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TOC Alert for Publication# 87 2019April 18<![CDATA[Table of contents]]>273C1C4121<![CDATA[IEEE Transactions on Control Systems Technology publication information]]>273C2C290<![CDATA[Deadbeat Source Localization From Range-Only Measurements: A Robust Kernel-Based Approach]]>2739239334091<![CDATA[<inline-formula> <tex-math notation="LaTeX">$mathcal{L}_2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$mathcal{L}_{infty}$ </tex-math></inline-formula> Stability Analysis of Heterogeneous Traffic With Application to Parameter Optimization for the Control of Automated Vehicles]]>$mathcal {L}_infty $ string stability of the mixed traffic flow while considering the comfort of automated driving. The optimization strategy systematically leads to increased traffic flow stability. We show that very few automated vehicles are required to prevent the propagation of realistic disturbances.]]>2739349491622<![CDATA[Distributed Optimal Traffic Lights Design for Large-Scale Urban Networks]]>2739509632043<![CDATA[A Distributed Optimization Approach for Complete Vehicle Energy Management]]>a priori, while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc–dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52% by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.]]>2739649803725<![CDATA[4-D Flight Trajectory Tracking: A Receding Horizon Approach Integrating Feedback Linearization and Scenario Optimization]]>2739819962519<![CDATA[Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark]]>27399710112053<![CDATA[Distributed Robust Hierarchical Power Sharing Control of Grid-Connected Spatially Concentrated AC Microgrid]]>273101210221752<![CDATA[Point-Based Methodology to Monitor and Control Gene Regulatory Networks via Noisy Measurements]]>273102310352251<![CDATA[Reliable Data Fusion of Hierarchical Wireless Sensor Networks With Asynchronous Measurement for Greenhouse Monitoring]]>273103610462541<![CDATA[Attitude Control of a 2U Cubesat by Magnetic and Air Drag Torques]]>273104710592047<![CDATA[Fast Dual-Loop Nonlinear Receding Horizon Control for Energy Management in Hybrid Electric Vehicles]]>273106010702426<![CDATA[Design Tools for Electrochemical Supercapacitors Using Local Absolute Stability Theory]]>273107110832095<![CDATA[Nonlinear Observer for Tightly Coupled Integrated Inertial Navigation Aided by RTK-GNSS Measurements]]>$L_{1}$ -based RTK reference solution.]]>273108410993408<![CDATA[Tire-Stiffness and Vehicle-State Estimation Based on Noise-Adaptive Particle Filtering]]>273110011142189<![CDATA[Distributed Model Predictive Control for Cooperative and Flexible Vehicle Platooning]]>273111511282570<![CDATA[Disturbance-Observer-Based Control for Air Management of PEM Fuel Cell Systems via Sliding Mode Technique]]>273112911382394<![CDATA[Precise Angles-Only Navigation for Noncooperative Proximity Operation With Application to Tethered Space Robot]]>273113911501400<![CDATA[Achieving Self-Balancing by Design in Photovoltaic Energy Storage Systems]]>273115111642853<![CDATA[An Energy-Optimal Warm-Up Strategy for Li-Ion Batteries and Its Approximations]]>273116511801828<![CDATA[Erythropoietin Dose Optimization for Anemia in Chronic Kidney Disease Using Recursive Zone Model Predictive Control]]>273118111933257<![CDATA[An Optimized Real-Time Energy Management Strategy for the Power-Split Hybrid Electric Vehicles]]>273119412022459<![CDATA[Optimization Algorithms for Catching Data Manipulators in Power System Estimation Loops]]>273120312184163<![CDATA[Passivity Sliding Mode Control of Large-Scale Power Systems]]>27312191227897<![CDATA[Optimal Containment Control of Unknown Heterogeneous Systems With Active Leaders]]>273122812361852<![CDATA[New Framework for Optimal Current Sharing of Nonidentical Parallel Buck Converters]]>273123712432182<![CDATA[Quasi-Soft Variable Structure Control of Discrete-Time Systems With Input Saturation]]>27312441249515<![CDATA[Compatible Convex–Nonconvex Constrained QP-Based Dual Neural Networks for Motion Planning of Redundant Robot Manipulators]]>273125012581396<![CDATA[Robust Sliding Mode Control for Robots Driven by Compliant Actuators]]>273125912661608<![CDATA[Design and Experimental Assessment of an Active Fault-Tolerant LPV Vertical Dynamics Controller]]>273126712741205<![CDATA[Robust