Showing papers in "IEEE Transactions on Control Systems and Technology in 2016"

Optimal DoS Attack Scheduling in Wireless Networked Control System

Abstract: Recently, many literature works have considered the security issues of wireless networked control system (WNCS). However, few works studied how the attacker should optimize its attack schedule in order to maximize the effect on the system performance due to the insufficiency of energy at the attacker side. This paper fills this gap from the aspect of control system performance. We consider the optimal jamming attack that maximizes the Linear Quadratic Gaussian (LQG) control cost function under energy constraint. After analyzing the properties of the cost function under an arbitrary attack schedule, we derive the optimal jamming attack schedule and the corresponding cost function. System stability under this optimal attack schedule is also considered. We further investigate the optimal attack schedule in a WNCS with multiple subsystems. Different examples are provided to demonstrate the effectiveness of the proposed optimal denial-of-service attack schedule.

Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids With Consensus-Based Distributed Voltage Control

Abstract: We propose a consensus-based distributed voltage control (DVC) that solves the problem of reactive power sharing in autonomous inverter-based microgrids with dominantly inductive power lines and arbitrary electrical topology. Opposed to other control strategies available thus far, the control presented here does guarantee a desired reactive power distribution in steady state while only requiring distributed communication among inverters, i.e., no central computing nor communication unit is needed. For inductive impedance loads and under the assumption of small phase angle differences between the output voltages of the inverters, we prove that the choice of the control parameters uniquely determines the corresponding equilibrium point of the closed-loop voltage and reactive power dynamics. In addition, for the case of uniform time constants of the power measurement filters, a necessary and sufficient condition for local exponential stability of that equilibrium point is given. The compatibility of the DVC with the usual frequency droop control for inverters is shown and the performance of the proposed DVC is compared with the usual voltage droop control via simulation of a microgrid based on the Conseil International des Grands Reseaux Electriques (CIGRE) benchmark medium voltage distribution network.

Fixed-Time Attitude Control for Rigid Spacecraft With Actuator Saturation and Faults

Abstract: This brief investigates the finite-time control problem associated with attitude stabilization of a rigid spacecraft subject to external disturbance, actuator faults, and input saturation. More specifically, a novel fixed-time sliding mode surface is developed, and the settling time of the defined surface is shown to be independent of the initial conditions of the system. Then, a finite-time controller is derived to guarantee that the closed-loop system is stable in the sense of the fixed-time concept. The actuator-magnitude constraints are rigorously enforced and the attitude of the rigid spacecraft converges to the equilibrium in a finite time even in the presence of external disturbances and actuator faults. Numerical simulations illustrate the spacecraft performance obtained using the proposed controller.

Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis

Abstract: This paper reports the final results of the predictive building control project OptiControl-II that encompassed seven months of model predictive control (MPC) of a fully occupied Swiss office building. First, this paper provides a comprehensive literature review of experimental building MPC studies. Second, we describe the chosen control setup and modeling, the main experimental results, as well as simulation-based comparisons of MPC to industry-standard control using the EnergyPlus simulation software. Third, the costs and benefits of building MPC for cases similar to the investigated building are analyzed. In the experiments, MPC controlled the building reliably and achieved a good comfort level. The simulations suggested a significantly improved control performance in terms of energy and comfort compared with the previously installed industry-standard control strategy. However, for similar buildings and with the tools currently available, the required initial investment is likely too high to justify the deployment in everyday building projects on the basis of operating cost savings alone. Nevertheless, development investments in an MPC building automation framework and a tool for modeling building thermal dynamics together with the increasing importance of demand response and rising energy prices may push the technology into the net benefit range.

Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles

Abstract: In this brief, an adaptive robust finite-time tracking control (ARFTTC) scheme for trajectory tracking of a fully actuated marine surface vehicle with unknown disturbances is proposed. A new finite-time disturbance observer is incorporated into the proposed finite-time tracking control (FTTC) structure that facilitates faster convergence and better robustness to disturbances. Hence, in the presence of unknown disturbances, the ARFTTC can cause tracking error to converge to zero in a finite time. Simulation studies and comprehensive comparisons with conventional backstepping technique demonstrate remarkable performance and superiority of the ARFTTC in terms of both tracking accuracy and robustness.

