William S. Levine

Bio: William S. Levine is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Optimal control & Model predictive control. The author has an hindex of 29, co-authored 129 publications receiving 6008 citations. Previous affiliations of William S. Levine include Massachusetts Institute of Technology.

The Control Handbook

Abstract: FUNDAMENTALS OF CONTROL Mathematical Foundations Ordinary Linear Differential and Difference Equations, B.P. Lathi The Fourier, Laplace, and Z-Transforms, E.W. Kamen Matrices and Linear Algebra, B.W. Dickinson Complex Variables, C.W. Gray Models for Dynamical Systems Standard Mathematical Models Input-Output Models, W.S. Levine State Space, J. Gillis Graphical Models Block Diagrams, D.K. Frederick and C.M. Close Signal Flow Graphs, N.S. Nise Determining Models Modeling from Physical Principles, F.E. Cellier, H. Elmqvist, and M. Otter System Identification When Noise is Negligible, W.S. Levine Analysis and Design Methods for Continuous-Time Systems Analysis Methods Time Response of Linear Time-Invariant Systems, R.T. Stefani Controllability and Observability, W.A. Wolovich Stability Tests The Routh-Hurwitz Stability Criterion, R.H. Bishop and R.C. Dorf The Nyquist Stability Test, C.E. Rohrs Discrete-Time and Sampled-Data Stability Tests, M. Mansour Gain Margin and Phase Margin, R.T. Stefani Design Methods Specification of Control Systems, J.-S. Yang and W.S. Levine Design Using Performance Indices, R.C. Dorf and R.H. Bishop Nyquist, Bode, and Nichols Plots, J.J. D'Azzo and C.H. Houpis The Root Locus Plot, W.S. Levine PID Control, K.J. Astrom and T. Hagglund State Space - Pole Placement, K. Ogata Internal Model Control, R.D. Braatz Time-Delay Compensation - Smith Predictor and Its Modifications, Z.J. Palmor Digital Control Discrete-Time Systems, M.S. Santina and A.R. Stubberud Sampled-Data Systems, A. Feuer and G.C. Goodwin Discrete-Time Equivalents to Continuous-Time Systems, M.S. Santina and A.R. Stubberud Design Methods for Discrete-Time Linear Time-Invariant Systems, M.S. Santina and A.R. Stubberud Quantization Effects, M.S. Santina and A.R. Stubberud Sample-Rate Selection, M.S. Santina and A.R. Stubberud Real Time Software for Implementation of Digital Control, D.M. Auslander, J.R. Ridgely, and J. Jones Programmable Controllers, G. Olsson Analysis and Design Methods for Nonlinear Systems Analysis Methods The Describing Function Method, D.P. Atherton The Phase Plane Method, D.P. Atherton Design Methods Dealing with Actuator Saturation, R.H. Middleton Bumpless Transfer, A. Ahlen and S.F. Graebe Linearization and Gain-Scheduling, J.S. Shamma Software for Control System Analysis and Design Numerical and Computational Issues in Linear Control and System Theory, R.V. Patel, A.J. Laub, and P.M. Van Dooren Software for Modeling and Simulating Control Systems, M. Otter and F.E. Cellier Computer-Aided Control Systems Design, C.M. Rimvall and C.P Jobling ADVANCED METHODS OF CONTROL Analysis Methods for MIMO Linear Systems Multivariable Poles, Zeros, and Pole/Zero Cancellations, J. Douglas and M. Athans Fundamentals of Linear Time-Varying Systems, E.W. Kamen Geometric Theory of Linear Systems, F. Hamano Polynomial and Matrix Fraction Descriptions, D.F. Delchamps Robustness Analysis with Real Parametric Uncertainty, R. Tempo and F. Blanchini MIMO Frequency Response Analysis and the Singular Value Decomposition, S.D. Patek and M. Athans Stability Robustness to Unstructured Uncertainty for Linear Time-Invariant Systems, A. Chao and M. Athans Tradeoffs and Limitations in Feedback Systems, D.P. Looze and J.S. Freudenberg Modeling Deterministic Uncertainty, J. Raisch and B.A. Francis The Use of Multivariate Statistics in Process Control, M.J. Piovoso and K.A. Kosanovich Kalman Filter and Observers Linear Systems and White Noise, W.S. Levine Kalman Filter, M. Athans Riccati Equations and Their Solution, V. Kucera Observers, B. Friedland Design Methods for MIMO LTI Systems Eigenstructure Assignment, K.M. Sobel, E.Y. Shapiro, and A.N. Andry, Jr. Linear Quadratic Regulator Control, L. Lublin and M. Athans H2 (LQG) and H8 Control, L. Lublin, S. Grocott, and M. Athens Robust Control: Theory, Computation, and Design, M. Dahleh The Structured Singular Value (m) Framework, G.J. Balas and A. Packard