1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

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Essentials of Robust Control

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Linear Robust Control

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

A lifting technique for linear periodic systems with applications to sampled-data control

Data dropout in networked control systems (NCSs) is unavoidable. The desired performance may not be achieved if the data dropout is not considered. Therefore, a controller in the NCS needs to be designed which takes the data dropout into account. In this paper, the data dropout is assumed to follow a stochastic process. For the NCS, a model predictive control (MPC) method is proposed for the design of an optimal input sequence. The effectiveness of the proposed method is verified through simulations and experiments.

Optimal control for networked control systems with stochastic data dropout

In this paper, a nonlinear output feedback control method is proposed for a magnetic bearing system which has a strong nonlinearity in the magnetic actuator. The basic idea is to make full use of the feature that the dynamics part is linear and hence can be stabilized by output feedback when the output of the actuator is regarded as a virtual input. Then it is shown that the backstepping and completing square techniques enable the construction of the real input using measured output only.

A nonlinear output feedback control method for magnetic bearing systems

In this paper, a constrained optimization based approach is proposed for the switching control of inverters. This method achieves the best tracking performance of three phase currents, or equivalently the best tracking of torque command. This approach may be viewed as an improved SVM (space vector modulation) method. Detailed performance analysis proves that in the overmodulation region, a relative ratio of improvement up to 15.4% is achieved over SVM. Meanwhile, the computation complexity is the same as SVM. Further, numerous simulations verify that the proposed approach outperforms both PWM and SVM in the overmodulation region.

Kang-Zhi Liu

Papers

Optimal control for networked control systems with stochastic data dropout

A nonlinear output feedback control method for magnetic bearing systems

A constrained optimization approach to the control of inverters

A Unified Analysis of Stability for Linear Regulator and Servomechanism Problems

An MPC-PI Approach for Buck DC-DC Converters and Its Implementation