Integral Line-of-Sight Guidance and Control of Underactuated Marine Vehicles: Theory, Simulations, and Experiments

Abstract: This paper presents an extensive analysis of the integral line-of-sight (ILOS) guidance method for path-following tasks of underactuated marine vehicles, operating on and below the sea surface. It is shown that due to the embedded integral action, the guidance law makes the vessels follow straight lines by compensating for the drift effect of environmental disturbances, such as currents, wind, and waves. The ILOS guidance is first applied to a 2-D model of surface vessels that includes the underactauted sway dynamics of the vehicle as well as disturbances in the form of constant irrotational ocean currents and constant dynamic, attitude dependent, and forces. The actuated dynamics are not considered at this point. A Lyapunov closed-loop analysis yields explicit bounds on the guidance law gains to guarantee uniform global asymptotic stability (UGAS) and uniform local exponential stability (ULES). The complete kinematic and dynamic closed-loop system of the 3-D ILOS guidance law is analyzed in the following and hence extending the analysis to underactuated autonomous underwater vehicles (AUVs) for the 3-D straight-line path-following applications in the presence of constant irrotational ocean currents. The actuated surge, pitch, and yaw dynamics are included in the analysis where the closed-loop system forms a cascade, and the properties of UGAS and ULES are shown. The 3-D ILOS control system is a generalization of the 2-D ILOS guidance. Finally, results from simulations and experiments are presented to validate and illustrate the theoretical results, where the 2-D ILOS guidance is applied to the cooperative autonomous robotics towing system vehicle and light AUV.

Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

Abstract: Quality- or output-related fault detection has attracted much attention in recent years. Several approaches have been developed to solve this issue based on postprocessing schemes. However, further studies find that these methods gradually lose their functions when amplitudes of quality-unrelated faults increase; in addition, they still consume a relatively large amount of calculation load in practice. In this brief, we propose a new structure of preprocessing–modeling–postprocessing, within which modified orthogonal projections to latent structures (MOPLS) method is developed. Compared with the previous approaches, the new method significantly improves the performance of quality-related fault detection. In addition, it reduces the number of required latent variables, thus it has a quite lower computational load than the previous ones. A numerical example and the Tennessee Eastman process are used to verify the effectiveness of the proposed approach.

Stability Margin Improvement of Vehicular Platoon Considering Undirected Topology and Asymmetric Control

Abstract: The platooning of autonomous vehicles has the potential to significantly improve traffic capacity, enhance highway safety, and reduce fuel consumption. This paper studies the scalability limitations of large-scale vehicular platoons moving in rigid formation, and proposes two basic ways to improve stability margins, i.e., enlarging information topology and employing asymmetric control. A vehicular platoon is considered as a combination of four components: 1) node dynamics; 2) decentralized controller; 3) information flow topology; and 4) formation geometry. Tools, such as the algebraic graph theory and matrix factorization technique, are employed to model and analyze scalability limitations. The major findings include: 1) under linear identical decentralized controllers, the stability thresholds of control gains are explicitly established for platoons under undirected topologies. It is proved that the stability margins decay to zero as the platoon size increases unless there is a large number of following vehicles pinned to the leader and 2) the stability margins of vehicular platoons under bidirectional topologies using asymmetric controllers are always bounded away from zero and independent of the platoon size. Simulations with a platoon of passenger cars are used to demonstrate the findings.

Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

Abstract: This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.

