<![CDATA[ IEEE Transactions on Automatic Control - new TOC ]]>
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TOC Alert for Publication# 9 2016April 28<![CDATA[Table of Contents]]>615C1C453<![CDATA[IEEE Transactions on Automatic Control publication information]]>615C2C237<![CDATA[Scanning the Issue<formula formulatype="inline"> <img src="/images/tex/697.gif" alt="^{\ast }"> </formula>]]>6151141114248<![CDATA[<inline-formula> <img src="/images/tex/692.gif" alt="L_{1}"> </inline-formula> Discretization for Sampled-Data Controller Synthesis via Piecewise Linear Approximation]]>$L_{1}$ optimal controller synthesis problem of sampled-data systems, which is the problem of minimizing the $L_{infty}$-induced norm of sampled-data systems. We apply fast-lifting on the top of the lifting technique, by which the sampling interval $[0,h)$ is divided into $M$ subintervals with an equal width. The signals on each subinterval are then approximated by linear functions by introducing two types of ‘linearizing operators’ for input and output, which leads to piecewise linear approximation of sampled-data systems. By using the arguments of preadjoint operators, we provide an important inequality that forms a theoretical basis for tackling the $L_{1}$ optimal controller synthesis problem of sampled-data systems more efficiently than the conventional method. More precisely, a mathematical basis for the piecewise linear approximation method associated with the convergence rate is shown through this inequality, and this suggests that the piecewise linear approximation method may drastically outperform the conventional method in the $L_{1}$ optimal controller synthesis problem of sampled-data systems. We then provide a discretization procedure of sampled-data systems by which the $L_{1}$ optimal controller synthesis problem is converted to the discrete-time $l_{1}$ optimal controller synthe-
is problem. Finally, effectiveness of the proposed method is demonstrated through a numerical example.]]>61511431157655<![CDATA[Optimal Steering of a Linear Stochastic System to a Final Probability Distribution, Part I]]>nonlinearly coupled through their boundary values. This result, given its attractive algorithmic nature, appears to have several potential applications such as to quality control, control of industrial processes, as well as to active control of nanomechanical systems and molecular cooling. The problem to steer a diffusion process between end-point marginals has a long history (Schrödinger bridges) and the present case of steering a linear stochastic system constitutes such a Schrödinger bridge for possibly degenerate diffusions. Our results provide the first implementable form of the optimal control for a general Gauss-Markov process. Illustrative examples are provided for steering inertial particles and for “cooling” a stochastic oscillator. A final result establishes directly the property of Schrödinger bridges as the most likely random evolution between given marginals to the present context of linear stochastic systems. A second part to this work, that is to appear as part II, addresses the general situation where the stochastic excitation enters through channels that may differ from those used to control.]]>61511581169665<![CDATA[Optimal Steering of a Linear Stochastic System to a Final Probability Distribution, Part II]]>feasibility for both problems. For the finite-horizon case, provided the system is controllable, we prove that without any restriction on the directionality of the stochastic disturbance it is always possible to steer the state to any arbitrary Gaussian distribution over any specified finite time-interval. For the stationary infinite horizon case, it is not always possible to maintain the state at an arbitrary Gaussian distribution through constant state-feedback. It is shown that covariances of admissible stationary Gaussian distributions are characterized by a certain Lyapunov-like equation and, in fact, they coincide with the class of stationary state covariances that can be attained by a suitable stationary colored noise as input. We finally address the question of how to compute suitable controls numerically. We present an alternative to solving the system of coupled Riccati equations, by expressing the optimal controls in the form of solutions to (convex) semi-definite programs for both cases. We conclude with an example to steer the state covariance of the distribution of inertial -
articles to an admissible stationary Gaussian distribution over a finite interval, to be maintained at that stationary distribution thereafter by constant-gain state-feedback control.]]>61511701180603<![CDATA[On the Stabilizability of Discrete-Time Switched Linear Systems: Novel Conditions and Comparisons]]>61511811193562<![CDATA[Stability Margins in Adaptive Mixing Control Via a Lyapunov-Based Switching Criterion]]>61511941207916<![CDATA[Backstepping Control Under Multi-Rate Sampling]]>61512081222894<![CDATA[Port-Hamiltonian Systems in Adaptive and Learning Control: A Survey]]>61512231238650<![CDATA[Synthesis of Maximally Permissive Supervisors for Partially-Observed Discrete-Event Systems]]>615123912541173<![CDATA[Bearing Rigidity and Almost Global Bearing-Only Formation Stabilization]]>61512551268700<![CDATA[A Graph Laplacian Approach to Coordinate-Free Formation Stabilization for Directed Networks]]>61512691280960<![CDATA[MIMO Control Over Additive White Noise Channels: Stabilization and Tracking by LTI Controllers]]>61512811296659<![CDATA[A Unifying Framework for Robust Synchronization of Heterogeneous Networks via Integral Quadratic Constraints]]>61512971309874<![CDATA[Fault Detection Filtering for Nonlinear Switched Stochastic Systems]]>${mathcal H}_{infty}$ error performance. Then, the corresponding solvability condition for the fault detection fuzzy filter is also established by the linearization procedure technique. Finally, simulation has been presented to show the effectiveness of the proposed fault detection technique.]]