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Control Systems Technology, IEEE Transactions on

Issue 1 • Date Jan. 2013

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Displaying Results 1 - 25 of 28
  • Table of Contents

    Page(s): C1 - C4
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  • IEEE Transactions on Control Systems Technology publication information

    Page(s): C2
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  • Robust Estimation of Road Frictional Coefficient

    Page(s): 1 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3372 KB) |  | HTML iconHTML  

    Knowledge of tire force potential, i.e., tire-road frictional coefficient, is important for vehicle active safety systems because tire-road friction is an effective measure of the safety margin of vehicle dynamics. For vehicle handling dynamics, the frictional coefficient is highly coupled with tire slip angle, therefore, they need to be estimated simultaneously when the latter is not measured. This paper presents an estimation algorithm based on a robust adaptive observer methodology. Stability and robustness of this observer are analyzed numerically. The performance is analyzed using computer simulations and experiments under various road and steering conditions. View full abstract»

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  • Optimal Energy and Catalyst Temperature Management of Plug-in Hybrid Electric Vehicles for Minimum Fuel Consumption and Tail-Pipe Emissions

    Page(s): 14 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3427 KB) |  | HTML iconHTML  

    Control of plug-in hybrid electric vehicles (PHEVs) poses a different challenge from that of the conventional hybrid electric vehicle (HEV) because the battery energy is designed to deplete throughout the drive cycle. In particular, when the travel distance exceeds the all-electric range (AER) of a PHEV and when tailpipe emissions are considered, optimal operation of the PHEV must consider optimization of the performance over a time horizon. In this paper, we develop a method to synthesize a supervisory powertrain controller (SPC) that achieves near-optimal fuel economy and tailpipe emissions under known travel distances. We first find the globally optimal solution using the dynamic programming (DP) technique, which provides an optimal control policy and state trajectories. Based on the analysis of the optimal state trajectories, a new variable energy-to-distance ratio (EDR), θ, is introduced to quantify the level of battery state-of-charge (SOC) relative to the remaining distance. This variable plays an important role in adjusting both energy and catalyst thermal management strategies for PHEVs. A novel extraction method is developed to extract adjustable engine on/off, gear-shift, and power-split strategies from the DP control policy over the entire state space. Based on the extracted results, an adaptive SPC that optimally adjusts the engine on/off, gear-shift, and power-split strategies under various EDR and catalyst temperature conditions was developed to achieve near-optimal fuel economy and emission performance. View full abstract»

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  • Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks

    Page(s): 27 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3428 KB) |  | HTML iconHTML  

    In this paper, a novel distributed Kalman filter (KF) algorithm along with a distributed model predictive control (MPC) scheme for large-scale multi-rate systems is proposed. The decomposed multi-rate system consists of smaller subsystems with linear dynamics that are coupled via states. These subsystems are multi-rate systems in the sense that either output measurements or input updates are not available at certain sampling times. Such systems can arise, e.g., when the number of sensors is smaller than the number of variables to be controlled, or when measurements of outputs cannot be completed simultaneously because of practical limitations. The multi-rate nature gives rise to lack of information, which will cause uncertainty in the system's performance. To circumvent this problem, we propose a distributed KF-based MPC scheme, in which multiple control and estimation agents each determine actions for their own parts of the system. Via communication, the agents can in a cooperative way take one another's actions into account. The main task of the proposed distributed KF is to compensate for the information loss due to the multi-rate nature of the systems by providing optimal estimation of the missing information. A demanding two-area power network example is used to demonstrate the effectiveness of the proposed method. View full abstract»

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  • Two-Channel Transparency-Optimized Control Architectures in Bilateral Teleoperation With Time Delay

    Page(s): 40 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2447 KB) |  | HTML iconHTML  