Algebraic Design Methods, V. Kucera Quantitative Feedback Theory (QFT) Technique, C.H. Houpis The Inverse Nyquist Array and Characteristic Locus Design Methods, N. Munro and J.M. Edmunds Robust Servomechanism Problem, E.J. Davidson Numerical Optimization-Based Design, V. Balakrishnan and A.L. Tits Optimal Control, F.L. Lewis Decentralized Control, M.E. Sezer and D.D. Siljak Decoupling, T. Williams and P.J. Antsaklis Predictive Control, A.W. Pike, M.J. Grimble, M.A. Johnson, A.W. Ordys, and S. Shakoor Adaptive Control Automatic Tuning of PID Controllers, T. Hagglund and K.J. Astrom Self-Tuning Control, D.W. Clarke Model Reference Adaptive Control, P.A. Ioannou Analysis and Design of Nonlinear Systems Analysis Methods The Lie Bracket and Control, V. Jurdjevic Two Time Scale and Averaging Methods, H.K. Khalil Volterra and Fliess Series Expansion for Nonlinear Systems, F. Lamnabi-Lagarrique Stability Lyapunov Stability, H.K. Khalil Input-Output Stability, A.R. Teel, T.T. Georgiou, L. Praly, and E. Sontag Design Methods Feedback Linearization of Nonlinear Systems, A. Isidori and M.D. Di Benedetto Nonlinear Zero Dynamics, A. Isidori and C.I. Byrnes Nonlinear Output Regulation and Tracking, A. Isidori Lyapunov Design, R.A. Freeman and P.V. Kokotovic Variable Structure and Sliding Mode Controller Design, R.A. De Carlo, S.H. Zak, and S.V. Drakunov Control of Bifurcation and Chaos, E.H. Abed, H.O. Wang, and A. Tesi Open-Loop Control Using Oscillatory Inputs, J. Baillieul and B. Lehman Adaptive Nonlinear Control, M. Krstic and P.V. Kokotovic Intelligent Control, K.M. Passino Fuzzy Control, K.M. Passino and S. Yurkovich Neural Control, J.A. Farrell System Identification System Identification, L. Ljung Stochastic Control Discrete Time Markov Processes, A. Schwartz Stochastic Differential Equations, J.A. Gubner Linear Stochastic Input-Output Models, T. Soderstrom Minimum Variance Control, M.R. Katebi and A.W. Ordys Dynamic Programming, P.R. Kumar Stability of Stochastic Systems, K.O. Loparo and X. Feng Stochastic Adaptive Control, T.E. Duncan and B. Pasik-Duncan Control of Distributed Parameter Systems Controllability of Thin Elastic Beams and Plates, J.E. Lagnese and G. Leugering Control of the Heat Equation, T.I. Seidman Observability of Linear Distributed Parameter Systems, D.L. Russell APPLICATIONS OF CONTROL Process Control Water Level Control for the Toilet Tank: A Historical Perspective, B.G. Coury Temperature Control in Large Buildings, C.C. Federspiel and J.E. Seem Control of pH, F.G. Shinskey Control of the Pulp and Paper-Making Process, W.L. Bialkowski Control for Advanced Semiconductor Device Manufacturing: A Case History, T. Kailath, C. Schaper, Y. Cho, P. Gyugyi, S. Norman, P. Park, S. Boyd, G. Franklin, K. Saraswat, M. Modehi, and C. Davis Mechanical Control Systems Automotive Control Systems Engine Control, J.A. Cook, J.W. Grizzle, and J. Sun Adaptive Automotive Speed Control, M.K. Liubakka, D.S. Rhode, J.R. Winkelman, and P.V. Kokotovic Aerospace Controls Flight Control of Piloted Aircraft, M. Pachter and C.H. Houpis Spacecraft Attitude Control, V.T. Coppola and N.H. McClamroch Control of Flexible Space Structures, S.M. Joshi and A.G. Kelkar Line-of-Sight Pointing and Stabilization Control Systems, D.A. Haessig Control of Robots and Manipulators Motion Control of Robotic Manipulators, M.W. Spong Force Control of Robotic Manipulators, J. De Schutter and H. Bruyninckx Control of Nonholonomic Systems, J.T.-Y. Wen Miscellaneous Mechanical Control Systems Friction Compensation, B. Armstrong-Helouvry and C. Canudas de Wit Motion Control Systems, J. Tal Ultra-High Precision Control, T.R. Kurfess and H. Jenkins Robust Control of a Compact Disc Mechanism, M. Steinbuch, G. Schootstra, and O.H. Bosgra Electrical and Electronic Control Systems Power Electronic Controls Dynamic Modeling and Control in Power Electronics, G.C. Verghese Motion Control with Electric Motors by Input-Output Linearization, D.G. Taylor Control of Electric Generators, T. Jahns and R.W. De Doncker Control of Electrical Power Control of Electrical Power Generating Plants, H.G. Kwatny and C. Maffezzoni Control of Power Transmission, J.J. Paserba, J.J. Sanchez-Gasca, and E.V. Larsen Control Systems Including Humans Human-in-the-Loop Control, R.A. Hess Index

