Two-link Robot Arm Research Articles

Design of an Optimal Multivariable PID Controller for a Two-Link Robot Arm

Incorporating manipulator robots into various industries has revolutionized automation and manufacturing processes. Manipulator robots are versatile machines equipped with robotic arms and end-effectors, enabling them to handle complex tasks with precision and efficiency. This research aims to design an optimal Mutivariable PID (MPID) controller that ensures precise and accurate movements of two-link robot arm. Theoretical foundations of the MPID controller are discussed together with its relevance to robotic arm systems, and the challenges associated with its implementation. The research methodology involves mathematical modelling of the two-link robot arm dynamics, and the use of an optimization algorithm to determine the optimal MPID controller coefficients. Simulation results demonstrate performance enhancement and productivity of the two-link robot arm through the improved MPID controller.

Fixed-Time Control of a Robotic Arm Based on Disturbance Observer Compensation

Backstepping-based fixed-time tracking control is proposed for a robotic arm system to solve the problem of trajectory tracking control under system uncertainties, which ensures the robotic arm system can realize stable tracking control within a fixed time independent of the initial state of the system. In addition, to address the uncertainties in the robotic arm system, a control strategy based on disturbance observer compensation is designed, which provides feed-forward compensation through the accurate estimation of the system uncertainties and enhances the system’s robustness. Finally, a two-link robotic arm model is used for simulation experiments, and the comparison results show that the control scheme designed in this article can effectively improve the robotic arm’s tracking accuracy and convergence speed.

Aye-aye middle finger kinematic modeling and motion tracking during tap-scanning

The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a unique thin middle finger. Its slender third digit has a remarkably specific adaptation, allowing them to perform tap-scanning to locate small cavities beneath tree bark and extract wood-boring larvae from it. As an exceptional active acoustic actuator, this finger makes an aye-aye’s biological system an attractive model for pioneering Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in the aye-aye’s unique foraging and its potential contribution to the engineering sensory, little is known about the mechanism and dynamics of this unique finger. This paper used a motion-tracking approach for the aye-aye’s middle finger using simultaneous video graphic capture. To mimic the motion, a two-link robot arm model is designed to reproduce the trajectory. Kinematics formulations were proposed to derive the motion of the middle finger using the Lagrangian method. In addition, a hardware model was developed to simulate the aye-aye’s finger motion. To validate the model, different motion states such as trajectory paths and joint angles, were compared. The simulation results indicate the kinematics of the model were consistent with the actual finger movement. This model is used to understand the aye-aye’s unique tap-scanning process for pioneering new tap-testing NDE strategies for various inspection applications.

NARMA-L2–based online computed torque control for robotic manipulators

Computed torque is an effective method for control of robotic manipulators. In this paper, a novel approach is proposed for computed torque control method, and also, it is integrated with online least squares support vector regression algorithm. The motive is to improve control performance and to build an adaptive architecture where no prior information on system model is required; the model is obtained continuously using a machine learning technique. There are two main contributions of the proposed work: the first one is the formulation of the computed torque control law using the nonlinear autoregressive moving average-L2 control methodology and the second contribution is the implementation of online least squares support vector regression method in estimating the dynamical model of the system and calculating the control law. The performance of the proposed method has been evaluated by simulations on a two-link robotic arm. The robustness of the method has also been evaluated for cases with disturbance and model uncertainty. Comparison with an existing computed torque control approach in the literature demonstrates the superiority of the proposed approach. Thus, it can be concluded that the method can be effectively used for first-order and second-order nonlinear systems without inherent instability.

Distributed sliding mode consensus control for multiple discrete-Time Euler-Lagrange systems

This paper studies the consensus problem for multiple discrete-time Euler-Lagrange (DTEL) systems via distributed sliding mode control under a directed graph. Different from the existing work, we transform the DTEL system into a discrete-time second-order nonlinear system through the famous Euler’s first-order approximation method, and a local discrete-time disturbance observer (DTDO) is introduced to estimate both model uncertainties and external disturbances. In addition, a novel integral sliding surface is proposed to guarantee that the consensus error is asymptotically stable when agents move on the sliding surface. Based on such a sliding manifold combined with the proposed DTDO, a distributed sliding mode controller is constructed. Meanwhile, a sufficient condition is derived to ensure the existence of the quasi-sliding mode motion. Finally, numerical simulations of the two-link robot arm’s system are carried out to verify the effectiveness of the proposed control algorithm.

