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.

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.

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>

Wireless Charging Efficiency Optimization Method Based on Adaptive Robust Control

This paper designs, analyzes and optimizes the electric vehicle wireless charging system and its control method. Compared with the traditional plug-in conduction charging, wireless charging is more convenient, safe, reliable and has better environmental adaptability. Inductive power transmission technology (IPT) is the main technology for wireless charging of electric vehicles and plug-in hybrid vehicles. Based on the fundamental approximation method, the critical self inductance of the primary or secondary coil is derived in this paper. This critical value reflects the power transmission capacity and control performance of the two chargers when they are interoperable. Compared with the non integrated structure, the magnetically integrated LCC compensated wireless charging system can transmit the same power with smaller compensation inductance. Based on the voltage dependent dynamic state space model, four working modes of magnetically integrated LCC compensated wireless charging system are studied in this paper. Simulation and experimental results show that the voltage dependent equivalent circuit model can be effectively applied to the basic characteristic analysis of wireless charging system. The voltage dependent dynamic state space model is more accurate and realistic. In addition to reflecting the basic characteristics of the wireless charging system, it can also reflect the working mode of the wireless charging system.

Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization

Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.

Nonlinear Cutterhead Pose Control of Large-Diameter Slurry Shields in Complicated Stratum

To replace cutterhead worn tools conveniently or get rid of shield’s jamming effectively in complicated stratum, a new nonlinear cutterhead pose control system of large-diameter slurry shields is especially designed. High precision cutterhead pose control of large-diameter slurry shields is hardly achieved due to the uncertain load force and mass. A nonlinear controller constructed by adaptive robust control based on sliding mode is designed for this parallel mechanism, which includes a special adaptation law to compensate for the uncertainties. The stability of the whole closed loop system is verified based on Lyapunov theory. And the validity of the proposed strategy is proved by Simulink and AMESim co-simulation. The simulation results show that not only in control accuracy but also in parameter uncertainty, the designed nonlinear cutterhead pose control is effective.