On the determination of the optimal constant output feedback gains for linear multivariable systems

Abstract: The optimal control of linear time-invariant systems with respect to a quadratic performance criterion is discussed. The problem is posed with the additional constraint that the control vector u(t) is a linear time-invariant function of the output vector y(t) (u(t) = -Fy(t)) rather than of the state vector x(t) . The performance criterion is then averaged, and algebraic necessary conditions for a minimizing F\ast are found. In addition, an algorithm for computing F\ast is presented.

On the optimal error regulation of a string of moving vehicles

Abstract: This paper uses the theory of optimal control to design an optimal linear feedback system which regulates the position and velocity of every vehicle in a densely packed string of high-speed moving vehicles. In addition to the general theoretical formulation and solution of the optimization problem, analog computer simulation results are presented for the case of a string of three vehicles.

Optimal feedback control as a theory of motor coordination.

Abstract: A central problem in motor control is understanding how the many biomechanical degrees of freedom are coordinated to achieve a common goal. An especially puzzling aspect of coordination is that behavioral goals are achieved reliably and repeatedly with movements rarely reproducible in their detail. Existing theoretical frameworks emphasize either goal achievement or the richness of motor variability, but fail to reconcile the two. Here we propose an alternative theory based on stochastic optimal feedback control. We show that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions. This strategy does not enforce a desired trajectory, but uses feedback more intelligently, correcting only those deviations that interfere with task goals. From this framework, task-constrained variability, goal-directed corrections, motor synergies, controlled parameters, simplifying rules and discrete coordination modes emerge naturally. We present experimental results from a range of motor tasks to support this theory.

The interacting multiple model algorithm for systems with Markovian switching coefficients

Abstract: An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients. >

The representing brain: Neural correlates of motor intention and imagery

Abstract: This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a macroscopic level, based on global aspects of the action (the duration and amount of effort involved) and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas (independently of other tasks such as object recognition). Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization.

Nonlinear Control Systems

Abstract: Definition Nonlinear control systems are those control systems where nonlinearity plays a significant role, either in the controlled process (plant) or in the controller itself. Nonlinear plants arise naturally in numerous engineering and natural systems, including mechanical and biological systems, aerospace and automotive control, industrial process control, and many others. Nonlinear control theory is concerned with the analysis and design of nonlinear control systems. It is closely related to nonlinear systems theory in general, which provides its basic analysis tools. Characteristics Numerous methods and approaches exist for the analysis and design of nonlinear control systems. A brief and informal description of some prominent ones is given next. Full details may be found in the textbooks [1-6], and in the Control Handbook [7]. Most of the theory and practice focuses on feedback control. A typical layout of a feedback control system is shown in Figure 1.