Torque Constraints Research Articles

Analysis of electromagnetic vibration of IPM motor based on vector control for electric propulsion ships

AbstractAn overall investigation of the electromagnetic force, vibration, and average torque of the Interior Permanent Magnet Synchronous Motors (IPMSM) in the dq model for electric propulsion ships is carried out, and the electromagnetic vibration characteristics of the PMSM under the vector control strategy is explored. Firstly, the space and frequency characteristics of the electromagnetic force of the dq model motor are derived using the Maxwell stress tensor method, and the influence of the dq‐axes magnetic field on the electromagnetic force under different loads is discussed in detail. Secondly, the finite element method is used to verify the influence of dq‐axes currents on motor electromagnetic force, vibration, and average torque. Finally, the experiments are conducted on an 8‐pole 48‐slot IPMSM, and the results are consistent with the theoretical analysis and simulation results. The results indicate that the electromagnetic vibration of the PMSM for electric propulsion ships increases with the increase of the current iq and decreases with the increase of the current id. The electromagnetic vibration of the motor can be reduced by selecting the appropriate dq‐axes current under the output torque constraint.

Trajectory tracking controller design for wheeled Mobile robot with velocity and torque constraints

This article investigates the trajectory tracking control problem for wheeled mobile robot (WMR) subject to both velocity and torque constraints. Firstly, a more general model is established by considering the displacement between the centre of mass and the midpoint of the driving wheels of the WMR, based on which, a new bounded kinematic controller is proposed by combining the first-order filtering approach to keep the trajectory tracking error asymptotically stable. Then the dynamic controller is designed and the stability of the overall closed-loop system is proved by the Lyapunov stability theory and Barbalat's lemma. Some conditions are given to ensure that both the velocity constraints and torque constraints are satisfied. Finally, some numerical examples are given to show the effectiveness and advantages of the method proposed in this paper.

Vision-based adaptive LT sliding mode admittance control for collaborative robots with actuator saturation

Abstract In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

Investigation of the Effect of Physical Ability on the Fall Mitigation Motion Using the Combination of Experiment and Simulation

The simulation of fall plays a critical role in estimating injuries caused by fall. However, implementing human fall mitigation motions on a simulator proves challenging due to the complexity and variability of fall movement. Our simulator estimates fall motion by extrapolating the motion observed in fall experiments. By incorporating actual fall motion data for the upper limbs, we enhanced the realism of the fall simulation. The application of forward dynamics control to the lower limbs allowed for the adjustment of mitigation motions, taking into account individual physical capabilities. In this study, fall simulations were conducted under the constraints of maximum joint torque and maximum torque change rate, emulating the physical capabilities of both the elderly and young adults. Our results successfully demonstrated the mitigation motion facilitated by the stance leg reduced the descent velocity of the center of mass by 0.75 m/s for elderly individuals and by 1.25 m/s for young adults, compared to a zero torque condition. This indicates that our study introduced a novel method for quantifying the impact of the lower limbs’ physical capabilities on fall velocity. Such a method represents a significant advancement in understanding how mitigation motions can influence fall injury simulations.

A Novel Trajectory Tracking Control of AGV Based on Udwadia-Kalaba Approach

Several control approaches have been used in order to achieve the trajectory tracking control of automated guided vehicle (AGV). But they are all based on the D'Alembert principle which requires to determine the Lagrangian multipliers. Unlike those control methods, in this paper, a novel approach of trajectory tracking control is presented, which is based on Udwadia-Kalaba approach. This approach presents a novel, concise and explicit equation of motion for constrained mechanical systems with holonomic and/or nonholonomic constraints as well as constraints that may be ideal or non-ideal. The Udwadia-Kalaba equation is uncoupled and free from the Lagrangian multipliers. In this paper, we classify constraints into structural constraints and performance constraints. Structural constraints are set up regardless of trajectory control and we use structural constraints to establish the dynamic model. Then the desired trajectory is set as performance constraints and constraint torques are solved by analyzing Udwadia-Kalaba equation. For AGV, a nonholonomic mechanical system, second-order constraints are used to get constraint forces and the numerical simulation is conducted by using Matlab. The results show that the AGV's movement meets the requirement and the tracking trajectory is exact and perfect. Compared with other approaches, this approach requires very little computation. Through two cases of tracking a circle and a line, how to use Udwadia-Kalaba equation to realize trajectory tracking control is shown in detail.

Inverse dynamics analysis of a 6-RR-RP-RR parallel manipulator with offset universal joints

AbstractThis paper presents an algorithm for solving the inverse dynamics of a parallel manipulator (PM) with offset universal joints (RR–joints) via the Newton–Euler method. The PM with RR–joints increase the joint stiffness and enlarge the workspace but introduces additional joint parameters and constraint torques, rendering the dynamics more complex. Unlike existing studies on PMs with RR–joints, which emphasize the kinematics and joint performance, this paper studies the dynamical model. First, an iterative algorithm is established through a rigid body velocity transformation, which calculates the input parameters of the link velocity and acceleration. A linear system of equations in matrix form is then established for the entire PM through the Newton–Euler method. By using the generalized minimal residual method (GMRES) to solve the equation system, all the forces and torques on the joints can be obtained, from which the required actuation force can be derived. This method is validated through numerical simulations using the automatic dynamic analysis of multibody systems software. The proposed method is suitable for establishing the dynamic model of complex PMs with redundant or hybrid structures.

Multi-body dynamical modeling and prediction of flexible origami/kirigami structures by affine transformation

A generalized multi-body dynamical modeling method of Origami/Kirigami structures considering the elastic–flexible coupling of surfaces and nonlinear creases is proposed. For different structures with complex spatial configuration and constraints, an Affine Transformation (AT) technique based on Absolute Nodal Coordinate Formulation (ANCF) is proposed to make the modeling more convenient and efficient. The surfaces are discretized by triangle reduced elements, and the constitutive relations of creases are described by nonlinear state-dependent torque constraints. The proof of the topological invariance of configurations is also given. The simulation of the folding capacity and dynamic behaviors on two Origami structures, Origami solar array mechanism, and enclosed Yoshimura Origami, are given, which verifies the effectiveness of the modeling method. Additionally, the quantitative dynamic behaviors contributes to the design principle of a Kirigami structure for multi-stability. Comparisons between theoretical results from modeling and experiments demonstrate the effectiveness of the modeling method and crease design. The AT-ANCF lays a theoretical foundation for dynamic prediction and design for large deformation Origami/ Kirigami structures, which has significant potential applications in dynamic properties design in the fields of aerospace, robotic etc.

Data-Driven Model Predictive Control for Uncalibrated Visual Servoing

This paper addresses the image-based visual servoing (IBVS) control problem with an uncalibrated camera, unknown dynamics, and constraints. A novel data-driven uncalibrated IBVS (UIBVS) strategy is proposed, incorporated with the Koopman-based model predictive control (KMPC) algorithm and the adaptive robust Kalman filter (ARKF). First, to alleviate the need for calibration of the camera’s intrinsic and extrinsic parameters, the ARKF with an adaptive factor is utilized to estimate the image Jacobian matrix online, thereby eliminating the laborious camera calibration procedures and improving robustness against camera disturbances. Then, a data-driven MPC strategy is proposed, wherein the unknown nonlinear dynamic model is learned using the Koopman operator theory, resulting in a linear Koopman prediction model. Only input–output data are used to construct the prediction model, and hence, the proposed approach is robust against model uncertainties. Furthermore, with a symmetric quadratic cost function, the proposed approach solves the quadratic programming problem online, and visibility constraints as well as joint torque constraints are taken into account. As a result, the proposed KMPC scheme can be implemented in real time, and the UIBVS performance degradation which arises from the control torque constraints can be avoided. Simulations and comparisons for a 2-DOF robotic manipulator demonstrate the feasibility of the proposed approach. Simulation results further validate that the computation time of the proposed approach is comparable to the one of kinematic-based methods.

Attitude Stabilization of a Satellite with Large Flexible Elements Using On-Board Actuators Only

Attitude control of a satellite with three flexible elements is considered. Control torque is developed by a set of reaction wheels, which are installed on the central hub of the satellite. The flexible elements are large, so the control torque constraints must be taken into account. In the paper, a control algorithm based on a linear-quadratic regulator is studied. The asymptotic stability of this control is shown. The choice of the control parameters is based on the closed form solution of the corresponding algebraic Riccati equation, which is supplemented by the linear matrix inequality. To increase the convergence rate, particle swarm optimization is used to tune the control parameters.

Multiobjective Optimization of a Traction Motor in Driving Cycles Using a Coupled Electromagnetic–Thermal 1D Simulation

This paper proposes an electric propulsion system model for computationally efficient multiobjective optimization considering overall driving cycles using coupled electromagnetic–thermal 1D simulation. The proposed system model uses equivalent circuit networks rather than finite element method to obtain high computational efficiency because considering both driving cycles and motor temperature change during optimization requires considerable computational cost. The magnetic saturation effect and temperature change of traction motor during operation are examined by constructing look-up tables. This study first integrated the lumped parameter thermal network into the system model to consider the motor temperature change without any iterative process. The 1D simulation results over urban and highway driving cycles indicate that the changes in the induced current, efficiency, and motor temperature could be predicted while driving. The Pareto optimal solution of traction motor cross-sectional geometry including reduction ratio is successfully obtained by multiobjective optimization with the maximum output torque constraint. Constructing the proposed system model once, optimal design for multiple target driving cycles can be obtained without any driving cycle analysis. In addition, high computational efficiency indicates that the proposed system model is practical in the early design stage of various electric propulsions.

Experimental Validation of Constrained Spacecraft Attitude Planning via Invariant Sets

This paper experimentally validates the invariant-set motion planner (ISMP) for the spacecraft attitude motion planning problem. Three novel results are presented that enable the experimental implementation: i) a method for gridding quaternions from the keep-in cone, ii) a method for scaling the invariant sets to enforce angular velocity constraints, and iii) a method for scaling the sets used by the ISMP to ensure their positive invariance despite this torque constraint. The ISMP is experimentally validated through three experimental scenarios. In the first scenario, the spacecraft must perform a re-orientation maneuver that caused it to move toward a keep-out cone. The ISMP manages the momentum of the spacecraft to prevent it from overshooting into the keep-out cone. In the second scenario, the spacecraft performs a slalom maneuver to avoid a pair of keep-out cones. The ISMP must reverse the momentum of the spacecraft to transition from avoiding the first keep-out cone to the second. The final scenario is an unrealistically difficult scenario designed to stress-test the capabilities of the ISMP where the spacecraft must escape from a maze of keep-out cones. These results demonstrate the ability of the ISMP to control the spacecraft attitude while enforcing state and input constraints.

Robust consensus control of second-order uncertain multiagent systems with velocity and input constraints

In this paper, we investigate the consensus problem of second-order multiagent systems under directed graphs. Simple yet robust consensus algorithms that advance existing achievements in accounting for velocity and input constraints, agent uncertainties, and lack of neighboring velocity measurements are proposed. Furthermore, we show that the proposed method can be applied to the consensus control of uncertain robotic manipulators with velocity and control torque constraints. We rigorously prove that the velocity and control inputs stay within prespecified ranges by tuning design parameters a priori and that asymptotic consensus can be achieved through Lyapunov functions and fixed-time stability. Simulations are performed for symmetric and asymmetric constraints to show the efficiency of the theoretical findings.

Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints

In various permanent magnet synchronous motor (PMSM) drive applications the torque dynamics are an important performance criterion. Here, time-optimal control (TOC) methods can be utilized to achieve highest control dynamics. Applying state-of-the-art TOC methods leads to unintended overcurrents and torque over- and undershoots during transient operation. To prevent these unintended control characteristics while still achieving TOC performance the time-optimal model predictive control (TO-MPC) is proposed in this work. The TO-MPC contains a reference pre-rotation (RPR) and a continuous control set model predictive flux control (CCS-MPFC). By applying Pontryagin's maximum principle, the TOC solution trajectories for states and inputs of the PMSM are determined neglecting current and torque limits. With the TOC solution, a flux linkage reference for the CCS-MPFC is calculated that corresponds to a pre-rotation of the operating point in the stator-fixed coordinate system. This pre-rotated flux linkage reference is reached in minimum time without overcurrents and torque over- as well as undershoots by incorporating current and torque limits as time-varying softened state constraints into the CCS-MPFC. Simulative and experimental investigations for linearly and nonlinearly magnetized PMSMs in the whole speed and torque range show that, compared to state-of-the-art TOC methods, overcurrents and torque over- as well as undershoots are prevented by the proposed TO-MPC.

Explicit reference governor on [formula omitted] for torque and pointing constraint management

This paper focuses on the constrained attitude control of a rigid body. We propose an Explicit Reference Governor (ERG) specifically designed to handle constraints on SO(3). Two types of constraints are considered: (i) input torque constraints, and (ii) geometric conic pointing constraints. The objective of this paper is to design a control law that is able to steer the attitude of a rigid body to the desired attitude reference while enforcing constraints at all times. To this end, the first step is to design a controller that makes the desired attitude asymptotically stable when constraints (i) and (ii) are neglected. Next, the proposed control scheme is augmented with a novel ERG scheme able to add constraint-handling capabilities to the overall architecture. All the proposed solutions are defined in terms of SO(3) and do not make use of any parameterization such as quaternions or Euler angles. Numerical simulations are carried out to show the effectiveness of the proposed control scheme.

Adaptive Stability Control Based on Sliding Model Control for BEVs Driven by In-Wheel Motors

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control.

Homogeneous weighted motion planning for cooperated manipulator systems under position-torque constrained processing Environment

Real-time motion planning under position and torque constraints is a critical challenge for cooperative manipulator efficiency and safety operation. Real-time motion planning at the velocity level improves computation efficiency, eliminates the complex derivative calculation of the Jacobian matrix, and the velocity planning solution can be used directly for robotic kinematic control. However, little research attention has been attached to handling the position and torque constraints simultaneously at velocity level for cooperative manipulator systems. In this paper, we introduce a novel homogeneous weighted least-norm method (HWLN) for joint velocity redistribution of cooperated manipulators. Within the coupled kinematics-dynamics model of cooperated manipulators, joint position and torque constraints are simultaneously homogenized and taken into account by the constraint performance index. To avoid joint's constraint saturations, two real-time weight updating laws are designed for the joint position and driving torque respectively. The joint velocities of cooperated manipulators are then adaptively redistributed using the pseudo-kinetic-energy minimum optimization criteria. When compared to single manipulator regulation, this strategy takes greater advantage of cooperative redundancy and significantly enhances the position-torque planning performance. Mathematical stability proof is presented. In the meanwhile, numerical experiment results under various joint position and torque constraints demonstrate the effectiveness of the proposed HWLN method. The experimental results for motion planning and control of two 6R ABD-20Kg robotic manipulators are provided.

Applied nonlinear control of spacecraft simulator with constraints on torque and momentum of reaction wheels

In this paper, the attitude of a spacecraft simulator is controlled in the presence of nonlinearities, uncertainties and constraints of the system. An optimization-based controller is developed in the closed form based on predicting the nonlinear responses of spacecraft system. For practical requirements of the spacecraft actuated by the reaction wheels (RWs), the control method considers the actuator limitation in the torques applied to RWs and the restriction in the angular momentum of the wheels due to the limited rotational speed of motors. In this way, the momentum constraint is equated with the torque constraint by a prediction-based transformation. Then, the constrained optimal control law is calculated by solving an equivalent optimization formulation. The constrained stability is analyzed by the Lyapunov second method and the bound of system response is obtained in terms of the prediction time. In the results, at first, the performance of the unconstrained version of the proposed controller, which leads to the feedback linearization like controller, is evaluated in the presence of uncertainties through computer simulations. Finally, the constrained version is simulated and experimentally implemented as hardware in the loop on the spacecraft simulator. The obtained comparative results indicate a good performance for the constrained controller in realistic conditions.

Moving Target Landing of a Quadrotor Using Robust Optimal Guaranteed Cost Control

Dear Editor, This letter proposes a robust control strategy for the autonomous landing of a quadrotor on a moving target. Specifically, a force command that consists of a cascade dynamics estimator and an optimal guaranteed cost control law is exploited for the position-loop tracking. Then, an orientation constraint torque command is employed for the attitude-loop tracking such that the quadrotor refrains from flipping during the landing operation. Stability analysis indicates that the overall closed-loop system is asymptotically stable. Finally, flight experiments validate and access the theoretical results.

Probabilistic framework for reliable optimal design of gearboxes in general-purpose industrial robots considering random use conditions

AbstractA vertically articulated robot with 6-degrees of freedom (DoF), called a general-purpose robot, can perform a myriad of different tasks within a workspace. This paper newly presents a probabilistic framework for the reliable optimal design of gearboxes used in general-purpose industrial robots, which considers random use conditions. To account for random use conditions, the start and end positions of a single motion profile are described as the random variable, which is statistically modeled as a uniform distribution based on the assumption that we have no information about the robot use pattern. Then, each sample of the random variable is converted to the corresponding motion profile by using an on-line trajectory planner. Monte Carlo simulation is implemented for the uncertainty propagation analysis, due to the heuristic feature of the on-line trajectory planner. In the design optimization formulation, the peak torque constraint and maximum bending moment constraint are described in a probabilistic manner. The system-level lifetime is calculated by combining component scale factors. The effectiveness of the proposed framework is demonstrated by examining a case study of a gearbox size problem for a 6-DoF serial industrial robot. The benefits of this study are as follows: Firstly, the proposed framework can evaluate the performance considering random use conditions. Secondly, torque reliability and bending moment reliability are newly proposed to ensure the dynamic performance of an industrial robot. Thirdly, the system-level lifetime by combining component scale factors gives more user-oriented and intuitive measure in an industrial robot design.

Design of a Wheel-Side Rear-Drive Distributed Electric Bus Control Strategy Based on Self-Correcting Fuzzy Control

A suitable and effective control strategy is a prerequisite for achieving the stable driving of a distributed drive electric bus. In order to effectively utilize the advantage of the independent controllability of each rear wheel, this paper designs and compares two direct transverse moment control strategies of sliding mode control and self-correcting fuzzy control and distributes the drive torque in combination with the vehicle steering torque constraint. Moreover, based on the established seven-degrees-of-freedom vehicle model, the simulation was verified in the MATLAB/Simulink and TruckSim co-simulation platforms. The simulation results show that, compared with the sliding mode control, the self-correcting fuzzy control strategy can reduce the maximum sideslip angle deviation by 19%, 6% and 9.7%, respectively, under the double shift line condition, the high-speed small steering angle step condition and the sinusoidal line shift condition and can more effectively reduce the vehicle lateral acceleration and improve the vehicle yaw rate tracking ability, significantly improving the lateral stability of the vehicle.