Dynamic Model Of Robot Research Articles

Design and analysis of a continuum manipulator for use in narrow spaces

PurposeThis paper aims to present a kinematics analysis method and statics based control of the continuum robot with mortise and tenon joints to achieve better control performance of the robot.Design/methodology/approachThe kinematics model is derived by the geometric analysis method under the piecewise constant curvature assumption, and the workspace and dexterity of the proposed robot are analyzed to optimize its structure parameters. Moreover, the statics model is established by the principle of virtual work, which is used to analyze the mapping relationship between the bending deformation and the applied forces/torques. To improve the control accuracy of the robot, a model-based controller is put forward.FindingsResults of the experiments verify the feasibility of the proposed continuum structure and the correctness of the established model and the control method. The force deviation between the theoretical value and the actual value is relatively small, and the mean value of the deviation between the driving forces is only 0.46 N, which verify the established statics model and the controller.Originality/valueThe proposed model and motion controller can realize its accurate bending control with a few deviations, which can be used as the reference for the motion planning and dynamic model of the continuum robot.

Automatic vibration control method for grasping end of flexible joint robot

Because flexible robots have flexible components such as reducers, there are problems of accuracy deviation and end vibration in the process of external interference and trajectory tracking. This leads to the proposal of a Sliding Mode Control Approach Based on RBF Neural Network (SMC-RBF) parameter optimization. This method is mainly applied to reduce the end vibration and running position error of flexible robot. Firstly, the Newton-Euler method is used to establish the dynamic model of robot considering joint flexibility. At the same time, the experiment optimizes the Sliding Mode Control (SMC) method through RBF neural network. The experiments verify the control methods of the two-joint flexible robot and the six-joint flexible robot respectively. In the control of two-joint robot, the maximum tracking curve error of SMC is only about 0.25 rad under the interference of pulse signal; And the recovery time is only about 1 s. In the control of 6-joint robot, the maximum error of RBF-sliding mode control method on XYZ axis is 0.7 mm, 0.25 mm and 1.25 mm respectively; The error on three axes is smaller than that of traditional PD control method. The results demonstrate that the tracking error of the improved mode control is small, the chattering phenomenon of the robot system is weakened as well.

Adaptive Fuzzy Fault Tolerant Control for Robot Manipulators With Fixed-Time Convergence

This paper aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: (i) the faster response, lower tracking errors, lower chattering and higher robustness of the FTC, (ii) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and (iii) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analysed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

Modeling and invariably horizontal control for the parallel mobile rescue robot based on PSO-CPG algorithm

AbstractA walking robot consisting of double Stewarts parallel legs was designed by our research team in the past time, which was mainly used for the transportation of the wounded after the disaster. In order to promote stability of control locomotion and ensure invariably horizontal state of the robot platform in the process of motion, the central pattern generator (CPG) based on particle swarm optimization (PSO) is presented to optimize the kinematic model. The purpose of optimization is to solve the hysteresis problem of displacement variation among the electric cylinders. Moreover, the dynamic model of the robot is established, which can provide mechanical basis for the feedback of control signal and make the robot move stably. The simulation results show that the displacement hysteresis problem of the electric cylinders is solved well. Meanwhile, compared with simulation results based on GA-CPG method, it is demonstrated that the robot motion planned using PSO-CPG method has better motion stability and can avoid the impact of legs landing during the transition phase of the motion cycle. The experimental results show that the platform on the robot can maintain an invariably horizontal state, and the locomotion is more stable. It verifies the feasibility of PSO-CPG model and the correctness of the dynamic model of the parallel mobile rescue robot.

Recurrent Neural Network with Finite Time Sampling for Dynamics Identification in Rehabilitation Robots

Rehabilitation robots can establish a direct connection between the user’s nerve signals and the robot’s actuators by integrating with the human nervous system. However, uncertainties in these systems limit their performance and accuracy. To address this challenge, the current study introduces an algorithm that effectively identifies and predicts unfamiliar dynamics in lower-limb rehabilitation robots. To accomplish this, the current study initially presents the dynamic model of a knee rehabilitation robot. Then, a finite time sampler is developed and the algorithm is proposed. In the proposed algorithm, the electromyographic signals are input into the rehabilitation robot. Via the use of a guaranteed stable sampler, samples from the unknown dynamics are extracted. By training the recurrent neural network with the acquired samples, the algorithm effectively learns and captures the underlying patterns of the unknown dynamics. The proposed recurrent neural network is enhanced with a self-attention mechanism, which plays a vital role in devising effective strategies for practical applications. Numerical simulation demonstrates the algorithm’s effectiveness, highlighting its excellent performance in identifying the system’s unknown dynamics.

