Newton-Euler Approach Research Articles

Control of Helicopter Using Virtual Swashplate

This article presents a virtual swashplate mechanism for a mini helicopter in classic configuration. The propeller bases are part of a passive mechanism driven by main rotor torque modulaton, this mechanism generates a synchronous and opposite change in the propellers angle of attack, then the thrust vector tilts. This approach proposes to control the 6 degrees of freedom of the aircraft using two rotors. The main rotor controls vertical displacement and uses torque modulation and swing-hinged propellers to generate pitch and roll moments and the horizontal displacement while the yaw moment is controlled by the tail rotor. The dynamic model is obtained using the Newton-Euler approach and robust control algorithms are proposed. Experimental results are presented to show the performance of the proposed virtual swashplate in real-time outdoor hover flights.

System Identification for Rigid‐Flexible Coupled Dynamics Model of a Cable‐Driven Aerial Manipulator

Aerial manipulators, integrating a quadrotor base and a multifaceted manipulator, represent a frontier in robotic innovation. This study introduces an identification methodology for the comprehensive dynamics of a cable‐driven aerial manipulator, incorporating both rigidity and flexibility. The initial section delineates the conception of an inventive cable‐driven aerial manipulator. Subsequent to the design, separate dynamics models for the quadrotor and the cable system are formulated using the Newton–Euler approach. The manipulator’s dynamics incorporate joint flexibility, resulting in a hybrid rigid‐flexible dynamics model. A synthesized dynamics model emerges upon the integration of the quadrotor and manipulator models. For the system’s identification, a backpropagation neural network is utilized. Enhancement of the neural network’s performance is achieved through an augmented butterfly optimization algorithm (BOA), which fine‐tunes thresholds and weights. This algorithm operates effectively owing to a clustering‐based competitive learning and a chaotic elite learning strategy, demonstrating adeptness in data extraction. Experimental validation confirms the superior accuracy and precision of the model derived from the algorithm herein, in comparison to two alternative methodologies. The findings underscore the algorithm’s exceptional accuracy, robustness, and stability, providing an accurate dynamic representation of the aerial manipulator.

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.

Kinematic and dynamic analysis of a 4-DOF over-constraint parallel driving mechanism with planar sub-closed chains

AbstractIn this paper, a new over-constrained parallel driving mechanism (PDM) with planar sub-closed chains is proposed. First, the number of over-constraints on the PDM is calculated. Then, an analysis is conducted as to the kinematics of the hybrid manipulator, including positions, velocities, and accelerations of all bodies. Furthermore, the Newton–Euler approach is taken to deduce the kinematic formula of each link and the formula of inertial force at the center of mass. However, it remains difficult to solve the equation since the number of equations is smaller than that of unknown variables. To solve this problem, the screw theory is applied in the present study to analyze the cause of over-constraints, with the link’s elastic deformation introduced as the supplement of deformation compatibility equations. Moreover, the actuation forces and constrained forces/moments are calculated simultaneously. Finally, the dynamic model is verified through simulation and experimentation. The proposed modeling approach provides a fundamental basis for the structural optimization and friction force computation of the over-constrained PDM.

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy.

Design computed torque control for Stewart platform with uncertainty to the rehabilitation of patients with leg disabilities

Physiotherapy is a treatment that may be required permanently by many patients. As a result, a robot that can execute physiotherapy exercises for the legs like a professional therapist with adequate performance and acceptable safety may be efficient and widely used. In this study, a robust control system for a Stewart platform with six degrees of freedom is provided. First, the Newton-Euler approach is used in conjunction with a methodology and some simplification tools to achieve explicit dynamics formulation for the Stewart platform. For the primary application of this research, which is to follow the specified trajectory of ankle rehabilitation, computed torque control law (CTCL) and polynomial chaos expansion (PCE) were used to examine and consider any uncertainty in geometric and physical parameters. In fact, this strategy integrated the uncertainties with CTCL using PCE. The suggested PCE-based CTCL eliminates the system’s nonlinearity by applying feedback linearization to evaluate generalized driving forces; hence, the nondeterministic multi-body system follows the desired direction. Uncertainties in the patient’s foot as well as the main diameter parameters of the moment of inertia of the upper platform of the Stewart robot with various uniform, beta, and normal distributions, have been analyzed. The PCE technique’s results were compared to the Monte Carlo method’s outcomes, and the strengths and weaknesses of each method were investigated. In brief, the PCE method operated far better than the Monte Carlo (MC) method in speed, accuracy, and numerical volume.

Control based on GLRT algorithm for Unmanned Aerial Vehicle

This paper proposes the study of a vision-based control scheme for an Unmanned Aerial Vehicle (UAV) for tracking objects floating on the sea surface. The applied vision scheme is based on the generalized likelihood ratio test (GLRT) algorithm. Once the target is detected, its coordinates are computed and used by the UAV control to track the target. The quadrotor mathematical model is developed using the Newton-Euler approach and a PID controller is implemented to track the vector containing the coordinates obtained through the vision scheme. To verify the effectiveness of the proposal, simulation tests are developed in MATLAB/Simulink based on a real video of an objective floating in the sea surface. The obtained results show an appropriate detection and tracking of the objective.

Design, Modeling, and Control Allocation of a Heavy-Lift Aerial Vehicle Consisting of Large Fixed Rotors and Small Tiltrotors

In this article, we propose an unconventional heavy-lift aerial vehicle (HLAV), present the design and its control allocation analysis, and prove the concept by the demonstration of the experimental test prototype in the outdoor environment. We aim for a mechanically robust and simple vehicle design that efficiently performs heavy lifting compared to other common aerial vehicles under certain width constraints, without using a complex swashplate mechanism. The HLAV performs the task of carrying the main load with two large propellers that are efficient, thanks to the greater disk area, and includes two small tilting propellers for controllability. This system has a “PPNN” (P: clockwise and N: counterclockwise) propeller arrangement to compensate for the reaction torques of equally sized propeller pairs placed on opposite sides. However, conventional quadcopters with the “PPNN” propeller arrangement lose their controllability upon hovering. Therefore, servo motors are integrated into the small propellers to ensure controllability. The nonlinear dynamic model of the HLAV is built by the Newton–Euler approach, and the system is linearized around the hover equilibrium point for controllability analysis. Model parameters are estimated using closed-loop system identification tools. High-level controllers are designed with the “loop shaping” method. To show feasibility, the proposed system is experimentally verified with a newly designed prototype, and sufficient trajectory tracking performance is achieved in a stable manner.

An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control

A quadcopter control system is a fundamentally difficult and challenging problem because its dynamics modelling is highly nonlinear, especially after accounting for the complicated aerodynamic effects. Plus, its variables are highly interdependent and coupled in nature. There are six controllers studied and analysed in this work which are (1) Proportional–Integral–Derivative (PID), (2) Proportional-Derivative (PD), (3) Linear Quadratic Regulator (LQR), (4) Proportional-Linear Quadratic Regulator (P-LQR), (5) Proportional-Derivative-Linear Quadratic Regulator (PD-LQR) and lastly (6) the proposed controller named Proportional-Double Derivative-Linear Quadratic Regulator (PD2-LQR) controller. The altitude control and attitude stabilization of the quadcopter have been investigated using MATLAB/Simulink software. The mathematical model of the quadcopter using the Newton–Euler approach is applied to these controllers has illuminated the attitude (i.e. pitch, yaw, and roll) and altitude motions of the quadcopter. The simulation results of the proposed PD2-LQR controller have been compared with the PD, PID, LQR, P-LQR, and PD-LQR controllers. The findings elucidated that the proposed PD2-LQR controller significantly improves the performance of the control system in almost all responses. Hence, the proposed PD2-LQR controller can be applied as an alternative controller of all four motions in quadcopters.

Sliding Mode Control-Based Autonomous Control of a Tri-rotor Unmanned Aerial Vehicle

A nonlinear control technique for autonomous control of a tri-rotor unmanned aerial vehicle is presented in this paper. First, a comprehensive mathematical model is developed using the Newton–Euler approach for a tri-rotor, which is found to be highly nonlinear and coupled. Then, the equivalent input affine model is extracted by applying a suitable transformation. Finally, the sliding mode control for trajectory tracking is chosen which is immune to matched external disturbances, parametric uncertainties, and modeling errors. The proposed controller performance has been verified for appropriate inputs under wind disturbances using MATLAB, and the simulation results are presented.