    This paper introduces transparency-optimized control architectures (TOCAs) using two communication channels. Two classes of two-channel TOCAs are found, thereby showing that two channels are sufficient to achieve transparency. These TOCAs achieve a greater level of transparency but poorer stability than three-channel TOCAs and four-channel TOCAs. Stability of the two-channel TOCAs has been enhanced while minimizing transparency degradation by adding a filter; and a combined use of the two classes of two-channel TOCAs is proposed for both free space and constrained motion, which involve switching between two TOCAs for transition between free space and constrained motions. The stability condition of the switched teleoperation system is derived for practical applications. Through the one degree-of-freedom (DOF) experiment, the proposed two-channel TOCAs were shown to operate stably, while achieving better transparency under time delay than the other TOCAs. View full abstract»

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  • Antiwindup Design for Induction Motor Control in the Field Weakening Domain

    Page(s): 52 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3176 KB) |  | HTML iconHTML  

    Operation of induction machines in the high-speed and/or high-torque range requires field-weakening to comply with voltage and current physical limitations. This paper presents an anti-windup approach to this problem: rather than developing an ad-hoc field weakening strategy in the high-speed region, we equip an unconstrained vector-control design with an anti-windup module that automatically adjusts the current and flux set-points so that voltage and current constraints are satisfied at every operating point. The anti-windup module includes a feedforward modification of the set point aimed at maximizing the available torque in steady-state and a feedback modification of the controller based on an internal model-based antiwindup scheme. This paper includes a complete stability analysis of the proposed solution and presents encouraging experimental results on an industrial drive. View full abstract»

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  • Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles

    Page(s): 67 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2578 KB) |  | HTML iconHTML  

    This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation. View full abstract»

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  • Plug-and-Play Control—Modifying Control Systems Online

    Page(s): 79 - 93
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2832 KB) |  | HTML iconHTML  

    Often, when new sensor or actuator hardware becomes available for use in a control system, it is desirable to retain the existing control system and apply the new control capabilities in a gradual fashion rather than decommissioning the entire existing system and replacing it with an altogether new control system. However, this requires that the existing controller remains in action, and the new control law component is added to the existing system. This paper formally introduces the concept of Plug-and-Play control and proposes two different methods of introducing new control components in a smooth manner, providing stability guarantees during the transition phase as well as retaining the original control structure. The applicability of the methods is illustrated on two different practical example systems, a livestock stable climate control system and a laboratory-scale model of a district heating system. View full abstract»

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  • Data-Based Modeling and Control of Nylon-6, 6 Batch Polymerization

    Page(s): 94 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1518 KB) |  | HTML iconHTML  

    This work addresses the problem of modeling the complex nonlinear behavior of the nylon-6, 6 batch polymerization process and then subsequently tracking trajectories of important process variables, namely the reaction medium temperature and reactor pressure, using model predictive control. To this end, a data-based multi-model approach is proposed in which multiple local linear models are identified from previous batch data using latent variable regression and then combined using an appropriate (continuous) weighting function that arises from fuzzy c-means clustering. The proposed approach unifies the concepts of auto-regressive exogenous (ARX) modeling, latent variable regression techniques, fuzzy c-means clustering, and multiple local linear models in an integrated framework capable of capturing the nonlinearities and multivariate nature of batch data. The resulting data-based model is then used to formulate a trajectory tracking predictive controller. Through simulation studies, the modeling approach is shown to capture the major nonlinearities in the nylon-6, 6 polymerization process and closed-loop simulation results demonstrate the efficacy of the proposed predictive controller and illustrate its advantages over existing trajectory tracking approaches such as conventional proportional-integral control and latent variable model predictive control. View full abstract»

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  • Efficient Parallel Implementation of State Estimation Algorithms on Multicore Platforms

    Page(s): 107 - 120
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3655 KB) |  | HTML iconHTML  

    For many applications in signal processing and control it is crucial that estimates of the state vector in a dynamic system can be obtained in real time. This poses the problem of producing algorithms that are fast enough to enable online execution. In this article, it is investigated how two of the most popular and powerful state estimation algorithms, the Kalman filter and the particle filter, can be efficiently implemented in parallel on a multicore architecture. The proposed parallel implementations are analyzed in terms of hardware requirements, such as memory bandwidth and available cache memory, to provide the desired speedup. The algorithms are exemplified by and evaluated in an adaptive filtering and a bearings-only tracking application. In the cases when original algorithms have been modified for parallelization, the accuracy of the estimates obtained is evaluated in comparison with that of the sequential algorithm. It is found that linear speedup, in the number of cores used, can indeed be achieved without loss of accuracy, for both state estimation algorithms. View full abstract»