Leader–Follower Formation and Tracking Control of Mobile Robots Along Straight Paths

Abstract: We address the problem of tracking control of multiple mobile robots advancing in formation along straight-line paths. We use a leader–follower approach, and hence, we assume that only one swarm leader robot has the information of the reference trajectory. Then, each robot receives information from one intermediary leader only. Therefore, the communications graph forms a simple spanning directed tree. As the existence of a spanning tree is necessary to achieve consensus, it is the minimal configuration possible to achieve the formation-tracking objective. From a technological viewpoint, this has a direct impact on the simplicity of its implementation; e.g., less sensors are needed. Our controllers are partially linear time-varying with a simple added nonlinearity satisfying a property of persistency of excitation, tailored for nonlinear systems. Structurally speaking, the controllers are designed with the aim of separating the tasks of position-tracking and orientation. Our main results ensure the uniform global asymptotic stabilization of the closed-loop system, and hence, they imply robustness with respect to perturbations. All these aspects make our approach highly attractive in diverse application domains of vehicles’ formations such as factory settings.

Direct Adaptive Fuzzy Tracking Control of Marine Vehicles With Fully Unknown Parametric Dynamics and Uncertainties

Abstract: In this brief, a novel direct adaptive fuzzy tracking control (DAFTC) scheme for marine vehicles with fully unknown parametric dynamics and uncertainties is proposed. The significant contributions of the DAFTC approach are as follows. First, in the backstepping framework, fully unknown parametric dynamics and uncertainties are encapsulated into a lumped nonlinearity function encompassing system states and virtual control signals. Second, the integrated nonlinearity function is further identified online by an adaptive fuzzy approximator that synthesizes a model-free control scheme (termed DAFTC) without requiring any a priori knowledge of the model. Third, tracking errors are proven to be uniformly ultimately bounded (UUB) and can converge to an arbitrarily small neighborhood of zero in a finite time. Simulation studies and comprehensive comparisons demonstrate that the proposed DAFTC scheme has remarkable performance and is superior in both tracking accuracy and unknown parametric dynamics compensation.

Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery

Abstract: Increased demand for hybrid and electric vehicles has motivated research to improve onboard state of charge (SOC) and state of health estimation (SOH). In particular, batteries with composite electrodes have become popular for automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. SOH algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes, such as lithium consumption at the solid-electrolyte interface or active material dissolution. Furthermore, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells. In this paper, we first present a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3Co1/3O2 electrode battery that predicts the surface and bulk lithium concentration of each material in the composite electrode, as well as the current split between each material. The model is then used in dual-nonlinear observers to estimate the cell SOC and loss of cyclable lithium over time. Three different observer types are compared: 1) the extended Kalman filter; 2) fixed interval Kalman smoother; and 3) particle filter. Finally, an experimental aging campaign is used to compare the estimated capacities for five different cells with the measured capacities over time.

Inner–Outer Loop Control for Quadrotor UAVs With Input and State Constraints

Abstract: The constrained control of unmanned aerial vehicles (UAVs) is a challenging task due to their nonlinear and underactuacted dynamics. This brief focuses on the position control of a quadrotor UAV with state and input constraints using an inner–outer loop control structure. The outer loop generates a saturated thrust, and the reference roll and pitch angles, while the inner loop is designed to follow these reference angles using a traditional PID controller. Assuming perfect inner loop tracking, the outer loop nested saturation controller guarantees global asymptotic stability for output regulation and tracking. The effect of nonideal inner loop tracking on closed-loop stability is analyzed. The proposed method is experimentally validated on an indoor quadrotor platform.

Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

Abstract: In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.

Correct-by-Construction Adaptive Cruise Control: Two Approaches

Abstract: Motivated by the challenge of developing control software provably meeting specifications for real-world problems, this paper applies formal methods to adaptive cruise control (ACC). Starting from a linear temporal logic specification for ACC, obtained by interpreting relevant ACC standards, we discuss in this paper two different control software synthesis methods. Each method produces a controller that is correct-by-construction, meaning that trajectories of the closed-loop systems provably meet the specification. Both methods rely on fixed-point computations of certain set-valued mappings. However, one of the methods performs these computations on the continuous state space whereas the other method operates on a finite-state abstraction. While controller synthesis is based on a low-dimensional model, each controller is tested on CarSim, an industry-standard vehicle simulator. Our results demonstrate several advantages over classical control design techniques. First, a formal approach to control design removes potential ambiguity in textual specifications by translating them into precise mathematical requirements. Second, because the resulting closed-loop system is known a priori to satisfy the specification, testing can then focus on the validity of the models used in control design and whether the specification captures the intended requirements. Finally, the set from where the specification (e.g., safety) can be enforced is explicitly computed and thus conditions for passing control to an emergency controller are clearly defined.