Hexarotor Longitudinal Flight Control with Deep Neural Network, PID Algorithm and Morphing

Unmanned Aerial Vehicles (UAV) have become an integral part of life, from military operations to entertainment. With this popularity, the interest of researchers on these UAVs has gradually increased. The use of UAVs in many areas and the researches carried out by researchers have revealed the limitations of these devices. At the beginning of these restrictions is the passage through the narrow space. In this study, longitudinal flight control of a hexarotor UAV is discussed with morphing. The limitation of this study is that it is difficult to estimate the moment of inertia and proportional integral derivative (PID) coefficient values according to the arm length, since there is a change in the hexarotor arm lengths with the morphing and the rigid body model changes accordingly. In this study, it is aimed to obtain these parameters with the Deep Neural Network(Artificial Neural Network(ANN) with two or more hidden layers) to overcome this problem. 15 drawings of hexarotor morphing states were drawn in Solidworks program. A training set was created by obtaining the PID coefficients for the longitudinal flight of these drawings from the Matlab/Simulink program. The training set was taught to the Deep Neural Network and moments of inertia and PID coefficients were obtained according to the arbitrarily estimated arm extension or shortening rates. In addition, the hexarotor dynamic model was derived according to the Newton-Euler approach and modeled using the state space model. The longitudinal flight of the hexarotor is simulated with the state space model. The moment of inertia and PID coefficients were estimated by Deep Neural Network according to the values determined by the program randomly together with the initial state. Simulations were made with these parameters and the results were given in graphics.

Task-based torque minimization of a 3-PṞR spherical parallel manipulator

AbstractA comprehensive dynamic modeling and actuator torque minimization of a new symmetrical three-degree-of-freedom (3-DOF) 3-PṞR spherical parallel manipulator (SPM) is presented. Three actuating systems, each of which composed of an electromotor, a gearbox and a double Rzeppa-type driveshaft, produce input torques of the manipulator. Kinematics of the 3-PṞR SPM was recently studied by the author (Zarkandi, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2020, https://doi.org/10.1177%2F0954406220938806). In this paper, a closed-form dynamic equation of the manipulator is derived with the Newton–Euler approach. Then, an optimization problem with kinematic and dynamic constraints is presented to minimize torques of the actuators for implementing a given task. The results are also verified by the SimMechanics model of the manipulator.

Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter

A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) and attitude (roll, pitch, and yaw) control of a quadrotor vehicle. The derivation of a mathematical model of the quadrotor is formulated based on the Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain a superior control performance. Therefore, we exploit the advantages of the nature-inspired optimization algorithm called Grey Wolf Optimizer (GWO) to search for those optimal values. Hence, an improved version of GWO called IGWO is proposed and used instead of the original one. A comparative study with the conventional controllers, namely proportional derivative (PD), proportional integral derivative (PID), linear quadratic regulator (LQR), proportional linear quadratic regulator (P-LQR), proportional derivative and linear quadratic regulator (PD-LQR), PD2-LQR, and original GWO-based PD2-LQR, was undertaken to show the effectiveness of the proposed approach. An investigation of 20 different quadcopter models using the proposed hybrid controller is presented. Simulation results prove that the IGWO-based PD2-LQR controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).

Enhanced impedance control for an sEMG-based prosthetic using RNE approach

Impedance control has been a major challenge faced by most robotic limbs and arms. One of the ways of enhancing the prosthetic performance is by optimising the kinematic and dynamic systems. Therefore, based on surface electromyography (sEMG) signal, this study implemented the recursive Newton-Euler (RNE) approach to establish an effective control for a prosthetics. The model introduced a primal approach which trains the subject to utilise the prosthetic sEMG data through the use of an operator-machine communal control. Further, the study utilised the estimated kinematics of the prosthetic manipulator to measure the joint torques which is expected to activate the prosthetic robotic arm, while using the active compensation to mask the real dynamics. Finally, the research used several simulations for prosthetic grasping, control and angular velocities to show that with the incorporation of the RNE approach, impedance is efficiently managed while the prosthetic devices are better controlled by the subjects.

Enhanced impedance control for an sEMG-based prosthetic using RNE approach

Impedance control has been a major challenge faced by most robotic limbs and arms. One of the ways of enhancing the prosthetic performance is by optimising the kinematic and dynamic systems. Therefore, based on surface electromyography (sEMG) signal, this study implemented the recursive Newton-Euler (RNE) approach to establish an effective control for a prosthetics. The model introduced a primal approach which trains the subject to utilise the prosthetic sEMG data through the use of an operator-machine communal control. Further, the study utilised the estimated kinematics of the prosthetic manipulator to measure the joint torques which is expected to activate the prosthetic robotic arm, while using the active compensation to mask the real dynamics. Finally, the research used several simulations for prosthetic grasping, control and angular velocities to show that with the incorporation of the RNE approach, impedance is efficiently managed while the prosthetic devices are better controlled by the subjects.