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  • An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

    Page(s): 121 - 135
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3259 KB) |  | HTML iconHTML  

    We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of nontraditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. View full abstract»

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  • A Decentralized Explicit Predictive Control Paradigm for Parallelized DC-DC Circuits

    Page(s): 136 - 148
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2255 KB) |  | HTML iconHTML  

    This paper extends previously introduced explicit optimal control methods for fixed frequency switched mode DC-DC converter circuits to the parallel synchronous step-down (i.e., buck) topology with N branches. Due to the computational complexity of modelling and controlling such potentially large multivariable systems a decentralized model predictive control scheme is employed allowing for the related optimal control problem to be efficiently formulated and solved offline for each local controller. Simulation results are provided to illustrate the outcome of the proposed approach. View full abstract»

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  • Design and Experimental Validation of a Nonlinear Low-Level Controller for an Unmanned Fin-Less Airship

    Page(s): 149 - 161
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2823 KB) |  | HTML iconHTML  

    This paper discusses the design of a combined backstepping/Lyapunov controller for the attitude, velocity and height control of an unmanned, unstable, fin-less airship. As the airship actuation has more degrees of freedom than the motion controlled, the controller includes a quadratic optimization algorithm to find the optimal thruster commands. The control law developed provides attitude and velocity control for the entire airship flight regime, i.e., hover, vertical ascent and descent as well as cruise, all with a single controller. Controller performance is first verified using a simulation that includes detailed modeling of sensor noise, computational delays and actuation dynamics. Subsequently, the controller is tested in outdoor flight tests. The controller has been found to perform well both in simulation and flight tests. The controller parameters were identical in simulation and flight test demonstrating the high fidelity of the simulation. View full abstract»

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  • Intelligence-Based Supervisory Control for Optimal Operation of a DCS-Controlled Grinding System

    Page(s): 162 - 175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2430 KB) |  | HTML iconHTML  

    Optimizing the final grinding production indices (GPIs), which include the product particle size and the grinding production rate, to meet the overall manufacturing performance requirements is the main function of automatic control of a grinding circuit (GC). However, the complex and time-varying nature of the GC process dictates that these GPIs cannot be optimized solely by the lower-level distributed control systems (DCS), therefore an operator is often incorporated to manually determine the set-points for the DCS using his/her operational experience. With a human being involved, the performance and even the safety and stability of the GC operation is subject to human errors. Focusing on this practical challenge, this paper proposes an intelligence-based supervisory control strategy that consists of a control loop set-point optimization module, an artificial neural network-based soft-sensor module, a fuzzy logic-based dynamic adjustor, and an expert-based overload diagnosis and adjustment module to perform the control tasks for the GC system. This hybrid system can automatically adjust the set-points for the DCS-controlled grinding system in response to the changes in boundary conditions or the imminent overload conditions, thereby eliminating the need for an operator. Practical applications have shown the validity and effectiveness of the proposed approach. View full abstract»

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  • Output Feedback Sliding Mode Control for a Stewart Platform With a Nonlinear Observer-Based Forward Kinematics Solution

    Page(s): 176 - 185
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2303 KB) |  | HTML iconHTML  

    In this paper, an observer-based forward kinematics solution of a 6-6 Stewart platform is proposed and this algorithm is applied to implement an output feedback sliding mode control. The conventional forward kinematics solutions take too much computational load or are too complex to be carried out in the online control scheme. The proposed nonlinear observer-based algorithm provides a simple method to obtain a real-time forward kinematics solution. With this solution, 6-degrees-of-freedom posture control of the moving platform can be achieved without installation of any external sensor after applying an output feedback control. In contrast with the conventional control scheme which aims to control individual leg length in actuator domain, the output feedback controller is proposed here to control the posture in Cartesian domain directly. The stability of the whole system is thoroughly proved to ensure convergence of the control errors. Simulations and experimental results are presented to validate the feasibility of the hereby proposed results. View full abstract»

