Stability and control aspects of flexible link robot manipulators during constrained motion tasks

The development of autonomous robots for agricultural applications includes motion planning, fruit picking, and collision avoidance with surrounding environments, and these become challenging tasks. For harvesting applications, robust control of the manipulator is needed for the effective motion of the robot. Several combinations of Proportional(P)- Integrative(I)- Derivative(D) controllers are modelled and a simulation study was performed for trajectory tracking of a redundant manipulator in virtual agricultural environments. The article presents a comprehensive study on kinematic modelling and dynamic control of redundant manipulator for fruit-picking applications in virtual environments. The collisions with surrounding environment were eliminated using ‘bounding box technique’. The joint variables are obtained by constructing Inverse Kinematics (IK) problem and are determined using a classical optimization technique. Different controllers are modelled in the ‘Simulink’ environment and are tuned to generate error-free trajectory tracking during harvesting. The task space locations (TSLs) are considered as via-points, and joint variables at each TSLs are obtained by Sequential Quadratic Programming (SQP) technique. Joint-level trajectories are generated using Quintic and B-spline polynomials. For effective trajectory tracking, torque variations are controlled using the PID and Feedforward (FF) controller. The dynamic simulations of the robot manipulator are performed in Simscape Multibody software. Results show that the during the trajectory tracking of the manipulator, the Feed-forward controller performs best with Quintic polynomial trajectory.

The present work addresses modeling and control issues pertaining to the positioning and orientating of rigid body payloads as they are being manipulated by flexible spherical robotic manipulators. A general approach, to systematically derive the equations of motion of the robotic manipulator, is used herein. The objective of the controller is to yield a desired rigid body response of the arm while damping out the transverse and torsional vibrations of the compliant link. Note that the control objective has to be achieved by solely relying on the existing joint actuators whose band-widths are far below the natural frequencies of the torsional modes. The current work demonstrates that, in spite of the physical limitations of the system, the controller can actively damp out the torsional vibrations by relying on the coupling terms between the torsional vibrations and the remaining degrees of freedom of the arm. Moreover, a gain scheduling procedure is introduced to continuously tune the controller to the natural frequencies of the flexible link whose length is varied by the prismatic joint. The digital simulation results demonstrate the capability of the “rigid and flexible motion controller (RFMC)” in drastically attenuating the transverse and torsional vibrations during point-to-point (PTP) maneuvers of the arm. Furthermore, the gain scheduling procedure is shown to significantly reduce the degradations in the RFMC performance that are brought about by having the flexible link connected to a prismatic joint. A limited experimental work has also been conducted to demonstrate the viability of the proposed approach.

This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking.

This paper presents the current state of the art in the adaptive control of single rigid robotic manipulators in the constrained motion tasks. A complete mathematical model of a single rigid robotic manipulator in contact with dynamic environment is presented. The basic approaches in deriving the environment model are given. The significance of the dynamic environment in the scope of the stability problem of the whole system robot-dynamic environment is emphasized. A classification of the adaptive contact control concepts in manipulation robotics is presented. The main characteristics of the most important adaptive strategies in constrained manipulation are given. The advantages and the drawbacks of the presented methods are emphasized. The paper covers results published a few years ago, as well as some recent trends in this field. One important result in the stability analysis of robotic manipulators in the constrained motion tasks is reported. Finally, some concluding remarks are given and possible future investigation trends in adaptive control of robotic manipulators are indicated.

In this paper, a dynamic system consisting of a robot manipulator and a target is analyzed. The target is considered in a general way as a dynamic subsystem having finite mass and moments of inertia (e.g., a rigid body or a second robot). The situation investigated is when the robot establishes interaction with the target in such a way that it intercepts and captures a reference element of the target. The analysis of target capture is divided into three phases in terms of time: the precapture, “free” motion (finite motion); the transition from free to constrained motion in the vicinity of interception and capture (impulsive motion); and the postcapture, constrained motion (finite motion). The greatest attention is paid to the analysis of the phase of transition, the impulsive motion, and dynamics of the system. Based on the use of impulsive constraints and the Jourdainian formulation of analytical dynamics, a novel approach is proposed for the dynamic modeling of target capture by a robot manipulator. The proposed approach is suitable to handle both finite and impulsive motions in a common analytical framework. Based on the dynamic model developed and using a geometric representation of the system’s dynamics, a detailed analysis and a performance evaluation framework are presented for the phase of transition. Both rigid and structurally flexible models of robots are considered. For the performance evaluation analyses, two main concepts are proposed and corresponding performance measures are derived. These tools may be used in the analysis, design, and control of time-varying robotic systems. The dynamic system of a three-link robot arm capturing a rigid body is used to illustrate the material presented.

