Newest Issue

Research Papers

J. Dyn. Sys., Meas., Control. 2019;141(11):111001-111001-13. doi:10.1115/1.4043910.

In this paper, the six-wheel lunar rover is simulated in Adams/View software environment and then via co-simulation between adams and matlab/simulink with which a path-following controller is designed and implemented on the rocker-bogie mechanism. The proposed algorithm consists of three parts. First, the inverse kinematic equations are used to transform the trajectory into appropriate desired values. Second, a sliding mode controller (SMC) is designed which used the desired values to control the motion of the robot. Moreover, disturbances are taken into consideration to minimize the lateral error. In order to investigate the proposed integrated algorithm, the analysis of rover traversability on the uneven surface of the moon is performed in two different states, namely by considering the motion restrictions of the rocker-bogie mechanisms and by increasing the rover speed, body yaw angle, and also obstacle height in crossing the rough terrain. Investigation of the rover in different states has given insight on the performance of the proposed controller at limits of mobility of the robot. Finally, to reduce the battery energy consumption, input torques proportional to the load on the wheels are produced. The values of the deviations from the desired path and velocity in all the mentioned analyses indicate the effectiveness of the SMC.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(11):111002-111002-11. doi:10.1115/1.4043984.

A container crane mounted on a pontoon is utilized to transfer containers to smaller ships when a large container ship cannot reach the shallow water port. The shipboard container is considered as an underactuated system having complicated kinematic constraints and hysteretic nonlinearities, with only two actuators to conduct simultaneous tasks: tracking the trolley to destination, lifting the container to the desired cable length, and suppressing the axial container oscillations and container swing. Parameter variations, wave-induced motions of the ship, wind disturbance, and nonlinearities remain challenges for control of floating container cranes. To deal with these problems, this study presents the design of two nonlinear robust controllers, taking into account the influence of the output hysteresis, and using velocity feedback from a state observer. Control performance of the proposed controllers is verified in both simulation and experiments. Together with consistently stabilizing outputs, the proposed control approach well rejects hysteresis and disturbance.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(11):111003-111003-12. doi:10.1115/1.4043917.

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(11):111004-111004-14. doi:10.1115/1.4043933.

In this study, a task-space adaptive robust control methodology which takes uncertainties and external disturbances into account is proposed for a class of duct cleaning mobile manipulators. First of all, the configuration of the real duct cleaning robot is introduced, and the Jacobian matrix and the dynamic model of the real robotic system are obtained. Then, the structure of adaptive robust controller based on sliding mode control (SMC) approach and the fuzzy wavelet neural network is detailed, the proposed control approach combines the advantages of SMC which can suppress the external disturbances with the fuzzy wavelet neural network which can compensate the uncertainties by its strong ability to approximate a nonlinear function to an arbitrary accuracy, the stability of the whole robotic control system, the uniformly ultimately boundedness of tracking errors, and the boundedness of fuzzy wavelet neural networks weight estimation errors are all guaranteed based on the Lyapunov stability theory. Finally, simulation results are presented to demonstrate the superior performance of the proposed approach, and experiments are given to illustrate that the proposed approach is useful for real duct cleaning robot system with well performance.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(11):111005-111005-14. doi:10.1115/1.4044181.

The rack force is valuable information for a vehicle dynamics control system, as it relates closely to the road conditions and steering feel. Since there is no direct measurement of rack force in current steering systems, various rack force estimation methods have been proposed to obtain the rack force information. In order to get an accurate rack force estimate, it is important to have knowledge of the steering system friction. However, it is hard to have an accurate value of friction, as it is subject to variation due to operation conditions and material wear. Especially for the widely used column-assisted electric power steering (C-EPAS) system, the load-dependent characteristic of its worm gear friction has a significant effect on rack force estimation. In this paper, a rack force estimation method using a Kalman filter and a load-dependent friction estimation algorithm is introduced, and the effect of C-EPAS friction on rack force estimator performance is investigated. Unlike other rack force estimation methods, which assume that friction is known a priori, the proposed system uses a load-dependent friction estimation algorithm to determine accurate friction information in the steering system, and then a rack force is estimated using the relationship between steering torque and angle. The effectiveness of this proposed method is verified by carsim/simulink cosimulation.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Dyn. Sys., Meas., Control. 2019;141(11):114501-114501-7. doi:10.1115/1.4044179.

This paper proposes a robust compensator for a class of uncertain nonlinear systems subjected to unknown time-varying input delay. The proposed control law is based on the integral of past values of control and a novel filtered tracking error. Sufficient gain conditions dependent on the known bound of the delay are derived using a Lyapunov-based stability analysis, where Lyapunov–Krasovskii (LK) functionals are used to achieve a global uniformly ultimately bounded (GUUB) tracking result. Simulation results for a nonlinear system are used to evaluate the performance and robustness of the controller for different values of time-varying input delay.

Commentary by Dr. Valentin Fuster

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In