Double Integral Sliding Mode MPPT Control of a Photovoltaic System

Abstract: This brief proposes a new maximum power point tracker (MPPT) for a stand-alone photovoltaic (PV) system using the concept of double integral sliding mode controller (DISMC). Performance of a sliding mode controller (SMC) is greatly influenced by the choice of the sliding surface. A double integral SMC uses double integral of tracking voltage error term in its sliding surface to eliminate steady-state error apart from providing robust control actions in face of system uncertainties. However, there is still difficulty of increased chattering and slow transient response in DISMC. In this brief, the objective is to exploit the merits of the DISMC for designing an MPPT for PV system whilst the disadvantages of chattering and slow transient response are alleviated by choosing a new sliding surface. The design of this new DISMC-MPPT system considers the uncertainties in weather conditions and variations in load. This MPPT has been implemented using a pulsewidth modulator controlled dc/dc converter to keep the switching frequency constant. Thus, designing the control and filter circuits becomes simpler. The effectiveness of the proposed DISMC has been validated using simulation and experimental results using a prototype PV control system setup.

A Decentralized Scalable Approach to Voltage Control of DC Islanded Microgrids

Abstract: We propose a new decentralized control scheme for DC Islanded microGrids (ImGs) composed of several Distributed Generation Units (DGUs) with a general interconnection topology. Each local controller regulates the voltage of the point of common coupling of the corresponding DGU to a reference value. Notably, offline control design is conducted in a plug-and-play fashion, meaning that: 1) the possibility of adding/removing a DGU without spoiling the stability of the overall ImG is checked through an optimization problem; 2) when a DGU is plugged in or out, at most its neighboring DGUs have to update their controllers; and 3) the synthesis of a local controller uses only information on the corresponding DGU and lines connected to it. This guarantees the total scalability of control synthesis as the ImG size grows or DGUs get replaced. Yet, under mild approximations of line dynamics, we formally guarantee the stability of the overall closed-loop ImG. The performance of the proposed controllers is analyzed simulating different scenarios in PSCAD.

A Framework for Simplification of PDE-Based Lithium-Ion Battery Models

Abstract: Simplified models are commonly used in battery management and control, despite their (often implicit) limitations in capturing the dynamic behavior of the battery across a wide range of operating conditions. This paper seeks to develop a framework for battery model simplification starting from an initial high-order physics-based model that will explicitly detail the assumptions underpinning the development of simplified battery models. Starting from the basis of a model capturing the electrochemical, thermal, electrical, and aging dynamics in a set of partial differential equations, a systematic approach based on singular perturbations and averaging is used to simplify the dynamics through identification of disparate timescales inherent in the problem. As a result, libraries of simplified models with interconnections based on the specified assumptions are obtained. A quantitative comparison of the simplified models relative to the original model is used to justify the model reductions. To demonstrate the utility of the framework, a set of battery charging strategies is evaluated at reduced computational effort on simplified models.

Vision-Based Model Predictive Control for Steering of a Nonholonomic Mobile Robot

Abstract: In this paper, we have developed a novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target. The proposed control scheme has been realized at both kinematics and dynamics levels. The kinematics predictive steering controller generates command of desired velocities that are achieved by employing a low-level motion controller, while the dynamics predictive controller directly generates torques used to steer the WMR to the target. In the presence of both kinematics and dynamics constraints, the control design is carried out using quadratic programming (QP) for optimal performance. The neurodynamic optimization technique, particularly the primal-dual neural network, is employed to solve the QP problems. Theoretical analysis has been first performed to show that the desired velocities can be achieved with the guaranteed stability, as well as with the global convergence to the optimal solutions of formulated convex programming problems. Experiments have then been carried out to validate the effectiveness of the proposed control scheme and illustrate its advantage over the conventional methods.