SUMMARYThe study of inertial forces effects at high speeds in flexible parallel manipulators, which generate undesired deviations, is a challenging task due to the coupled and complicated equations of motion. A dynamic model of the Revolute Prismatic Revolute (RPR) planar manipulators (specifically 3-RPR, 2-RPR and 1-RPR) with flexible intermediate links is developed based on the assumed mode method. The flexible intermediate links are modeled as Euler-Bernoulli beams with fixed-free boundary conditions. Using the Lagrange multipliers, a generalized set of differential algebraic equations (DAEs) of motion is developed. In the simulations, the rigid body motion of the end-effector is constrained by some moving constraint equations while the vibrations of the flexible intermediate links cause deviations from the desired trajectory. From this analysis, the dynamic performance of the manipulators when tracking a desired trajectory is evaluated. A comparison of the results indicates that in some cases, adding each extra RPR chain in the n-RPR planar manipulators with flexible intermediate links reduces the stiffness and accuracy due to the inertial forces of the flexible links, which is opposite to what would be expected. The study provides insights to the design, control and suitable selection of the flexible manipulators.

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

In this paper, the port Hamiltonian model of a manipulator is presented as the result of the power-conserving interconnection of a set of main components (rigid bodies, flexible links and kinematic pairs). Since rigid bodies and flexible links are described within the port Hamiltonian formalism, their interconnection is possible once a proper relation between the power conjugated port variables is deduced. These relations are the analogous of the Kirchoff laws of circuit theory. The final model is a mixed port Hamiltonian system because of the presence of a finite dimensional subsystem modelling the rigid bodies and of an infinite dimensional one describing the flexible links. The intrinsic modularity of the approach simplifies the model deduction and simulation, while the Hamiltonian description suggests the development of energy-based controllers.

Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

This paper develops a novel non-singular terminal sliding mode control scheme for trajectory tracking of robot manipulators in the presence of external disturbances and uncertainties. Firstly, with the introduction of two nonlinear terms, a newly terminal sliding surface is designed. Then utilizing this sliding surface, a novel non-singular terminal sliding mode controller is proposed to eliminate the reaching interval, singularity issue and subsequently with this controller, the finite time error convergence is also assured. In the proposed controller, radial basis function neural network is employed to approximate highly uncertain nonlinear dynamics of robot manipulators using update laws derived with Lyapunov approach. Meanwhile, the effects of approximation errors are attenuated with $$H_\infty $$ performance criterion by introducing a robust term into the controller. As a result of proposed approach, asymptotic convergence of tracking errors is achieved within finite time and the approximation errors are attenuated to desired levels. The numerical simulation result shows the effectiveness of proposed controller for the case of microbot type robot manipulator.

This article aims at the problem of trajectory tracking for industrial robotic manipulators with control backlash. An arctangent terminal sliding mode surface is developed to deal with the lumped disturbance and enhance the robustness of the system. A novel adaptive super‐twisting sliding mode control method is developed to achieve fast convergence and continuous control. The chattering in control law is surmounted by using super‐twisting method. The lumped disturbance with unknown upper bound is compensated with the help of the adaptive technique. Based on the Lyapunov stability theory, the sliding mode surface will be arrived in a finite time and the trajectory tracking error will converge to zero in a finite time. The feasibility of the proposed control scheme is validated through an example of a two‐link robotic manipulator.

A recursive decentralized control scheme is presented for the trajectory tracking issue of flexible space manipulator. In the traditional decentralized control for manipulators, each link in the manipulator is viewed as an isolated second-order subsystem. Its interconnections with other links are viewed as disturbances. As a consequence, the obtained decentralized control has a simple structure but lacks high control performance. In this paper, the manipulator is considered as a set of connected second-order subsystems. The interconnections between two adjacent links are revealed by the recursive kinematics and dynamics. In such a manner, the nominal value of the interaction forces is constructed and used for compensation to improve the tracking accuracy. Then, decentralized control is devised for each link to achieve trajectory tracking and vibration suppression of the flexible manipulator. The actuators are chosen as the joint motors and the distributed force or torque actuators on the flexible links. Under the assumption that the modal information of the flexible links is obtainable by the modal filter technique, an adaptive term in the controller is deduced for estimating the bound of the model uncertainties. Moreover, a steering logic is devised for the actuators to produce the required control. The obtained controller inherits the simple structure of the decentralized control and also achieves a higher tracking performance. The numerical examples of a free flying flexible manipulator are given to illustrate the effectiveness of the proposed control scheme.

During the trajectory tracking of robotic manipulators, many factors including dead zones, saturation, and uncertain dynamics, greatly increase the modeling and control difficulty. Aiming for this issue, a nonlinear active disturbance rejection control (NADRC)-based control strategy is proposed for robotic manipulators. In this controller, an extended state observer is introduced on basis of the dynamic model, to observe the extend state of model uncertainties and external disturbances. Then, in combination with the nonlinear feedback control structure, the robust trajectory tracking of robotic manipulators is achieved. Furthermore, to optimize the key parameters of the controller, an improved particle swarm optimization algorithm (IPSO) is designed using chaos theory, which improves the tracking accuracy of the proposed NDRC strategy effectively. Finally, using comparative studies, the effectiveness of the proposed control strategy is demonstrated by comparing with several commonly used controllers.

Adaptive neural network control with optimal number of hidden nodes for trajectory tracking of robot manipulators