<![CDATA[ IEEE Transactions on Control Systems Technology - new TOC ]]>
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TOC Alert for Publication# 87 2018February 19<![CDATA[Table of contents]]>262C1C4116<![CDATA[IEEE Transactions on Control Systems Technology publication information]]>262C2C287<![CDATA[2017 IEEE Transactions on Control Systems Technology Outstanding Paper Award]]>26238138168<![CDATA[Exhaust Pressure Estimation for Diesel Engines Equipped With Dual-Loop EGR and VGT]]>2623823923973<![CDATA[Online Energy Maximization of an Airborne Wind Energy Turbine in Simulated Periodic Flight]]>figure-8 motion of a buoyant airborne wind energy turbine. Crosswind figure-8 motion has the potential to increase average power generation compared with stationary flight. To achieve crosswind motion, we use a hierarchical control scheme, where a high-level controller adjusts the roll set-point trajectory and a lower-level motor controller tracks it. The optimal crosswind trajectory changes with both wind speed and the plant’s aerodynamic parameters. This creates a need for an optimal controller that adjusts the roll set-point trajectory both in response to wind speed variations and plant uncertainties. Adaptation is complicated by the facts that: 1) wind speed is difficult to measure accurately at high altitudes and 2) the use of an optimal roll set-point trajectory can induce instability if actual wind conditions are different from anticipated conditions. Building on these observations and the existing literature, this paper presents a controller that adapts the figure-8 trajectory in changing and uncertain wind conditions by fusing direct anemometry-based wind speed estimation with extremum seeking (ES). The fast anemometry-based estimation allows for quick set-point adjustments. The slow-converging ES adds a correction factor that can be used to account for uncertainties such as estimator bias or plant parameter uncertainties. In one simulation with real wind data, the proposed approach improves energy generation by 92% over a stationary controller and 40% over a similar controller based on anemometry-based speed estimation alone.]]>2623934034249<![CDATA[Nonlinear Model-Based Tracking Control of Underwater Vehicles With Three Degree-of-Freedom Fully Coupled Dynamical Plant Models: Theory and Experimental Evaluation]]>2624044141539<![CDATA[Dependability Analysis of Safety Critical Real-Time Systems by Using Petri Nets]]>2624154262641<![CDATA[Complex Process Monitoring Using KUCA With Application to Treatment of Waste Liquor]]>2624274382836<![CDATA[An Adaptive Online Co-Search Method With Distributed Samples for Dynamic Target Tracking]]>2624394512100<![CDATA[Resilient Corner-Based Vehicle Velocity Estimation]]>2624524622940<![CDATA[Unit Prediction Horizon Binary Search-Based Model Predictive Control of Full-Bridge DC–DC Converter]]>2624634741883<![CDATA[Robust Optimal Dispatch, Secondary, and Primary Reserve Allocation for Power Systems With Uncertain Load and Generation]]>2624754852686<![CDATA[Transmission of Signal Nonsmoothness and Transient Improvement in Add-On Servo Control]]>2624864961829<![CDATA[Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems]]>2624975062989<![CDATA[Real-Time Optimizing Control of an Experimental Crosswind Power Kite]]>2625075222875<![CDATA[Scheduled Controller Design for Systems With Varying Sensor Configurations: A Frequency-Domain Approach]]>a priori designed local linear time-invariant controllers, which are designed using frequency-domain loop-shaping techniques. Moreover, conditions on measured frequency response function data of the plant are provided under which the closed-loop system is input-to-state stable for arbitrarily fast parameter variations based on a generalized version of the circle criterion. By presenting a design and analysis framework for scheduled control systems that is easily implementable in existing control software, and does not require parametric plant models, this paper connects well to the industrial control practice. The effectiveness of the proposed scheduling technique, as a way to improve both transient and steady-state performance, is demonstrated by means of a case study which includes measurement results obtained from an industrial wafer stage system.]]