Dynamic Modeling and Passivity-Based Control of an RV-3SB Robot

This paper shows the dynamic modeling and design of a passivity-based controller for the RV-3SB robot. Firstly, the dynamic modeling of a Mitsubishi RV-3SB robot is conducted using Euler–Lagrange formulation in order to obtain a decoupled dynamic model, considering the actuator orientation besides the position of the analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is conducted based on kinematic model obtention, which is developed by the implementation of screw theory. Then, the passivity-based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity-based controller is obtained by selecting an appropriate storage function, and by using Lyapunov theory, the passivity-based control law is obtained in order to drive the error variable, which is the difference between the measured end effector position variable and the desired end effector position variable. The passivity-based controller makes the error variable reach the origin in finite time, taking into consideration the dissipation properties of the proposed controller in order to stabilize the desired end effector position. A numerical simulation experiment is performed in order to validate the theoretical results obtained in this research. Using numerical experimentation, it is verified that the proposed control strategy is efficient and effective in driving the error variable to the origin in comparison with other modified techniques found in the literature. Finally, an appropriate discussion and conclusion of this research study are provided.

A Reduced Mass-Spring-Mass-Model of Compliant Robots Dedicated to the Evaluation of Impact Forces

Abstract The introduction of intrinsic compliance in the design of robots allows to reduce the risk for humans working in the vicinity of a robotic cell. Indeed, it permits to decouple the dynamic effects of the links’ inertia from those of the rotors’ inertia, thus reducing the maximum impact force in case of a collision. However, robot designers are lacking modeling tools to help simulate numerous collision scenarios, analyze the behavior of a compliant robot, and optimize its design. In this article, we introduce a method to reduce the dynamic model of a multi-link compliant robot to a simple translational mass-spring-mass system. Simulation results show that this reduced model allows to accurately predict the maximal impact force in case of a collision with a constrained human body part. Multiple impact scenarios are conducted on two case studies, a planar serial elastic robot and the R-Min robot, an underactuated parallel planar robot, designed for collaboration.

Torque control of a wheeled humanoid robot with dual redundant arms

Most of conventional controllers are prone to consume more computational time for controlling a wheeled humanoid robot. A nonlinear trajectory control of a wheeled humanoid robot using a model-based controller with less computational load and energy consumption is presented in this article. The upper body of the humanoid robot consists of two 6 degrees of freedom redundant arms, a three degrees of freedom torso and a two degrees of freedom neck. The nonholonomic mobile platform consists of two actuated wheels and two caster wheels. Nonlinearity of the robot dynamic model and coupling between various branches of the upper body are taken into account. The dynamic model derived using Newton–Euler approach along with decoupled Natural Orthogonal Complement matrix approach is used to derive dynamic equations of the upper body and the wheeled platform. Zero moment point based stability approach is used for verifying stable motion of the wheeled humanoid robot with minimum energy and time consumption to complete a task. The proposed computed torque controller is compared with other similar controllers developed for the same robot model to prove advantages of the proposed torque controller. Simulation results are experimentally validated with the real robot.

Closed-loop nonlinear optimal control design for flapping-wing flying robot (1.6 m wingspan) in indoor confined space: Prototyping, modeling, simulation, and experiment

The flapping-wing technology has emerged recently in the application of unmanned aerial robotics for autonomous flight, control, inspection, monitoring, and manipulation. Despite the advances in applications and outdoor manual flights (open-loop control), closed-loop control is yet to be investigated. This work presents a nonlinear optimal closed-loop control design via the state-dependent Riccati equation (SDRE) for a flapping-wing flying robot (FWFR). Considering that the dynamic modeling of the flapping-wing robot is complex, a proper model for the implementation of nonlinear control methods is demanded. This work proposes an alternative approach to deliver an equivalent dynamic for the translation of the system and a simplified model for orientation, to find equivalent dynamics for the whole system. The objective is to see the effect of flapping (periodic oscillation) on behavior through a simple model in simulation. Then the SDRE controller is applied to the derived model and implemented in simulations and experiments. The robot bird is a 1.6 m wingspan flapping-wing system (six-degree-of-freedom robot) with four actuators, three in the tail, and one as the flapping input. The underactuated system has been controlled successfully in position and orientation. The control loop is closed by the motion capture system in the indoor test bed where the experiments of flight have been successfully done.