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  • Data-Driven Design of Braking Control Systems

    Page(s): 186 - 193
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1611 KB) |  | HTML iconHTML  

    The spread of active braking controllers on vehicles with significant mechanical differences and on low-cost products asks for control design approaches which offer easy and fast calibration and re-tuning capabilities. This task is made difficult by the use of model-based control approaches which heavily rely on specific vehicle dynamics descriptions. To address these issues, this brief paper proposes a data-driven approach to active braking control design, grounded on the virtual reference feedback tuning (VRFT) approach complemented with a data-driven nonlinear compensator. The effectiveness of the proposed approach is assessed both on a full-fledged multibody simulator and on a tire-in-the-loop experimental facility. View full abstract»

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  • Adaptive Robust Control of Servo Mechanisms With Compensation for Nonlinearly Parameterized Dynamic Friction

    Page(s): 194 - 202
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2338 KB) |  | HTML iconHTML  

    In this brief, an adaptive robust control (ARC) scheme with compensation for nonlinearly parameterized dynamic friction is proposed. Both parametric uncertainties and external disturbances are considered in this method. Our method takes advantage of a Lipschitzian property with respect to the parameters of nonlinearly parameterized model in the ARC design. The outcome is that the number of parameters to be updated in the ARC is equal to the number of unknown parameters in the plant, and thus the resulting control algorithm is convenient to be implemented. We have proved theoretically that the proposed method can not only guarantee desired transient performance for the system, but also make the magnitude of steady-state tracking error to be arbitrarily small in the presence of parametric uncertainties only. Experimental results are given to demonstrate the effectiveness of the proposed ARC scheme. View full abstract»

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  • Modeling and Control of a Nonlinear Mechanism for High Performance Microfluidic Systems

    Page(s): 203 - 211
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1361 KB) |  | HTML iconHTML  

    This brief presents modeling and control of a nonlinear mechanism for long-term and high-speed flow regulation in a three-lane microfluidic system. The principle of this mechanism is to modulate a mechanically coupled variable resistance and variable volume reservoir for pressure control at the inlets of microfluidic systems. We developed a dual-loop control system that consists of an inner-loop position controller and an outer-loop pressure controller. We show excellent agreements between analyses, simulations, and experimental results, and demonstrate bandwidth of 10 Hz and duration of 15 hours. We envision that this system will be useful to researchers in areas such as flow cytometry, chemical synthesis, drug delivery, and investigation of spatiotemporally integrated biological responses at molecular, cellular, and tissue levels. View full abstract»

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  • Manipulation of Magnetic Particles Using Adaptive Magnetic Traps

    Page(s): 212 - 219
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1077 KB) |  | HTML iconHTML  

    This brief discusses incorporating an adaptive Q-parametrized compensator structure into the design of magnetic traps. This approach characterizes all stabilizing controllers using a single, stable filter, that can be used as a free parameter to tune and adapt the instrument. Q-parameterization transforms the feedback control problem into an equivalent feedforward control problem, and adaptive filter theory is used to adjust the weights of an finite impulse response to account for changing instrument dynamics and to reduce the effects of Brownian disturbances. This ensures the performance of the magnetic trap instrument meets specific performance requirements while accounting for the changing dynamics of the instrument. The adaptive system was implemented in a hardware-in-the-loop simulation. The adaptive control approach converged to a controller that exhibits disturbance rejection performance at least as good as a fixed-gain controller designed for the system. View full abstract»

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  • Saturation Control of a Piezoelectric Actuator for Fast Settling-Time Performance

    Page(s): 220 - 228
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1978 KB) |  | HTML iconHTML  

