Control Systems and State Estimation
This book is about state estimation of control systems, nonlinear control systems in particular. Control systems are dynamic systems and exist in engineering, physical sciences as well as in social sciences. The earliest, somehow commonly recognised control system could be traced back to James Watt’s flyball governor in 1769. The study on control systems, analysis and system design, has been continuously developed ever since. Until the mid of last century the control systems under investigation had been single-input-single-output (SISO), time-invariant systems and were mainly deterministic and of lumped parameters. The approaches used were of frequency domain nature, so-called classical approaches. In classical control approaches, control system’s dynamic behaviour is represented by transfer functions. Rapid developments and needs in aerospace engineering in the 1950s and 1960s greatly drove the development of control system theory and design methodology, particularly in the state-space approach that is powerful towards multi-input-multi-output (MIMO) systems. State-space models are used to describe the dynamic changes of the control system. In the state-space approach, the system states determine how the system is dynamically evolving. Therefore, by knowing the system states one can know the system’s properties and by changing the trajectory of system states successfully with specifically designed controllers one can achieve the objectives for a certain control system.
- Banavar RN, Speyer JL (1991) A linear-quadratic game approach to estimation and smoothing. In: Proceedings of the 1991 American control conference. IEEE, pp 2818–2822Google Scholar
- Grewal M, Andrews A (2001) Kalman filtering: theory and practice using matlab. Wiley, New JerseyGoogle Scholar
- Gu D-W, Petkov PH, Konstantinov MM (2014) Robust control design with MATLAB®. Springer Science & Business Media, BerlinGoogle Scholar
- Kolmogorov AN, Doyle WL, Selin I (1962) Interpolation and extrapolation of stationary random sequences. Bulletin of academic sciences, Mathematics series, USSR, vol 5, 1941. Translation by W. Doyle and J. Selin, RM-3090-PR, Rand Corporation, Santa Monica, CaliforniaGoogle Scholar
- McGee LA, Schmidt SF (1985) Discovery of the kalman filter as a practical tool for aerospace and industryGoogle Scholar
- Mracek CP, Clontier J, D’Souza CA (1996) A new technique for nonlinear estimation. In: Proceedings of the 1996 IEEE international conference on control applications. IEEE, pp 338–343Google Scholar