>2625235342946<![CDATA[Event-Based Power/Performance-Aware Thermal Management for High-Density Microprocessors]]>2625355503693<![CDATA[GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks]]>2625515621975<![CDATA[Multicopter With Series Connected Propeller Drives]]>2625635742954<![CDATA[Prototyping of Concurrent Control Systems With Application of Petri Nets and Comparability Graphs]]>2625755862322<![CDATA[Sliding Mode Control of Underground Coal Gasification Energy Conversion Process]]>2625875982040<![CDATA[Energy Storage Operation for Voltage Control in Distribution Networks: A Receding Horizon Approach]]>2625996091647<![CDATA[Adaptive Proportional–Integral Clock Synchronization in Wireless Sensor Networks]]>2626106231501<![CDATA[Locomotion Control and Gait Planning of a Novel Hexapod Robot Using Biomimetic Neurons]]>2626246363605<![CDATA[Frequency-Domain Analysis of Robust Monotonic Convergence of Norm-Optimal Iterative Learning Control]]>2626376512982<![CDATA[D-Type ILC Based Dynamic Modeling and Norm Optimal ILC for High-Speed Trains]]>$lambda $ -norm convergence property of the D-type ILC may lead to unsafe operation of the HST since huge overshoot phenomenon of tracking errors in the iteration axis may occur. To address this problem, this paper presents a novel dynamic modeling and norm optimal iterative learning control (Dynamic modeling based NOILC) approach. By making full use of the valuable information data generated after each repetitive operation of the HST, a modified iterative learning recursive least squares algorithm is proposed to identify the unknown and time-varying even fast time-varying aerodynamical coefficients in the nonlinear train dynamic model. Then, based on this identified nonlinear train model, a norm optimal iterative learning control with consideration of security, punctuality, and traveling comfort will be designed. Through theoretical analysis, reliable 2-norm convergence of both the model identification error and the tracking control error can be guaranteed. Simulation and experimental studies further verify that the proposed approach achieves a significant improvement in tracking control precision and meanwhile obeys safety requirement.]]>2626526631313<![CDATA[Structured Modeling and Control of Adaptive Optics Systems]]>$H_{2}$ -norm optimal control framework. Based on the Kronecker structured system matrices and the sparse controller gain, the obtained dynamical controller has a linear execution complexity in the dimension of the turbulent phase, which is even lower than the standard matrix-vector multiplication method. Since the proposed method is a preliminary result, which cannot be directly used in a telescope today, its performance is demonstrated by numerical simulations only.]]>2626646741926<![CDATA[A Double Disturbance Observer Design for Compensation of Unknown Time Delay in a Wireless Motion Control System]]>2626756832096<![CDATA[Disturbance Observer-Based Robust Saturated Control for Spacecraft Proximity Maneuvers]]>2626846921065<![CDATA[Adaptive Control of a Flexible String System With Input Hysteresis]]>2626937001335<![CDATA[Nonlinear Adaptive Fault-Tolerant Quadrotor Altitude and Attitude Tracking With Multiple Actuator Faults]]>2627017071984<![CDATA[Optimal Swing Up and Stabilization Control for Inverted Pendulum via Stable Manifold Method]]>2627087151394<![CDATA[Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by Kalman Filtering]]>2627167232709<![CDATA[Adaptive Reference Model Predictive Control With Improved Performance for Voltage-Source Inverters]]>2627247311993<![CDATA[Adaptive Near-Optimal Compensation in Lossy Polyphase Power Systems]]>262732739627<![CDATA[Antiswing Control of Offshore Boom Cranes With Ship Roll Disturbances]]>262740747869<![CDATA[Controller Design of an Electric Power Steering System]]>2627487551848<![CDATA[Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots]]>2627567641900<![CDATA[Lyapunov Estimator for High-Speed Demodulation in Dynamic Mode Atomic Force Microscopy]]>2627657721211<![CDATA[IEEE Transactions on Control Systems Technology information for authors]]>262C3C3101