    This brief studies fast tracking control of piezoelectric (PZT) actuators. Adverse effects associated with the PZT actuators typically include the nonlinear dynamics of hysteresis and saturation and the linear vibrational dynamics. To eliminate the loss of performance due to these effects, we propose a new control scheme for the PZT actuators. It consists of a combined feedforward/feedback compensator for hysteresis and resonance compensation and a nested switching controller (NSC) that optimizes a quadratic performance cost function involving the actuator saturation. The NSC not only can guarantee the system stability in the presence of saturation but also can improve the tracking speed by efficiently allocating the control efforts. The experimental results on an actual PZT nanopositioner show that the new control scheme outperforms the conventional control by more than 12% in settling time within the full PZT operational range and with nanoscale precision. View full abstract»

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  • Mixed {cal H}_{2}/{cal H}_{\infty } Observer-Based LPV Control of a Hydraulic Engine Cam Phasing Actuator

    Page(s): 229 - 238
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2431 KB) |  | HTML iconHTML  

    In this paper, a family of linear models previously obtained from a series of closed-loop system identification tests for a variable valve timing cam phaser system is used to design a dynamic gain-scheduling controller. Using engine speed and oil pressure as the scheduling parameters, the family of linear models was translated into a linear parameter varying (LPV) system. An observer-based gain-scheduling controller for the LPV system is then designed based on the linear matrix inequality technique. A discussion on weighting function selection for mixed H2/H controller synthesis is presented, with an emphasis placed on examining various frequency responses of the system. Test bench results show the effectiveness of the proposed scheme. View full abstract»

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  • Power Optimization in Embedded Systems via Feedback Control of Resource Allocation

    Page(s): 239 - 246
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (963 KB) |  | HTML iconHTML  

    Embedded systems often operate in so variable conditions that design can only be carried out for some worst-case scenario. This leads to over-provisioned resources, and undue power consumption. Feedback control is an effective (and not yet fully explored) way to tailor resource usage online, thereby making the system behave and consume as if it was optimized for each specific utilization case. A control-theoretical methodology is here proposed to complement architecture design in a view to said tailoring. Experimental results show that a so addressed architecture meets performance requirements, while consuming less power than any fixed (i.e., uncontrolled) one capable of attaining the same goals. Also, the methodology naturally induces computationally lightweight control laws. View full abstract»

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  • Proportional Navigation With Delayed Line-of-Sight Rate

    Page(s): 247 - 253
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1831 KB) |  | HTML iconHTML  

    This brief discusses the convergence analysis of proportional navigation (PN) guidance law in the presence of delayed line-of-sight (LOS) rate information. The delay in the LOS rate is introduced by the missile guidance system that uses a low cost sensor to obtain LOS rate information by image processing techniques. A Lyapunov-like function is used to analyze the convergence of the delay differential equation (DDE) governing the evolution of the LOS rate. The time-to-go until which decreasing behaviour of the Lyapunov-like function can be guaranteed is obtained. Conditions on the delay for finite time convergence of the LOS rate are presented for the linearized engagement equation. It is observed that in the presence of line-of-sight rate delay, increasing the effective navigation constant of the PN guidance law deteriorates its performance. Numerical simulations are presented to validate the results. View full abstract»

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  • A Multiple Model-Based Approach for Fault Diagnosis of Jet Engines

    Page(s): 254 - 262
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1315 KB) |  | HTML iconHTML  

    In this brief, a novel real-time fault detection and isolation (FDI) scheme that is based on the concept of multiple model is proposed for aircraft jet engines. A modular and a hierarchical architecture is developed which enables the detection and isolation of both single faults as well as multiple concurrent faults in the jet engine. The nonlinear dynamics of a dual spool jet engine is linearized and a set of linear models corresponding to various operating modes of the jet engine (namely healthy and different faulty modes) at each operating point is obtained. Using the multiple model approach the probabilities corresponding to each operating point of the jet engine are generated and the current operating mode of the system is detected based on evaluating the maximum probability criteria. It is shown that the proposed methodology is also robust to the failure of pressure and temperature sensors and extensive levels of noise outliers in the sensor measurements. Simulation results are presented that demonstrate the effectiveness and capabilities of our proposed multiple model FDI algorithm for both structural faults and an actuator fault in the aircraft jet engine. View full abstract»

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