>61513101315307<![CDATA[Convergence and Stability of a Constrained Partition-Based Moving Horizon Estimator]]>61513161321223<![CDATA[Energy-Efficient Data Forwarding for State Estimation in Multi-Hop Wireless Sensor Networks]]>61513221327386<![CDATA[Randomized Control Strategies Under Arbitrary External Noise]]>unknown but bounded deterministic real sequence is an example of such a noise. In the case of a finite set of observations, we propose two procedures for computing data-based confidence regions for unknown parameters of the plant. They could be used in adaptive control schemes. The first procedure is of the stochastic approximation type, while the second one is developed in the general framework of “counting of leave-out sign-dominant correlation regions” (LSCR), which returns confidence regions that are guaranteed to contain the true parameters with a prescribed probability. If the number of observations increases infinitely, we propose the combined procedure for computing confidence regions which shrink to the true parameters asymptotically. The theoretical results are illustrated via a simulation example with a nonminimum-phase second-order plant.]]>61513281333466<![CDATA[Sufficient Lie Algebraic Conditions for Sampled-Data Feedback Stabilizability of Affine in the Control Nonlinear Systems]]>61513341339163<![CDATA[Minimal Conjunctive Normal Expression of Continuous Piecewise Affine Functions]]>61513401345178<![CDATA[On the Kalman-Yakubovich-Popov Lemma for Positive Systems]]>$m$ nonzero entries above the diagonal is negative semi-definite if and only if it can be written as a sum of $m$ negative semi-definite matrices, each of which has only four nonzero entries. This is useful in the context large-scale optimization.]]>61513461349111<![CDATA[Non-Minimal Order Model of Mechanical Systems With Redundant Constraints for Simulations and Controls]]>$bar{M}(q)$ is always positive definite even at singular configurations; ii) matrix $dot{bar{M}}-2bar{C}$ is skew symmetric, where all nonlinear terms are lumped into vector $bar{C}(q,dot{q})dot{q}$ after elimination of constraint forces. Eigenvalue analysis shows that the condition number of the constraint mass matrix can be minimized upon adequate selection of a scalar parameter called “virtual mass” thereby reducing the sensitivity to round-off errors in numerical computation. It follows by derivation of two oblique projection matrices for computation of constraint forces and actuation forces. It is shown that projection-based model allows feedback control of dependent coordinates which, unlike reduced-order dependent coordinates, uniquely define spatial configuration of constrained systems.]]>61513501355599<![CDATA[Converse Barrier Certificate Theorems]]>61513561361449<![CDATA[Switching Signal Estimator Design for a Class of Elementary Systems]]>61513621367272<![CDATA[Necessary and Sufficient Conditions for Global External Stochastic Stabilization of Linear Systems With Input Saturation]]>61513681372322<![CDATA[Robust Adaptive Controller Combined With a Linear Quadratic Regulator Based on Kalman Filtering]]>$(RMRAC)$ and a Linear Quadratic Regulator based on Kalman Filtering $(LQR_{KF})$ to obtain a high performance and robust control system. The adaptive portion of the controller deals with system uncertainties, while the optimum scheme, aided by a Kalman Filter, is designed to deal with harmonically related system disturbances. A proof of stability is presented in addition to a numerical example of the combined $RMRAC-LQR_{KF}$ controller to show the effectiveness of this new control approach.]]>61513731378326<![CDATA[Explicit Reference Governor for Constrained Nonlinear Systems]]>61513791384604<![CDATA[Hybrid Control of a Bioreactor With Quantized Measurements]]>615138513901940<![CDATA[Heterogeneous Multi-Agent Systems: Reduced-Order Synchronization and Geometry]]>61513911396261<![CDATA[Efficient Rate Allocation in Wireless Networks Under Incomplete Information]]>61513971402159<![CDATA[Reach Control Problem for Linear Differential Inclusion Systems on Simplices]]>61514031408322<![CDATA[A Note on Delay Coordinates for Locally Observable Analytic Systems]]>61514091412103<![CDATA[Stability and Disturbance Attenuation for Markov Jump Linear Systems with Time-Varying Transition Probabilities]]>a priori known or unknown. Necessary and sufficient conditions for uniform stochastic stability and uniform stochastic disturbance attenuation are reported. In both cases, conditions are expressed as a set of finite-dimensional linear matrix inequalities that can be solved efficiently.]]>61514131418166<![CDATA[Introducing IEEE Collabratec]]>615141914191908<![CDATA[IEEE Access]]>615142014201020<![CDATA[IEEE Control Systems Society Information]]>615C3C349