Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

An Initiative Learning Algorithm Based on System Uncertainty

Initiative-learning algorithms are characterized by and hence advantageousfor their independence of prior domain knowledge. Usually, their induced results could moreobjectively express the potential characteristics and patterns of information systems.Initiative-learning processes can be effectively conducted by system uncertainty, becauseuncertainty is an intrinsic common feature of and also an essential link between information systemsand their induced results. Obviously, the effectiveness of such initiative-learning framework isheavily dependent on the accuracy of system uncertainty measurements. Herein, a more reasonablemethod for measuring system uncertainty is developed based on rough set theory and the conception ofinformation entropy; then a new algorithm is developed on the bases of the new system uncertaintymeasurement and the Skowron's algorithm for mining prepositional default decision rules. Theproposed algorithm is typically initiative-learning. It is well adaptable to system uncertainty. Asshown by simulation experiments, its comprehensive performances are much better than those ofcongeneric algorithms.

Robust control of a class of non-affine nonlinear systems by state and output feedback

Robust control design is presented for a general class of uncertain non-affine nonlinear systems. The design employs feedback linearization, coupled with two high-gain observers: the first to estimate the feedback linearization error based on the full state information and the second to estimate the unmeasured states of the system when only the system output is available for feedback. All the signals in the closed loop are guaranteed to be uniformly ultimately bounded(UUB) and the output of the system is proven to converge to a small neighborhood of the origin. The proposed approach not only handles the difficulty in controlling non-affine nonlinear systems but also simplifies the stability analysis of the closed loop due to its linear control structure. Simulation results show the effectiveness of the approach.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Active disturbance rejection based load frequency control and voltage regulation in power systems

An active disturbance rejection controller （ADRC） is developed for load frequency control （LFC） and voltage regulation respectively in a power system. For LFC, the ADRC is constructed on a three-area interconnected power system. The control goal is to maintain the frequency at nominal value （60Hz in North America） and keep tie-line power flow at scheduled value. For voltage regulation, the ADRC is applied to a static var compensator （SVC） as a supplementary controller. It is utilized to maintain the voltages at nearby buses within the ANSI C84.1 limits （or ＋5% tolerance）. Particularly, an alternative ADRC with smaller controller gains than classic ADRC is originally designed on the SVC system. From power generation and transmission to its distribution, both voltage and frequency regulating systems are subject to large and small disturbances caused by sudden load changes, transmission faults, and equipment loss/malfunction etc. The simulation results and theoretical analyses demonstrate the effectiveness of the ADRCs in compensating the disturbances and achieving the control goals.