An Online Adaptation Strategy for Direct Data-driven Control

The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear systems by updating the system representation during control. Unlike existing approaches, our method does not impose suitable excitation to the control inputs, but runs as an observer parallel to the controller. Whenever a rank condition is deemed to be fulfilled, the system representation is updated using newly available datapoints. In a reference tracking simulation of a two-link robotic arm, we showcase the performance of the proposed strategy in a predictive control framework.

Predefined-Time Bipartite Consensus of Networked Euler-Lagrange Systems via Sliding-Mode Control

This brief investigates the predefined-time bipartite consensus of multiple Euler-Lagrange systems with a dynamic leader. A new distributed predefined-time observer is added to estimate the desired velocity of each follower. Then, a new sliding surface and a distributed protocol are constructed to guarantee the networked systems achieve the bipartite consensus within a predefined time. It should be pointed out that, all the observing time, sliding time and reaching time can be preset in advance according to the task requirements. One numerical example with two-link robot arms is proposed to verify the effectiveness of the new designs.

Selection of smooth motion profile for a tube locator module of an inspection device

<span lang="EN-US">The Prototype Fast Breeder Reactor steam generators inspection system has seven modules. In this, tube locator module is a planar serial two-link robotic arm, which is used to place the eddy current probe above the steam generators tube hole in the tube sheet region. The trajectory planning of the two-link robotic arm is one of the important tasks, so the peak velocity, peak acceleration, peak jerk of various motion profiles for a given distance has to be selected properly for smooth motion and to avoid actuator saturation. The fifth-order polynomial gives lower acceleration and velocity than the jerk-limited S-curve. In this paper, the comparison of peak values of kinematic variables (velocity, acceleration, and jerk) for different motion profiles has been presented.</span>

Averaged sub-gradient integral sliding mode control design for cueing end-effector acceleration of a two-link robotic arm

Acceleration tracking is a significant problem in aeronautics, automotive, and biomedical technical areas because its solution may yield effective simulation of motion cues. In the case of aeronautics, the proper solution for the tracking problem improves the in-flight simulations for the training of plane pilots. These simulators can be set up using robotic devices that develop controlled motions with the end-effector following the required three-dimensional reference accelerations robustly. Hence, the primary goal of this study is the effective application of the integral sliding mode controller to solve the acceleration tracking problem for the end-effector of a two-link robotic arm. The control design problem is formulated as an optimization of a convex (non-strict) performance functional depending on the difference between the acceleration of the robotic arm and the desired acceleration using the averaged sub-gradient (ASG) descendant method. A novel sliding surface considers the sensitiveness threshold for acceleration dynamics, inspired by the limit of detection in the pilot vestibular apparatus. The proposed controller was analyzed in terms of the finite-time convergence of the sliding surface and the practical stability analysis for the tracking error dynamics. Our main contribution is the design of the online averaged sub-gradient optimization controller based on integral SMCs. The controller solves the end-effector acceleration tracking for a two-link robotic arm, which implements a simplified version of a flight simulator that is considered to be operated under uncertain scenarios and assumes the presence of perturbations and modeling errors. The controller considers the case of incomplete knowledge of the robotic arm model, which adds an extra degree of robustness to the control design. The numerical evaluations demonstrate the attributes of the ASG formulation compared to traditional state feedback control, using the performance functional, the norm of the acceleration tracking error, and the control input variation

Repetitive hygroscopic snapping movements in awns of wild oats

Wild oat (Avena sterilis) is a very common annual plant species. Successful seed dispersion support its wide distribution in Africa, Asia and Europe. The seed dispersal units are made of two elongated stiff awns that are attached to a pointy compartment containing two seeds. The awns bend and twist with changes in humidity, pushing the seeds along and into the soil. The present work reveals the material structure of the awns, and models their functionality as two-link robotic arms. Based on nano-to-micro structure analyses the bending and twisting hygroscopic movements are explained. The coordinated movements of two sister awns attached to one dispersal unit were followed. Our work shows that sister awns intersect typically twice every wetting-drying cycle. Once the awns cross each other, epidermal silica hairs are suggested to lock subsequent movements, resulting in stress accumulation. Sudden release of the interlocked awns induces jumps of the dispersal unit and changes in its movement direction. Our findings propose a new role to epidermis silica hairs and a new facet of wild oat seed dispersion. Reversible jumping mechanism in multiple-awn seed dispersal units may serve as a blueprint for reversibly jumping robotic systems. Statement of significanceThe seed dispersal unit of wild oats carries two elongated stiff awns covered by unidirectional silica hairs. The awns bend and twist with changes in humidity, pushing the seed capsule along and into the ground. We studied structures constructing the movement mechanism and modeled the awn as a two-link robotic arm. We show that sister awns, attached to the same seed capsule, intersect twice every drying cycle. Once the awns cross each other, the epidermal silica hairs are suggested to lock any subsequent movements, causing stress accumulation. Sudden release of the interlocked awns may cause the dispersal unit to jump and change its direction. Our findings suggest a new role to silica hairs and a new dispersal mechanism in multiple-awn seed dispersal units.

Computed Torque Control Method for Two-Axis Planar Serial Robotic Arm

The heat generated in reactor core is extracted using the sodium water heat exchanger system in fast breeder reactor. In the heat exchanger system there are 8 steam generators. Steam generator uses sodium and water to generate steam. Sodium–water reaction is exothermic in nature, so SG tubes need inspection to check healthiness of the tube. Due to space constraints, in-service inspection of SG needs remote-controlled robotic arm to locate SG tubes. The tube sheet is planar, so revolute joint two-link robotic arm is adapted. The movement of two-axis robotic arm needs control on position as well as velocity. The dynamic equation of two-axis robotic arm is coupled in nature which makes the control of end effector very difficult. The un-modeled phenomena, as that of friction and errors in mass distribution, will drastically affect the tracking of the end effector in the desired path. The Coriolis Effect is also accounted in the model. To precisely control the robotic arm end effector, the computed torque control technique is proposed and used in this paper.

Computing Worst-Case Disturbances for Finite-Horizon Linear Time-Varying Approximations of Uncertain Systems

A method is presented for computing the worst-case disturbance of a given norm for a finite-horizon linear time-varying system with a nonzero initial condition. This method is motivated by the linearized robustness analysis of a nonlinear system about a finite-horizon nominal trajectory. The system is linearized about this trajectory and interconnected with sampled model uncertainties. An additional input term to improve the fidelity of the linear interconnection is introduced and is absorbed into an augmented system with a nonzero initial condition. The method, which analyzes the robustness of the resulting interconnection to disturbance, is demonstrated on the numerical example of a two-link robot arm.

Fractional-order sliding mode controller for the two-link robot arm

In this paper, the author proposes a sliding mode controller with the fractional-order for the two-link robot arm. Firstly, the model and dynamic equations of the two-link robot arm are presented. Based on these equations, the author builds the controller for each joint of the robot. The controller is a sliding mode controller with its order is not an integer value. The task of the controller is to adjust the torques acted on the joints in order for the angular coordinates of each link to coincide with the desired values. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built to investigate the system quality. The results show that the quality of the control system is very high: there is not the chattering phenomenon of torques, the response angles of each link quickly reach the desired values, and the static error equal to zero.

ITERATIVE LEARNING CONTROL OF A TWO LINK ROBOT ARM

The control of robot manipulators has been an important research subject due to their frequent use in many fields. Meanwhile, learning controllers are widely employed for industrial robots to enable precision control. This paper considers the implementation of Derivative type Iterative Learning Controller (D-type ILC) for trajectory tracking of a two-link robot arm model. The nonlinear mathematical model of the robot is first linearized before applying the ILC controller to the robot system. The controller system is implemented using MATLAB. The results obtained illustrated the efficiency of the proposed controller and are compared to the results of a Proportional-Integral-Derivative (PID) controller.