Dynamic Output Feedback and Neural Network Control of a Non-Holonomic Mobile Robot.

This paper presents the design and synthesis of a dynamic output feedback neural network controller for a non-holonomic mobile robot. First, the dynamic model of a non-holonomic mobile robot is presented, in which these constraints are considered for the mathematical derivation of a feasible representation of this kind of robot. Then, two control strategies are provided based on kinematic control for this kind of robot. The first control strategy is based on driftless control; this means that considering that the velocity vector of the mobile robot is orthogonal to its restriction, a dynamic output feedback and neural network controller is designed so that the control action would be zero only when the velocity of the mobile robot is zero. The Lyapunov stability theorem is implemented in order to find a suitable control law. Then, another control strategy is designed for trajectory-tracking purposes, in which similar to the driftless controller, a kinematic control scheme is provided that is suitable to implement in more sophisticated hardware. In both control strategies, a dynamic control law is provided along with a feedforward neural network controller, so in this way, by the Lyapunov theory, the stability and convergence to the origin of the mobile robot position coordinates are ensured. Finally, two numerical experiments are presented in order to validate the theoretical results synthesized in this research study. Discussions and conclusions are provided in order to analyze the results found in this research study.

Optimized Computer Torque Control and Dynamic Model of a Spatial Single Section Continuum robot

In this paper, an Optimized Computer Torque Controller (OCTC) based on dynamic model for a variable continuum robot is elaborately developed. Basically, the OCTC is applied for the first time on continuum robots in this paper aiming at finding out the required robot’s cables tension as well as the robot’s orientation/bending angles during tracking any prescribed trajectory. Interestingly, particle swarm optimization (PSO) is used to optimize CTC’s parameters which enables the robot to perform tasks with high precision and stability, even in the presence of external perturbations or uncertainties. The proposed controller is evaluated by testing its ability to track multiple trajectories even in the presence of external perturbations. The results of these simulations show that the OCTC controller exhibits superior performance in terms of settling time, overshoot, and robustness against disturbances for the continuum robots. It is noteworthy to say that CTC is able to capture the behavior of the robot quite rapidly when the robot faces a disturbance which perfectly fits in with real-world applications of continuum robot.

Adaptive End-Effector Buffeting Sliding Mode Control for Heavy-Duty Robots with Long Arms

This study aims to resolve the problems of low precision, poor flexibility and unstable operation in the control performance of loading robots with long telescopic booms and heavy loads. Firstly, the kinematics and dynamics of long-arm heavy-duty robots are analyzed, and the dynamics model of a long-arm heavy-duty robot is established using the Lagrange method. A new power-hybrid sliding-mode approach law is proposed, and a hybrid force/position control strategy is used to control long-arm heavy-duty robots. The position control of long-arm heavy-duty robots uses a new sliding-mode adaptive control to improve the position accuracy of important joints, and PD control is used to force control the other joints. The two-stage telescopic arm is flexible and the long-arm heavy-load robot is simulated. The simulation results show that the long-arm heavy-load robot obtained using the improved sliding-mode adaptive control algorithm has good track-tracking and jitter-suppression effects. The new power-hybrid sliding-mode controller designed in this paper reduces the jitter amplitude of the end-effector of long-arm heavy-duty robots by 28.75%, 10.92% and 16.22%, respectively, compared with the existing new approach law sliding-mode controller. The simulation results show that the proposed power-hybrid reaching law sliding-mode controller can effectively reduce the amplitude difference of the end-effector. Finally, the force/position control strategy is combined with force-based impedance control, and the design process of impedance controller parameters is introduced, which provides a reference for the trajectory-tracking and vibration-suppression of end-effectors of long-arm heavy-duty robots.

