Nonlinear Robust Control on Yaw Motion of a Variable-Speed Unmanned Aerial Helicopter under Multi-Source Disturbances

This paper studies the multi-source disturbances attenuation problem on the yaw motion of unmanned aerial helicopter with a variable-speed rotor. The yaw motion subsystem dominated by an electrically-driven tail rotor is firstly introduced, and its trajectory accuracy requires particularly close attention. To this end, we establish a fourth-order yaw error dynamic equation; subsequently, a nonlinear robust control scheme based on optimal H∞ principle is developed, consisting of laws of virtual functions, parameter estimation and a compensation signal. The novelty of this scheme lies in unifying the techniques to deal with the uncertain parameters, noise perturbations, actuator output fault and external airflow turbulence into a simple framework. Stability analysis guarantees that the yaw closed-loop system has the predefined performance of disturbance suppression in the sense of a finite L2-gain. Comparison results with the extended state observer based backstepping controller verify the effectiveness and superior performance of proposed scheme in an aircraft prototype.

Design method of tuned mass damper by LMI based robust control theory for seismic excitation

This paper presents a new design method based on a robust-control strategy in the form of a linear matrix inequality (LMI) approach for a passive tuned mass damper (TMD), which is one of the common passive-control devices for structural vibration control. To apply the robust control theory, we first present an equivalent expression that describes a passive TMD as an active TMD. Then, some LMI-based condition is derived that not only guarantees robust stability but also allows us to adjust the robust H¥ performance. In particular, this paper considers the transfer function from a seismic-wave input to structural responses. Unlike other methods, this method formulates the problem to be a convex optimization problem that ensures a global optimal solution and considers uncertainties of mass, damping, and stiffness of a structure for designing a TMD. Numerical example uses both a single-degree-of-freedom (SDOF) and 10DOF models, and seismic waves. The simulation results demonstrated that the TMD that is designed by the presented method has good control performance even if the structural model includes uncertainties, which are the modeling errors.

Adaptive Robust Position Control of Electro-Hydraulic Servo Systems with Large Uncertainties and Disturbances

In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.

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.

Adaptive Robust Control for Uncertain Systems via Data-Driven Learning

Although solving the robust control problem with offline manner has been studied, it is not easy to solve it using the online method, especially for uncertain systems. In this paper, a novel approach based on an online data-driven learning is suggested to address the robust control problem for uncertain systems. To this end, the robust control problem of uncertain systems is first transformed into an optimal problem of the nominal systems via selecting an appropriate value function that denotes the uncertainties, regulation, and control. Then, a data-driven learning framework is constructed, where Kronecker’s products and vectorization operations are used to reformulate the derived algebraic Riccati equation (ARE). To obtain the solution of this ARE, an adaptive learning law is designed; this helps to retain the convergence of the estimated solutions. The closed-loop system stability and convergence have been proved. Finally, simulations are given to illustrate the effectiveness of the method.

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.

Reliable Centralized Control of PMSM

In this paper, we introduce the development methodology of a reliable centralized control applied to a synchronous permanent magnet machine. The proposed system is nonlinear, we linearize around a point of application. The resulting model will then be used to reproduce the dynamic behavior of the machine for a reliable control. The controller is based on the standard h infinite to increase performance, reduce measurement noise, and to tolerate the outage of certain sensors. To illustrate the results, we made a comparison between a standard state feedback control and reliable h infinite robust control. The simulation results shows, that the system in case of technical placements poles loses classic performance in the presence of an outage, that the reliable centralized robust control remain satisfactory performance even in the presence of outage.