Residual Generator Synthesis for Uncertain LPV Systems Applied to Lateral Vehicle Dynamics]]>$mathcal {L}_{2}$ robust estimator synthesis problem, in which parameter uncertainty is handled via integral quadratic constraints. Optimization of the sensitivity ratios is integrated into the ensuing a semidefinite program. The resulting residual generator synthesis method achieves perfect decoupling when this is possible, and more generally improves performance compared with when arbitrary references are used, without the precomputation of an attainable reference as seen in other approaches. The effectiveness of this method is demonstrated in a vehicle lateral dynamics application and validated using experimental data.]]>27312751283952<![CDATA[Oscillatory Yaw Motion Control for Hydraulic Power Steering Articulated Vehicles Considering the Influence of Varying Bulk Modulus]]>273128412921741<![CDATA[Toward Real-Time Autonomous Target Area Protection: Theory and Implementation]]>$P$ , a single evader $E$ , and a stationary target $T$ . The goal of $P$ is to prevent $E$ from capturing $T$ , by intercepting $E$ as far away from $T$ as possible. An optimal solution to this problem, referred to as a command to optimal interception point (COIP), was proposed recently. This guidance law requires the positions of the agents involved. Typically, aerial sensors, such as GPS, used for obtaining these data may not always perform robustly on the field, thereby reducing the autonomy of the vehicles. The computational complexity of the expressions in the COIP law also makes it difficult for a real-time implementation. Here, the TGP is revisited and the optimal solution is reformulated to expressions that are suitable for autonomous systems with ranging sensors mounted on them. These expressions also allow for seamless real-time implementation in robotic hardware. The reformulation enables the optimal solution to be coded as a lookup table requiring minimal memory to further increase the speed of computations. An experimental setup with mobile robots is then used to validate the claims. The case of $T$ lying in $E$ ’s dominance region is considered -
lost game for $P$ . However, this is true only if $E$ plays optimally. If $E$ plays suboptimally $P$ stands a chance to win the game. This case, which has not been analyzed earlier, is also discussed in this brief, and an optimal strategy for $P$ is presented.]]>273129313001015<![CDATA[Output Voltage-Tracking Controller With Performance Recovery Property for DC/DC Boost Converters]]>273130113071202<![CDATA[Stratified Adaptive Finite-Time Tracking Control for Nonlinear Uncertain Generalized Vehicle Systems and Its Application]]>273130813162681<![CDATA[Online Fault Diagnosis in Industrial Processes Using Multimodel Exponential Discriminant Analysis Algorithm]]>273131713251094<![CDATA[Vision-Based Leader–Follower Formation Control of Multiagents With Visibility Constraints]]>273132613331401<![CDATA[Path-Following Control of an AUV: A Multiobjective Model Predictive Control Approach]]>27313341342890<![CDATA[Robust Control of a Cable From a Hyperbolic Partial Differential Equation Model]]>$mathcal H_{infty }$ -robust feedback control to this infinite dimensional system. The approach relies on Riccati equations to stabilize the system under measurement feedback when it is subjected to external disturbances. Henceforth, this article focuses on the construction of a standard linear infinite dimensional state space description of the cable under consideration before writing its approximation of finite dimension and studying the $mathcal H_{infty }$ feedback control of vibrations with partial observation of the state in both cases. The closed-loop system is numerically simulated to illustrate the effectiveness of the resulting control law.]]>273134313511066<![CDATA[Nonlinear Robust Control of Antilock Braking Systems Assisted by Active Suspensions for Automobile]]>273135213591394<![CDATA[Robust Cooperative Guidance Law for Simultaneous Arrival]]>273136013671598<![CDATA[Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA]]>273136813752946<![CDATA[Cascade Optimal Control for Tracking and Synchronization of a Multimotor Driving System]]>273137613841921<![CDATA[IEEE Transactions on Control Systems Technology information for authors]]>273C3C3117