Two Time-Scale Tracking Control of Nonholonomic Wheeled Mobile Robots

Abstract: In this paper, a practical control law is proposed for wheeled mobile robots in order to both improve the transient performance and repress the tracking errors. In particular, a two time-scale filtering technique, which can derive a fast variable to compensate for the disturbance, is applied during the carbot’s moving process. The nominal system is governed using a controller derived under the back-stepping framework. Such a design can effectively realize the system’s tracking objective and enhance robustness via properly configured parameters. In the meantime, a two time-scale filter is applied to the system function to estimate the disturbances, essentially improving the system’s precision. By virtue of this innovative technique, the final performance of the system is satisfactory in terms of both transient response and tracking error rejection. A previous sliding mode based control law is compared with the propounded one with respect to transient behavior and steady-state errors, and two types of disturbances, respectively the constant and sinusoid are simulated to verify the filter’s effectiveness. Since, from the results, there is significant improvement in both transient and steady-state performance, the proposed method is confirmed to be practical for tracking control of the wheeled mobile robots.

Design, Analysis, and Experimental Validation of a Distributed Protocol for Platooning in the Presence of Time-Varying Heterogeneous Delays

Abstract: This paper presents a novel control design framework for vehicle platooning together with its experimental validation. The problem of controlling the vehicles within a platoon, so that they converge to their desired velocities and intervehicle distances, is formulated as a high-order network consensus problem. By means of Lyapunov–Razumikhin functions, convergence is proven of the platoon to the desired consensus speed and intervehicle spacing under both fixed and switching communication network topologies, thus confirming the capability of the proposed approach to cope with maneuvers where vehicles join or leave the platoon and communication failures. Tuning criteria for the control gains are provided to guarantee string stability under the proposed control law. Finally, results of numerical simulations and in-vehicle experiments demonstrate the effectiveness of the proposed approach in a three-vehicle platoon.

Dynamic Positioning With Model Predictive Control

Abstract: Marine vessels with dynamic positioning (DP) capability are typically equipped with sufficient number of thrusters to make them overactuated and with satellite navigation and other sensors to determine their position, heading, and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a DP control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider a model-predictive control (MPC) algorithm that combines positioning control and TA into a single algorithm, which should theoretically yield a near-optimal controller output. This paper explores the advantages and disadvantages of using MPC compared with the traditional algorithms.

Nonlinear Control for Tracking and Obstacle Avoidance of a Wheeled Mobile Robot With Nonholonomic Constraint

Abstract: This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme.

Distributed Extremum Seeking for Constrained Networked Optimization and Its Application to Energy Consumption Control in Smart Grid

Abstract: In this paper, a distributed extremum seeking scheme is proposed to find the solution of a nonmodel-based distributed optimization problem among networked agents. The agents are supposed to have measurements of the local cost functions and constraint functions. However, no explicit expressions on the cost functions, the constraint functions, or their gradients are available. The design of the distributed extremum seeking scheme is based on the saddle point dynamics. Stability analysis is conducted via using averaging analysis, Lyapunov stability analysis, and the concept of saddle point. It is shown that the solution to the distributed optimization problem is semiglobally practically asymptotically stable under the proposed extremum seeking law. An application of the proposed extremum seeking method to energy consumption control for the electricity consumers in smart grid is discussed. Simulation results on energy consumption control of a network of heating ventilation and air conditioning systems are provided to validate the proposed distributed extremum seeker.

New Switching and Nonswitching Type Reaching Laws for SMC of Discrete Time Systems

Abstract: In this brief a new switching type reaching law for sliding mode control of discrete time systems is proposed. The proposed reaching law is a refined version of an earlier approach (introduced in the seminal work of Gao et al. ) which enforces constant plus proportional decrease rate of change of the sliding variable. In our method, the proportional term is modified, so that the rate is always bounded and decreases slower for smaller values of the sliding variable than in the original approach. The refined reaching law proposed in this brief, on the one hand, ensures faster convergence and better robustness of the controlled plant than the earlier approach, and on the other hand, it helps satisfy constraints of important signals in the system. Furthermore, in the latter part of this brief a new nonswitching type reaching law is introduced, and it is demonstrated that it results in further improvement of the system robustness without increasing the magnitude of the critical signals in the system.

