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

In this paper, a constrained optimization based approach is proposed for the control of inverter-fed induction motors. This approach has many distinguished features: the measurements are motor velocity and currents, the tracking of torque command is optimized, the switching signal of inverter is determined directly from the feedback signals. This approach may be viewed as an improved SVM (space vector modulation) method, but instead of the tracking of a voltage command we track the current and flux commands directly which is equivalent to the tracking of torque and flux commands. This method greatly simplifies the control structure and computation complexity, and the optimality is guaranteed even in the overmodulation region.

A constrained optimization based torque control approach for inverter-fed induction motors

In networked control systems, the data needs to be quantized for transmission over a limited bandwidth communication channel. We consider a dynamic quantizer in a quantized feedback control system. In this system, the quantizer parameters and input are optimized on-line by using the model predictive control to achieve the optimal control performance. In our method, the system constraints explicitly considered. The effectiveness of the proposed method is verified through simulation and experimental results.

Optimal dynamic quantizer and input in quantized feedback control system

This paper addresses quantization of control systems in which the state is quantized via a quantizer. In addition, some constraints imposed on input/output or state are also considered. The resolution of the quantizer is optimized by means of the model predictive control (MPC) to improve the control performance. The effectiveness of the proposed method is verified by some simulations.

http://ieeexplore.ieee.org/iel5/6045373/6060171/06060735.pdf

On quantized feedback control using model predictive control

In this paper, we propose a new method to design discontinuous stabilizing controllers for chained systems. The proposed controllers yield exponential convergence of the states to the origin. In our method, the closed loop system of a chained system is formulated by a linear differential equation with a regular singular point at z=0. It is shown that the controller design method proposed by Astolfi, in which /spl sigma/ process is used for a coordinate transformation, is included in our method as a special case where the linear differential equation with a regular singular point at z=0 is limited to its subclass, namely an Euler differential equation. This method can be easily extended to apply to multiple chained systems.

A new method to design discontinuous stabilizing controllers for chained systems

In this paper, a gain-scheduling controller for automotive vehicles is proposed. This controller is to protect the vehicle from spin and realize the improved cornering performance. Since the vehicle unstabilization is mainly caused by the cornering force saturation of rear tire. This saturation is considered explicity in design by using LPV modelling method. And a gain-scheduling controller is designed based on linear matrix inequality (LMI) approach. Simulation shows that the vehicle stability performance is improved significantly as compared with robust control based on linearized model.

https://www.jstage.jst.go.jp/article/kikaic1979/73/729/73_729_1285/_pdf

Kang-Zhi Liu

Papers

A constrained optimization based torque control approach for inverter-fed induction motors

Optimal dynamic quantizer and input in quantized feedback control system

On quantized feedback control using model predictive control

A new method to design discontinuous stabilizing controllers for chained systems

A Gain-Scheduling Control Method for Automotive Vehicle Stabilization