Modeling and Trajectory Tracking Control for a Multi-Section Continuum Manipulator

This paper presents a dynamic modeling and trajectory tracking control design for a hyper-redundant continuum manipulator (HRM), which is able to continuously bend along its length. The HRM has the advantages of high flexibility, large workspace, and low inertia, but also poses challenges of complex kinematics, and dynamic coupling. To address these challenges, the kinematics model was developed based on the piecewise constant curvature (PCC) assumption that captures the relationship between the cable lengths, joint angles, and end-effector pose. Inverse kinematics is presented geometrically by solving trigonometric equations. The dynamic model for the continuum robot (CR) is derived using the Euler–Lagrange representation, which incorporates the effects of gravity and elasticity. Accordingly, three control strategies are developed and applied to a two-section continuum robot, which are the inverse dynamic proportional integral derivative controller (PID), fuzzy logic controller (FLC), and sliding mode controller (SMC). We validate our proposed methods through simulations on various 3D trajectories, utilized MATLAB symbolic math toolbox in conjunction with Simulink simulation; demonstrate and evaluate different close-loop dynamic control responses for different scenarios. This simulation used in the creation of a unique animated simulation for a whole 3D continuum robot while tracking its desired trajectories. Finally, the simulation results demonstrate that our proposed methods can effectively control the HRM with high tracking performance, exhibiting enhanced responses in terms of settling time, rising time, and steady-state error. While the developed SMC outperforms Poth's PID and FLC in terms of robustness and settling time (51% and 10.3%), respectively.

Design and implementation of multi-level linkage mechanism bionic pectoral fin for manta ray robot

Manta rays achieve propulsion through the undulation of their pectoral fins, making them an important subject of study in bionics due to their efficient and agile swimming characteristics. However, currently, most biomimetic pectoral fins achieve a flapping pattern similar to that of biological fins by using multiple driving mechanisms. This paper designs an actively controlled bionic pectoral fin, driven by a single motor through a multi-level linkage mechanism, for a larger size and fully functional manta ray robot. Firstly, a bionic pectoral fin is designed based on a multi-level linkage mechanism driven by a single motor, and non-linear programming methods are innovatively applied to optimize the fin mechanism, making its motion profile more similar to the sinusoidal waveform of biological manta ray pectoral fins. Secondly, a dynamic model of the manta ray robot is established using dynamic mesh technology to verify the propulsion capability of the bionic pectoral fins and the robot's theoretical speed. Finally, prototypes of the manta ray robot are developed, and experimental research is conducted in a pool and lake. The experimental results show that the robot can complete straight-line swimming, buoyancy control, and turning in water, with a stable and flexible motion posture, proving the feasibility of this method.

Dynamic Modeling and Performance Evaluation of a 5-DOF Hybrid Robot for Composite Material Machining

Dynamic performance is an important performance of robots used for machine processing. This paper studies the dynamic modeling and evaluation method of a 5-DOF (Degree of Freedom) hybrid robot used in aerospace composite material processing. With the consideration of the dynamics of the serial part, the complete dynamic model of the hybrid robot is established based on the virtual work principle. In addition to the widely considered acceleration term, a dynamic performance evaluation index that comprehensively considers the acceleration term, velocity term and gravity term in the dynamic model is proposed. Using the dynamic performance index, the effect of the placement direction of the robot and the arrangement of the double symmetric limbs on robot dynamics are investigated. The results indicate that the vertical placement is beneficial to the dynamics of the hybrid robot, and the arrangement of double symmetric limbs has different effects on different limbs.

