Author
Petar V. Kokotovic
Other affiliations: Washington State University, University of Illinois at Urbana–Champaign, University of California
Bio: Petar V. Kokotovic is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Nonlinear system & Adaptive control. The author has an hindex of 83, co-authored 354 publications receiving 40395 citations. Previous affiliations of Petar V. Kokotovic include Washington State University & University of Illinois at Urbana–Champaign.
Papers published on a yearly basis
Papers
More filters
01 Dec 1981
TL;DR: In this paper, the authors show that the weak observability assumption is crucial, that is when the parasitics are strongly observable the adaptive schemes are no longer robust, and that the addition of a low pass filter at the output makes the parasITics weakly observable and hence guarantees the robustness of the enlarged scheme.
Abstract: In a recent paper we have established that several adaptive schemes are robust with respect to modeling errors consisting of fast parasitics which are weakly observable in the plant output. We now show that the weak observability assumption is crucial, that is when the parasitics are strongly observable the adaptive schemes are no longer robust. However, the addition of a low pass filter at the output makes the parasitics weakly observable and hence guarantees the robustness of the enlarged scheme.
10 citations
TL;DR: In this paper, an extended circle criterion design is developed which eliminates the relative degree obstacle and relaxes the restrictions on the zero dynamics by using the Popov multiplier, which also reduces controller complexity.
10 citations
14 Dec 1994
TL;DR: In this paper, the authors extend the concept of robust control Lyapunov function (RCLF) for nonlinear systems to the case of measurement feedback and explore conditions under which the existence of an RCLF is sufficient and/or necessary for robust global stabilizability via continuous static or dynamic measurement feedback.
Abstract: We extend the concept of a robust control Lyapunov function (RCLF) for nonlinear systems to the case of measurement feedback. We explore conditions under which the existence of an RCLF is sufficient and/or necessary for robust global stabilizability via continuous static or dynamic measurement feedback. >
10 citations
TL;DR: In this paper, an imbedding equation is used to solve linear regulator problems depending on a parameter α. From this equation, the optimum matrix K(α) is obtained for all α in [α 0, α 1 ].
Abstract: An imbedding equation is used to solve linear regulator problems depending on a parameter α. From this equation the optimum matrix K(\alpha) is obtained for all \alpha \in [\alpha_{0},\alpha_{1} ]. Two typical applications of this equation are discussed.
10 citations
01 Jan 1992
TL;DR: It is demonstrated how various integrator backstepping design procedures can be combined to produce new classes of stabilizing controllers for uncertain nonlinear systems.
Abstract: We review various integrator backstepping design procedures for generating globally stabilizing controllers for uncertain nonlinear systems. By way of a case study of a simple planar system, we demonstrate how these procedures can be combined to produce new classes of stabilizing controllers. We present eight different backstepping controllers for the planar system and compare their structural properties and simulated performance.
9 citations
Cited by
More filters
Book•
01 Jan 1994
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.
11,085 citations
02 Sep 2004
TL;DR: Free MATLAB toolbox YALMIP is introduced, developed initially to model SDPs and solve these by interfacing eternal solvers by making development of optimization problems in general, and control oriented SDP problems in particular, extremely simple.
Abstract: The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, developed initially to model SDPs and solve these by interfacing eternal solvers. The toolbox makes development of optimization problems in general, and control oriented SDP problems in particular, extremely simple. In fact, learning 3 YALMIP commands is enough for most users to model and solve the optimization problems
7,676 citations
15 Oct 1995
TL;DR: In this article, the authors present a model for dynamic control systems based on Adaptive Control System Design Steps (ACDS) with Adaptive Observers and Parameter Identifiers.
Abstract: 1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.
4,378 citations
Book•
26 Jun 2003
TL;DR: Preface, Notations 1.Introduction to Time-Delay Systems I.Robust Stability Analysis II.Input-output stability A.LMI and Quadratic Integral Inequalities Bibliography Index
Abstract: Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Index
4,200 citations
TL;DR: In this article, the authors proposed arbitrary-order robust exact differentiators with finite-time convergence, which can be used to keep accurate a given constraint and feature theoretically-infinite-frequency switching.
Abstract: Being a motion on a discontinuity set of a dynamic system, sliding mode is used to keep accurately a given constraint and features theoretically-infinite-frequency switching. Standard sliding modes provide for finite-time convergence, precise keeping of the constraint and robustness with respect to internal and external disturbances. Yet the relative degree of the constraint has to be 1 and a dangerous chattering effect is possible. Higher-order sliding modes preserve or generalize the main properties of the standard sliding mode and remove the above restrictions. r-Sliding mode realization provides for up to the rth order of sliding precision with respect to the sampling interval compared with the first order of the standard sliding mode. Such controllers require higher-order real-time derivatives of the outputs to be available. The lacking information is achieved by means of proposed arbitrary-order robust exact differentiators with finite-time convergence. These differentiators feature optimal asymptot...
2,954 citations