Active Fault-Tolerant Control for Electric Vehicles With Independently Driven Rear In-Wheel Motors Against Certain Actuator Faults

Abstract: An active fault-tolerant control (AFTC) system is proposed in this paper for electric vehicles with independently driven in-wheel motors (IWMs). It comprises a baseline controller, a set of reconfigurable controllers, a fault detection and diagnosis (FDD) mechanism, and a decision mechanism. The baseline controller, which is actually a passive fault-tolerant controller, is applied to accommodate actuator faults and stabilize the faulty vehicle when the actuator fault occurs. After the fault is detected and estimated by the FDD mechanism, a proper reconfigurable controller is switched ON to achieve optimal postfault performance. Taking advantage of the robust gain-scheduling algorithm, the loss-of-effectiveness and additive faults of the IWMs can be accommodated by the baseline controller, and the estimation error of the FDD mechanism can be tolerated by the reconfigurable controllers. The results of simulations in CarSim and vehicle experimental tests show the effectiveness of this AFTC system in dealing with certain IWM faults.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

Abstract: In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.

Gaussian Process-Based Predictive Control for Periodic Error Correction

Abstract: Many controlled systems suffer from unmodeled nonlinear effects that recur periodically over time. Model-free controllers generally cannot compensate these effects, and good physical models for such periodic dynamics are challenging to construct. We investigate nonparametric system identification for periodically recurring nonlinear effects. Within a Gaussian process (GP) regression framework, we use a locally periodic covariance function to shape the hypothesis space, which allows for a structured extrapolation that is not possible with more widely used covariance functions. We show that hyperparameter estimation can be performed online using the maximum a posteriori point estimate, which provides an accuracy comparable with sampling methods as soon as enough data to cover the periodic structure has been collected. It is also shown how the periodic structure can be exploited in the hyperparameter optimization. The predictions obtained from the GP model are then used in a model predictive control framework to correct the external effect. The availability of good continuous predictions allows control at a higher rate than that of the measurements. We show that the proposed approach is particularly beneficial for sampling times that are smaller than, but of the same order of magnitude as, the period length of the external effect. In experiments on a physical system, an electrically actuated telescope mount, this approach achieves a reduction of about 20% in root mean square tracking error.

Real-Time Fuel Economy Optimization With Nonlinear MPC for PHEVs

Abstract: This brief addresses the energy management problem with the framework of receding horizon optimization. For power-split plug-in hybrid electric vehicles (HEVs), the real-time power-split decision is formulated as a nonlinear receding horizon optimization problem. Then, an online iterative algorithm to solve the optimization problem is proposed based on the continuation/generalized minimum residual algorithm. It should be noted that the proposed energy management strategy aims for optimality of the targeted horizon, but the solution is not optimal for the full driving route, unlike many solutions presented using the dynamic programming approaches. At each decision step, only the initial value of the optimal solution is implemented according to the receding horizon optimization approach. Finally, to demonstrate a comparison of the proposed scheme with other schemes, numerical validations conducted on a full-scale GT-SUITE HEV simulator are presented.

Model Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs

Abstract: The problem of steering a nonholonomic mobile robot to a desired position and orientation is considered. In this paper, a model predictive control (MPC) scheme based on tailored nonquadratic stage cost is proposed to fulfill this control task. We rigorously prove asymptotic stability while neither stabilizing constraints nor costs are used. To this end, we first design suitable maneuvers to construct bounds on the value function. Second, these bounds are exploited to determine a prediction horizon length such that the asymptotic stability of the MPC closed loop is guaranteed. Finally, numerical simulations are conducted to explain the necessity of having nonquadratic running costs.