APPLICATION OF FUZZY LOGIC IN THE ROBOT CONTROL FOR MECHANICAL PROCESSING

Robot application in mechanical machining is growing day by day because it has many advantages over conventional machines such as high flexibility, large working space, and high repeatability. Many degrees of freedom of motion give robots the ability to perform complex technological operations, but also because of that, methods of controlling robots based on dynamic models have difficulties. Applying fuzzy logic to robot control can partially or completely exclude the calculation of the robot's dynamic model as well as overcome other uncertainties of the whole technological system. The variables and parameters of the fuzzy logic-based controller are modeled in a linguistic form, called linguistic variables, and are defined by the linguistic semantic values. Fuzzy rules are an important basis for the performance of operations defining the control quantities of the controller. Fuzzy rules are constructed by natural human inference and are based on expert intelligence. The main tasks of applying fuzzy logic control include “Fuzzification” to determine fuzzy parameters in the form of fuzzy sets of input-output data; “Fuzzy Rules and Fuzzy Inference Mechanism” to perform fuzzy operations defining control quantities, and finally “Defuzzification” to convert control quantities from linguistic values to physical values for controller operation. There have been many types of research on applying fuzzy logic to control robots in general, but the percentage of fuzzy control research work for mechanical machining robots is still limited.
 The article is based on published works on fuzzy control for robots in general and mechanical processing robots to analyze the applicability of fuzzy control for mechanical machining robots. The article provides detailed information on fuzzy controller design, on determining input and output variables, proportional mapping to determine the number of fuzzy sets and the corresponding type of membership function. The construction of fuzzy rule base system and fuzzy inference mechanism is presented, and finally defuzzification. Prospects for the use of methods to perfect and develop fuzzy control systems for mechanical machining robots are also presented. Collectively, the information in this document is intended to guide the implementation of fuzzy logic-based controller designs for application to machining robots, as well as to general robot control.

A nonlinear robot joint friction compensation method including stick and sliding characteristics

PurposeDue to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.Design/methodology/approachThis paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.FindingsExperiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.Originality/valueThe proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

Dynamic modeling and analysis of wheeled wall-climbing robot

The dynamic model is very important for the design of a wall-climbing robot and the final realization of its motion performance. The general process of dynamic modeling and expression equations of dynamic models are given for the wheeled wall-climbing robot based on the modeling method of the Udwadia-Phohomsiri equation. Firstly, the dynamic model of an unconstrained four-wheeled wall-climbing robot is constructed. Then, a trajectory constraint is defined, the rationality of the dynamic model for the unconstrained wall-climbing robot is verified by numerical simulation. Again, constraint equations under the conditions of synchronous toothed belt structure, non-lateral motion and nonslip between the driving wheel and the wall surface are established. Finally, the dynamic model of the unconstrained wall-climbing robot is gradually combined with constraint equations, and numerical simulations are implemented. Numerical simulation results verify the correctness of the wall-climbing robot model and constraint models, as well as the effectiveness and advantages of the modeling method.

Piecewise Linear Strain Cosserat Model for Soft Slender Manipulator

Recently soft robotics has rapidly become a novel and promising area of research with many designs and applications due to their flexible and compliant structure. However, it is more difficult to derive the nonlinear dynamic model of such soft robots. The differential kinematics and dynamics of the soft manipulator can be formulated as a set of highly nonlinear partial differential equations (PDEs) via the classic Cosserat rod theory. In this work, we propose a discrete modeling technique named piecewise linear strain (PLS) to solve the PDEs of Cosserat-based models, based on which the associated analytic models are deduced. To validate the accuracy of the proposed Cosserat model, the static model of the conical cantilever rod under gravity as a simple example is simulated by using different discretization methods. Results indicate that PLS Cosserat model is comparable to the mechanical deformation behavior of a real-world soft manipulator. Finally, a parameters identification scheme for this model is established, and the simulation as well as experimental validation demonstrate that using this method can identify the model physical parameters with high accuracy.

Dynamic modeling and analysis of a vibration-driven robot driven by a conical dielectric elastomer actuator

This article aims to establish a theoretical model of a vibration-driven robot driven by a conical dielectric elastomer actuator and analyze its characteristics, in order to make up for the lack of theoretical model construction and parameter evolution analysis for this type of robot. This article introduces a vibration-driven robot driven by a conical dielectric elastomer actuator, and then establishes its dynamic model based on its electromechanical coupling and viscoelastic characteristics. Subsequently, simulation research is conducted using this model. Overall, this article derived a dynamic equation that can be applied to this type of robot, analyzed its motion characteristics, studied the effects of different parameters on it, and discussed the influence of viscoelasticity on vibration-driven robots. The proposed dynamic model and evolution law of vibration robots can provide theoretical guidance for subsequent control and optimization.