on control inputs to support fast convergence while achieving uniform ultimate boundedness of tracking errors. The control gains of the proposed AMC-NTSM are adaptively adjusted over time according to the magnitude of the sliding variable. Such adaptive control gains become high for fast convergence or low for settling down to steady motion with better convergence precision, when necessary. The switching gains of the proposed AMC-NTSM are also adaptive to acceleration such that inherent time delay estimation (TDE) errors can be suppressed effectively regardless of their magnitudes. The simulation and experiment show that the proposed AMC-NTSM has good tracking performance.

Dynamic Model and Motion Control of a Robotic Manipulator

This paper presents the dynamic modeling and motion control of a three-link rotary robotic manipulator, also known as an RRR robot. The Kinect motion capture system by Microsoft is used in conjunction with the manipulator. A camera is used to capture the motion of a user’s arm and tracks certain angles made by parts of the arm. We consider a pinhole camera model to generate reference angles in our simulations. These desired angles are fed into the controller and are used by the RRR robot in an effort to copy the movement of the user. A proportional-derivative (PD) controller is developed and applied to the manipulator for improved trajectory tracking. The RRR robot is dynamically modeled and the results of the proposed control strategy demonstrate good trajectory following. Researchers can implement this system as a relatively inexpensive starting point for their activities.

Fractional-order iterative learning control with initial state learning design

In this paper, we present a fractional-order iterative learning control (ILC) framework with initial state learning for the tracking problems of linear time-varying systems. Both open-loop and closed-loop $$D^{\alpha }$$ -type iterative learning updating laws are considered. To design ILC scheme for practical control systems, initialization assumption, i.e., the system initial states should be the same at each repetition, is removed by using an initial state learning scheme together with the $$D^{\alpha }$$ -type ILC updating law. Sufficient conditions of convergence to the desired trajectory is theoretically proved for a linear time-varying mechanical system. Numerical simulation results are presented to illustrate effectiveness of the control strategies. Moreover, we also show that the proposed learning scheme can be applied to the motion control of robot manipulators under some reasonable conditions.

Motion Control of Five Bar Linkage Manipulator Using Conventional Controllers Under Uncertain Conditions

Robot trajectory tracking has been the core functioning unit in the modern industrial environment wherein the accuracy in the motion control of robotic manipulators is the main area of research. Based on the fact that the working of these automatic robotic machines is highly influenced by the disturbances, this paper constitutes various conventional controllers for the motion control of five bar linkage manipulator. To verify the performance of proposed conventional controllers, these are made to work with two different trajectories. Common disturbances like payload & friction has been incorporated in the five bar linkage manipulator system for validation purpose. Simulation results prove that the performance of SMC based controller is better when compared with other conventional controllers.

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.

Fuzzified PSO-SVM controller for motion control of robotic manipulator

The tremendous advantages of SVM make it one of the most emerging and proving techniques of estimating nonlinear quantities of the era. SVM performance can be enhanced by selecting optimal value of few of its kernel constant parameters for which a global optimisation technique, naming PSO can be successfully employed. For performance up gradation of PSO, fuzzy logic is used to adjust adaptively the inertia weights (w) and acceleration coefficients (c1 and c2) of PSO. This fuzzified PSO is used for obtaining optimal value of SVM parameters. Such hybrid of fuzzy logic, PSO and SVM proposed controller has not been found in literature yet. For validation purpose, the proposed controller has been applied for motion control of a robotic manipulator. Simulation results proves that the proposed controller remarkably outperforms the basic SVM and PSO-SVM controllers in terms of tracking performance and reduced 2-norm of control input. For validity, performance of various controllers has been checked in the presence of normally affecting uncertainties (payload changes and friction) of the manipulator system by incorporating the actuator dynamics in it.