Design, Construction, and Control for an Underwater Vehicle Type Sepiida

SUMMARYA novel underwater vehicle configuration with an operating principle as the Sepiida animal is presented and developed in this paper. The mathematical equations describing the movements of the vehicle are obtained using the Newton–Euler approach. An analysis of the dynamic model is done for control purposes. A prototype and its embedded system are developed for validating analytically and experimentally the proposed mathematical representation. A real-time characterization of one mass is done to relate the pitch angle with the radio of displacement of the mass. In addition, first validation of the closed-loop system is done using a linear controller.

Real-Time Hovering Control of Unmanned Aerial Vehicles

In this paper, the design of a controller for the altitude and rotational dynamics is presented. In particular, the control problem is to maintain a desired altitude in a fixed position. The unmanned aerial vehicle dynamics are described by nonlinear equations, derived using the Newton–Euler approach. The control problem is solved imposing the stability of the error dynamics with respect to desired position and angular references. The performance and effectiveness of the proposed control are tested, first, via numerical simulations, using the Pixhawk Pilot Support Package simulator provided by Mathworks. Then, the controller is tested via a real-time implementation, using a quadrotor Aircraft F-450.

Real-Time Impedance Control Based on Learned Inverse Dynamics

Impedance control of robotic manipulators allows them to perform interaction task where safety is the foremost requirement. A major hurdle in the implementation of impedance control has always been the requirement of availability and online real-time computation of dynamic model. This paper presents a complete data-driven, machine learning approach to impedance control in real time of an industrial manipulator. The technique used here to learn the inverse dynamic model of an industrial robot is based on an incremental nonparametric statistical learning approach and is called locally weighted projection regression. Unlike traditional model-based control schemes, the proposed control strategy requires less a priori knowledge of the system being modeled, is computationally efficient to run in hard real time, and can be updated during online operation of the arm. The main contribution of this paper is the development of learning-based impedance control scheme and its implementation in hard real time (at 500 Hz control loop rate) on an industrial robot (Barrett WAM arm). To validate performance of the proposed scheme, its comparison with a controller based on an analytically derived model (computed online in a numeric recursive way using Newton–Euler approach) has also been presented through experimentation in a laboratory environment.

Depth control design and simulation of hybrid underwater glider

The hybrid underwater glider is a class from the autonomous underwater vehicle (AUV) that the concept of a buoyancy-engine to drive the vehicle sinking or to float are integrated and propeller propulsion systems for variable motion. This kind of vehicle has multi-functionality that enables to maneuver with glider and AUV mode so that it is an effective tool for oceanographic research. To represent the vehicle dynamic, the mathematical model base on the Newton–Euler approach is designed as a nonlinear equation that derives from forces and moment that acting from the vehicle design. For glider motion, buoyancy-engine and moving mass are coupled for working as an input controller actuator, and there is controlling need to make the vehicle go to mission point in some depth for a mission. In this paper, the depth controller will be designed to make the vehicle stay on depth condition. PID controller is used for controller design, that the mathematical model is derived to linear model. Simulation on MATLAB/Simulink is used to design the model and get dynamic vehicle response. With vehicle characteristic for glider motion, desired waypoint obtained with PD control for moving mass in x-axes and P controller for buoyancy engine.

Modeling and Assessing of Self-Reconfigurable Cleaning Robot hTetro Based on Energy Consumption

The autonomous floor-cleaning self-reconfigurable robots have entered into the practical stage by establishing enhanced area coverage over the fixed morphology counterparts. Energy consumption during the self-reconfiguration, i.e., changing the shape of the robot from one form to another, becomes a primary focus in these robots that carry finite energy sources. In this paper, hTetro platform with two hinge dissections namely, Left–Left–Left (LLL) and Left–Left–Right (LLR) are modeled and assessed for the energy consumption during the reconfiguration. The geometry of the two dissections, its workspaces, and the set of inverse kinematics solutions for the seven forms are presented. The inverse dynamics using the Newton–Euler approach was adopted to calculate the wrench, i.e., forces and moments at the hinge joints, and subsequently assess the power consumed during the reconfigurations of two hinge dissections in the simulation. Extensive experiments were performed across the two assembled platforms to estimate the power consumption by logging the current data. The comparison was made with the simulation results. The results are particularly useful in the selection of reconfiguration with minimal energy consumption during the floor cleaning.