An adaptive robust nonsingular fast terminal sliding mode controller based on wavelet neural network for a 2-DOF robotic arm

The paper proposes a new robust adaptive control method for a two-link robot manipulator to enhance high tracking control performance despite the presence of dynamic uncertainties and unknown disturbances. The paper examines a Non-Singular Fast Time Sliding Mode (NFTSM) controller based on Wavelet Neural Network (WNN). The Wavelet Network is employed to approximate the upper bound of uncertainties and disturbances. In addition, a compensation term is added to the NFTSM control to attenuate the effect of uncertainties, including unavoidable approximation errors and unknown disturbances. Therefore, to ensure high tracking accuracy, chattering phenomenon reduction, and fast response against approximation errors and uncertainties. The parameters of the controller are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by the Lyapunov stability theorem. Due to these techniques, the suggested control drives the system to the desired performance where tracking errors converge to zero within a finite time. Simulations performed on a two-link robotic arm demonstrate a higher performance of the proposed adaptive robust NFTSMC based on WNN methodology compared to some advanced control strategies.

Neuro-based Canonical Transformation of Port Controlled Hamiltonian Systems

In the literature of control theory, tracking control of port controlled Hamiltonian systems is generally achieved using canonical transformation. Closed form evaluation of state-feedback for the canonical transformation requires the solution of certain partial differential equations which becomes very difficult for nonlinear systems. This paper presents the application of neural networks for the canonical transformation of port controlled Hamiltonian systems. Instead of solving the partial differential equations, neural networks are used to approximate the closed-form state-feedback required for canonical transformation. Ultimate boundedness of the tracking and neural network weight errors is guaranteed. The proposed approach is structure preserving. The application of neural networks is direct and off-line processing of neural networks is not needed. Efficacy of the proposed approach is demonstrated with the examples of a mass-spring system, a two-link robot arm and an Autonomous Underwater Vehicle (AUV).

Tracking Fault-Tolerant Control for a Class of Switching Systems with Sensor Faults

This paper tossed a challenge of developing a novel active fault-tolerant control scheme for switching systems subject to exogenous disturbances and sensor faults. The proposed method is based on a reconfiguration mechanism that uses a mixed $${H_\infty }/{{H}}_{-} $$ formulation to design a robust state feedback FTC. This later is designed through a convex optimization technique which leads to solve linear matrix inequalities. Indeed, the main idea presented in this work is to develop an observer-based controller able to detect with accuracy the instant of fault occurrence, to compensate for the fault effect and to minimize the tracking error for all bounded reference inputs. To illustrate the efficiency of the tracking FTC scheme, extensive simulation results carried on a two-link robot arm are included.

A Novel Prescribed Performance Controller With Unknown Dead-Zone and Impactive Disturbance

This paper investigates a robust prescribed performance control scheme for motion control system with unknown dead-zone input and impactive disturbance. The unknown dead-zone input is divided into a linear part and a disturb part firstly. A smooth dead-zone inverse is constructed to compensate the influence. Subsequently, a novel error transform function is proposed for the limitation that the former prescribed performance control scheme cannot tolerate the impact disturbance. A robust control scheme with prescribed convergence rate is derived with the help of back-stepping technique and the dead-zone parameters is estimated by a designed adaptive law. Lyapunov function is employed to improve the stability of the system. Moreover, a fast finite-time-converge sliding mode differentiator is introduced to estimate the unavailable states of the motion control system. Finally, a two-link robot arm with unknown dead-zone input and high impact is employed to demonstrate the robustness and effectiveness of the control scheme.

Leader–Following Consensus of Multiple Uncertain Euler–Lagrange Systems With Unknown Dynamic Leader

In this paper, we consider the leader–following consensus problem of uncertain Euler–Lagrange multi-agent systems. In comparison with existing results, the leader system is used to formulate both the reference trajectory and external disturbances, and the dynamics of the leader system contains unknown parameters. The current setting makes our problem formulation more realistic while at the same time, it poses significant technical challenges to the solvability of the problem. Inspired by the robust control approach and the adaptive control approach, we propose a novel adaptive distributed control law. It is shown that by the proposed control law, consensus of multiple uncertain Euler–Lagrange systems can be achieved in spite of unknown dynamic leader systems. Our main result is demonstrated by its exemplary application to cooperative control of a group of two-link robot arms.

Sliding mode control-based system for the two-link robot arm

In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero.