A novel model-based robust control design for collaborative robot joint module

In order to reduce the impact of load and system parameter changes on the dynamic performance of collaborative robot joint module, a novel robust control algorithm is proposed in this paper to solve the problem of dynamic control of collaborative robot joint module trajectory tracking. The controller is composed of two parts: one is a nominal control term designed based on the dynamical model, aiming to stabilize the nominal robot system; the other is a robust control term based on the Lyapunov method, aiming to eliminate the influence of uncertainty on tracking performance, where the uncertainties include nonlinear friction, parameter uncertainty, and external disturbances. The Lyapunov minimax method is adopted to prove that the system is uniformly bounded and uniformly ultimately bounded. We performed numerical simulation and experimental validation based on an actual collaborative robot joint module experimental platform and the rapid controller prototype cSPACE. The numerical simulation and experimental results show that the controller has excellent control performance for the collaborative robot joint module and provides more accurate trajectory tracking under the influence of uncertainties.

Model-based robust control design and experimental validation of SCARA robot system with uncertainty

In this paper, we design a novel robust control method to reduce the trajectory tracking errors of the SCARA robot with uncertainties including parameters such as uncertainty of the mechanical system and external disturbance, which are time-varying and nonlinear. Then, we propose a deterministic form of the model-based robust control algorithm to deal with the uncertainties. The proposed control algorithm is composed of two parts according to the assumed upper limit of the system uncertainties: one is the traditional proportional-derivative control, and the other is the robust control based on the Lyapunov method, which has the characteristics of model-based and error-based. The stability of the proposed control algorithm is proved by the Lyapunov method theoretically, which shows the system can maintain uniformly bounded and uniformly ultimately bounded. The experimental platform includes the rapid controller prototyping cSPACE, which is designed to reduce programming time and to improve the efficiency of the practical operation. Moreover, we adopt different friction models to investigate the effect of friction on robot performance in robot joints. Finally, numerical simulation and experimental results indicate that the control algorithm proposed in this paper has desired control performance on the SCARA robot.

Rigid-Flexible Coupled Dynamics and PD-Robust Control Design for the Spacecraft with Rotating Solar Panels

The dynamical model for the spacecraft with multiple solar panels and the cooperative controller for such spacecraft are studied in this paper. The spacecraft consists of a rigid platform and two groups of flexible solar panels, where solar panels could be driven to rotate by the connecting shaft. The flexible solar panel involves the use of the orthogonal polynomial in two directions to describe its elastic deformation. By using the Rayleigh–Ritz method, the characteristic equation is derived to obtain natural frequencies and modal shapes of the whole spacecraft. Then the discrete rigid-flexible coupled dynamical equation of the spacecraft is obtained by using the Hamiltonian principle. The equation involves the coupling of the attitude maneuver, solar panels’ driving and vibration suppression. These dynamical behaviors are addressed by the rigid-flexible coupled mode for the first time in this paper. Based on the dynamical equation, the cooperative control scheme is designed by combing the proportional-differential and robust control method. Numerical results show the accuracy of the present modelling method and the validation of the control strategy. The modal analysis implies the complex rigid-flexible coupled characteristic between the central platform and flexible solar panels. The proposed control scheme can maintain the attitude stability while solar panels are being driven, as well as the vibration suppression of flexible solar panels.

Robust Control Design for Kaplan Turbine

The paper presents the dynamic model of a Kaplan turbine coupled to a DC generator, which is part of the H112D didactic system. A robust controller is designed using two different techniques: H ∞ mixed sensitivity and Quantitative feedback Theory (QFT). The robustness of the controller was analysed with three indicators: analysis of parameter uncertainties, transient response given a variable reference signal and robustness against disturbances.

Adaptive robust control design to enhance smart grid power system stabilization using wind characteristics in Indonesia

<span lang="EN-US">This paper is interested to study power system stability in smart grid power system using wind characteristic in south of Yogyakarta, Indonesia. To overcome the intermittent of wind characteristics, this paper presents adaptive robust control design to enhance power system stabilization. The online identification system is used in this research, which updated whenever the estimated model mismatch exceeds predetermined bounds. Then genetic algorithm (GA) is applied to re-tune parameters controller based on the estimated model. The structure of controller is proportional integral (PI) controller due to the most applicable in industry, simple structure, low cost and high reliability. Robustness of controller is guaranteed by taking system uncertainties into consideration. The performance of the proposed controller has been carried out in a hybrid wind-diesel power system in comparison with previous work controller. Simulation results confirm that damping effect of the proposed controllers are much better that of the conventional controllers against various operating.</span>

Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust H∞ is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker.