Showing papers in "IEEE Transactions on Control Systems and Technology in 2023"
TL;DR: In this article , an autonomous vehicle sidelip angle estimation algorithm is proposed based on consensus and vehicle kinematics/dynamics synthesis, where a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS.
Abstract: An autonomous vehicle sideslip angle estimation algorithm is proposed based on consensus and vehicle kinematics/ dynamics synthesis. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and the global navigation satellite system (GNSS), a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS. The observability issue of the heading error, which affects sideslip estimation, is analyzed. Then, to enhance the observability and improve the estimation accuracy of the heading error under normal driving conditions, a consensus Kalman information filter is developed to synthesize the vehicle kinematics and dynamics and estimate the heading error. Within the developed consensus framework, one node augments a novel heading error measurement from a linear vehicle-dynamic-based sideslip estimator and another node adopts the heading error from the GNSS course. Next, based on the vehicle lateral excitation level, a weighting scheme is proposed to fuse the error state estimates from the velocity-based and consensus Kalman state observers. The stability of the proposed state observers is also investigated. Comprehensive experimental studies, including critical slalom, slight/normal double lane change, and normal driving maneuvers, were conducted to verify the proposed estimation framework; they confirm the reliability and accuracy of the estimator in various automated driving conditions even in comparison with state-of-the-art methods that utilize more measurements (dual-antenna GNSS). Also, this novel multisensor framework is extendable to leverage speed information from other sensors such as cameras and light detection and ranging (LiDAR) to increase reliability and accuracy.
60 citations
TL;DR: In this paper , the authors investigated the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs.
Abstract: This article investigates the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs. To achieve preset arbitrary configuration transformations in the case where the formation knowledge is assigned to only a part of ASVs, the affine image is employed to describe desired configurations. To ensure position and attitude configuration rigidities simultaneously, a synthetical constraint composed of the Laplacian matrices and the stress matrices is constructed. Besides, an algebraic relation between the synthetical constraint and the formation tracking error is established. The above tools enable the sliding mode technology to be used to the affine formation maneuver control of collaborative ASVs systems. It follows that a novel distributed event-triggered affine formation maneuver control scheme is developed for the collaborative ASVs systems. The advantages of the proposed control scheme are threefold: 1) the distributed affine formation maneuver control of collaborative second-order vehicles systems is achieved with changeable formation velocities and orientations; 2) the existing literatures adapt to developing the second-order distributed affine formation maneuver control based on the backstepping philosophy. In contrast, the sliding manifold replaces the backstepping philosophy here. Since the complexity explosion is circumvented, the proposed control scheme is relatively simple; and 3) the event-triggered data transmission mechanism among inter-ASVs is introduced, and neighbor velocities and accelerations are omitted for the control implementation. As a consequence, the communication resource usage can be reduced. Finally, the semi-physical simulations are conducted to illustrate the effectiveness of the proposed control scheme.
7 citations
TL;DR: In this article , a framework for safety-critical control in dynamic environments, by establishing the notion of environmental control barrier functions (ECBFs), is proposed, which is able to guarantee safety even in the presence of input delay, by accounting for the evolution of the environment during the delayed response of the system.
Abstract: Endowing nonlinear systems with safe behavior is increasingly important in modern control. This task is particularly challenging for real-life control systems that operate in dynamically changing environments. This article develops a framework for safety-critical control in dynamic environments, by establishing the notion of environmental control barrier functions (ECBFs). Importantly, the framework is able to guarantee safety even in the presence of input delay, by accounting for the evolution of the environment during the delayed response of the system. The underlying control synthesis relies on predicting the future state of the system and the environment over the delay interval, with robust safety guarantees against prediction errors. The efficacy of the proposed method is demonstrated by a simple adaptive cruise control (ACC) problem and a more complex robotics application on a Segway platform.
6 citations
6 citations
TL;DR: In this article , the authors investigated the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs.
Abstract: This article investigates the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs. To achieve preset arbitrary configuration transformations in the case where the formation knowledge is assigned to only a part of ASVs, the affine image is employed to describe desired configurations. To ensure position and attitude configuration rigidities simultaneously, a synthetical constraint composed of the Laplacian matrices and the stress matrices is constructed. Besides, an algebraic relation between the synthetical constraint and the formation tracking error is established. The above tools enable the sliding mode technology to be used to the affine formation maneuver control of collaborative ASVs systems. It follows that a novel distributed event-triggered affine formation maneuver control scheme is developed for the collaborative ASVs systems. The advantages of the proposed control scheme are threefold: 1) the distributed affine formation maneuver control of collaborative second-order vehicles systems is achieved with changeable formation velocities and orientations; 2) the existing literatures adapt to developing the second-order distributed affine formation maneuver control based on the backstepping philosophy. In contrast, the sliding manifold replaces the backstepping philosophy here. Since the complexity explosion is circumvented, the proposed control scheme is relatively simple; and 3) the event-triggered data transmission mechanism among inter-ASVs is introduced, and neighbor velocities and accelerations are omitted for the control implementation. As a consequence, the communication resource usage can be reduced. Finally, the semi-physical simulations are conducted to illustrate the effectiveness of the proposed control scheme.
4 citations
TL;DR: In this article , a stochastic event-triggered robust dynamic state estimation (DSE) method for non-Gaussian measurement noises, using the cubature Kalman filter (CKF) technique, is proposed.
Abstract: In power system communication and control, the wide-area measurement system (WAMS) is usually adversely affected by noisy measurements and data congestion, posing great challenges to the stability and functionality of modern power grids. This study proposes a stochastic event-triggered robust dynamic state estimation (DSE) method for non-Gaussian measurement noises, using the cubature Kalman filter (CKF) technique. To reduce the computational burden and data transmission congestion resulting from centrally processing the measurement data, the proposed event-triggered robust CKF (ET-RCKF) is deployed at a local level with appropriate system formulation. The proposition of the novel robust DSE strategy is detailed in this brief, with its stability mathematically analyzed and proven, and simulation study on the IEEE 39-bus benchmark test system verifies the effectiveness of the proposed ET-RCKF approach. This novel DSE method is able to cope with non-Gaussian measurement noises and produce highly satisfactory estimation results, leading to wide applicability in real-world power system applications.
4 citations
TL;DR: In this paper , a stochastic event-triggered robust dynamic state estimation (DSE) method for non-Gaussian measurement noises, using the cubature Kalman filter (CKF) technique, is proposed.
Abstract: In power system communication and control, the wide-area measurement system (WAMS) is usually adversely affected by noisy measurements and data congestion, posing great challenges to the stability and functionality of modern power grids. This study proposes a stochastic event-triggered robust dynamic state estimation (DSE) method for non-Gaussian measurement noises, using the cubature Kalman filter (CKF) technique. To reduce the computational burden and data transmission congestion resulting from centrally processing the measurement data, the proposed event-triggered robust CKF (ET-RCKF) is deployed at a local level with appropriate system formulation. The proposition of the novel robust DSE strategy is detailed in this brief, with its stability mathematically analyzed and proven, and simulation study on the IEEE 39-bus benchmark test system verifies the effectiveness of the proposed ET-RCKF approach. This novel DSE method is able to cope with non-Gaussian measurement noises and produce highly satisfactory estimation results, leading to wide applicability in real-world power system applications.
4 citations
TL;DR: In this paper , a spatiotemporal optimization approach for vertical path planning (i.e., waypoint optimization) to maximize the net output power of an ocean current turbine under uncertain ocean velocities is presented.
Abstract: This article presents a novel spatiotemporal optimization approach for vertical path planning (i.e., waypoint optimization) to maximize the net output power of an ocean current turbine (OCT) under uncertain ocean velocities. To determine the net power, OCT power generation from hydrokinetic energy and the power consumption for controlling the depth are modeled. The stochastic behavior of ocean velocities is a function of spatial and temporal parameters, which is modeled through a Gaussian process (GP) approach. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are applied to solve the formulated spatiotemporal optimization problem with constraints. Comparative studies show that the MPC- and RL-based methods are computationally feasible to address vertical path planning, which are evaluated with a baseline A* approach. Analysis of the robustness is further carried out under the inaccurate ocean velocity predictions. Results verify the efficiency of the presented methods in finding the optimal path to maximize the total power of an OCT system, where the total harnessed energy after 200 h shows over an 18% increase compared to the case without optimization.
4 citations
TL;DR: In this paper , the authors describe parallel and series resonant converters (PRC and SRC) via a unified set of input-dependent coordinates whose dynamics are intrinsically hybrid, and propose hybrid feedback showing a self-oscillating behavior whose amplitude and frequency can be adjusted by a reference input ranging from zero to
Abstract: We describe parallel and series resonant converters (PRC and SRC) via a unified set of input-dependent coordinates whose dynamics are intrinsically hybrid. We then propose hybrid feedback showing a self-oscillating behavior whose amplitude and frequency can be adjusted by a reference input ranging from zero to $\pi $ . For any reference value in that range, we give a Lyapunov function certifying the existence of a unique nontrivial hybrid limit cycle whose basin of attraction is global except for the origin. Our results are confirmed by experimental results on an SRC prototype.
3 citations
TL;DR: In this article , an eye-in-hand visual servoing control (VSC) scheme based on the input mapping method was proposed, which directly utilizes the past output-input data to enhance the original feedback control law rather than identifying the model.
Abstract: In image-based visual servoing (IBVS), parametric uncertainties tend to cause the model inaccuracy and limit the control performance. Considering these uncertainties can be embodied by the output–input data from the visual servoing system, this brief proposes an eye-in-hand visual servoing control (VSC) scheme based on the input mapping method, which directly utilizes the past output–input data to enhance the original feedback control law rather than identifying the model. The system with the input mapping method is proven to not only maintain the stability of the original VSC but also accelerate the convergent rate. The results of the experiments on a manipulator with an eye-in-hand camera demonstrate the superiority of our proposed method.
3 citations
TL;DR: In this article , a constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in field values can be estimated.
Abstract: This article presents an online parameter identification scheme for advection–diffusion processes using data collected by a mobile sensor network. The advection–diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection–diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection–diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances.
TL;DR: In this paper , a model-based approach that exploits the compactness and graphical representation of time-interpreted Petri nets, which adds input-output interpretation to transitions/places and embeds time information, is presented.
Abstract: Validation of industrial automation systems is the process of checking that commissioner requirements are successfully implemented. Formal approaches are needed when the considered system is critical. The method presented in this article relies on a model-based approach that exploits the compactness and graphical representation of time-interpreted Petri nets, which adds input–output interpretation to transitions/places and embeds time information. These nets are here used with multiple-server semantic to allow effective modeling of typical automation system requirements. The key idea of the system validation approach is to compare the observed behavior of the automation system with the expected behavior, as generated by updating online the model of system requirements using a state estimation algorithm. Also, an off-line procedure is provided to evaluate the evolutions admitted by the model but not observed. Both procedures yield useful data to the validation engineer, allowing to speed up the validation process. Technological issues due to the synchronous nature of controllers and the implications of their programming are considered.
TL;DR: In this article , the authors compare two phase-locked-loop (PLL) algorithms aimed at tracking a biased sinusoidal signal with unknown frequency, amplitude, and phase, with inherent robustness to dc offset.
Abstract: This work describes and compares two phase-locked-loop (PLL) algorithms aimed at tracking a biased sinusoidal signal with unknown frequency, amplitude, and phase, with inherent robustness to dc offset. The proposed methods endow quadrature PLLs, renowned for their excellent tracking performance, with frequency-adaptation capability, while providing robust global stability certificates. The large-gain global stability, proved by Lyapunov-like arguments borrowed from adaptive control theory, represents a major benefit when compared to the conventional PLLs, whose convergence instead can be proved only locally by small-signal analysis or small-gain assumptions. In this connection, the proposed algorithms represent the first frequency-adaptive and dc-bias rejecting PLL-type architectures with Lyapunov-certified global stability. When used for signal tracking, the proposed methods are shown to outperform the adaptive observer, especially in noisy conditions. Moreover, they provide more accurate frequency estimates than existent frequency-adaptive PLLs, showing enhanced robustness in facing both phase-noise and measurement perturbations.
TL;DR: In this paper , a fault diagnosis methodology for rotating machinery with limited expert interaction is proposed, where a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation.
Abstract: Recently, research on the fault diagnosis of rotating machinery, especially for the compound or unknown cases, has drawn increasing attention. Some advanced learning-based approaches have achieved good fault diagnosis performance to some degree. However, in practical applications, how to utilize prior knowledge as fully as possible for fault diagnosis with constraints of limited expert interaction remains an open issue. In this brief, a fault diagnosis methodology of rotating machinery with limited expert interaction is proposed. With related feature extraction techniques, a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation. In addition, the broad learning system (BLS) is exploited to achieve fast incrementally updating or retrain procedures with high diagnostic accuracy in different diagnosis scenarios. Several experiments are conducted on a real-world rotating machinery fault diagnosis (RMFD) experimental platform. Compared with other existing advanced approaches, the diagnosis performance of the proposal shows high stability and flexibility. The annotation cost of experts is also significantly reduced, which makes the proposal more suitable for dealing with practical problems.
TL;DR: In this article, a distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots are presented.
Abstract: This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an onboard computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a following: 1) synchronous or 2) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers (ADMM), we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots; that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in the following: 1) an unsupervised intersection crossing and 2) the platooning scenarios.
TL;DR: In this article , a formation control strategy for leader-follower wheeled mobile robots is proposed based on the embedded control technique, which decomposes the formation control task into two subtasks: the virtual signal generator (i.e., virtual follower) design and the trajectory tracking controller design.
Abstract: The formation control problem for leader–follower wheeled mobile robots (WMRs) is investigated in this article. A formation control strategy is proposed based on the embedded control technique. Different from the conventional design philosophy of designing the formation controller based on the formation tracking errors directly, the proposed strategy decomposes the formation control task into two subtasks. One is the virtual signal generator (i.e., virtual follower) design, and the other is the trajectory tracking controller design. The virtual signal generator is devised to act as a virtual follower such that it “achieves” the desired formation control goal, whose output is taken as the reference trajectory for the follower. To solve the subtask of trajectory tracking controller design, an unconstrained controller and a constrained controller with saturated velocities are designed, respectively. A rigorous stability analysis of the closed-loop system is given under these two kinds of controllers. Comparative simulations and experiments among the unconstrained controller with higher gains/lower gains and the constrained controller are given to illustrate the feasibility and effectiveness of the proposed formation control strategy.
TL;DR: In this article , a formation control system (FCS) with multiple virtual leaders and semi-Markov switching topologies is proposed to alleviate the threat of communication failures, and a time-varying formation tracking protocol with an error compensation term is introduced to mitigate the effects among the real leader and the virtual leaders.
Abstract: To alleviate the threat of communication failures, a formation control system (FCS) with multiple virtual leaders and semi-Markov switching topologies is proposed in this article. A time-varying formation tracking protocol with an error compensation term is introduced to mitigate the effects among the real leader and the virtual leaders. Sufficient and necessary conditions are derived based on the Routh–Hurwitz criterion. Then, a semi-Markov chain with communication delay is presented to characterize the topology switching process in different communication failure scenarios. The FCS is constructed with a varying gain controller, and its tracking performance is analyzed with the Lyapunov–Krasovskii functional. Finally, substantial experiments with Quanser Qbot2 mobile formation demonstrate the effectiveness of position and velocity tracking for FCS.
TL;DR: In this paper , a system identification of an uncommon quadrotor in hover is discussed, where the attitude dynamics of the quadrotors are derived from the Newton-Euler formulation.
Abstract: In this article, system identification of an uncommon quadrotor in hover is discussed. Two counter-rotating big rotors on the longitudinal axis and two counter-rotating small tilt rotors on the lateral axis form this quadrotor configuration. First, the nonlinear dynamic model of this vehicle is derived from Newton–Euler formulation. Next, using the approximate linear hover model and the suitable rotor mixing matrix for axes decoupling, simplified attitude dynamics of this quadrotor are obtained. However, the effects of sensor delays, flexible modes of the airframe, and inexact decoupling are not visible in this diagonal model, which is mainly based on physical principles. Therefore, a system identification method is used to obtain a more accurate model, which is required for control design. Hence, during parametric identification of nominal model coprime factors, a small robust control criterion is targeted. Then, the frequency response function of the proposed quadrotor prototype in hover is obtained. Next, the linear parametric model of the vehicle is estimated by solving optimally conditioned least squares and subsequent Gauss–Newton problems. After that, using validation-based uncertainty quantification, the uncertain model set is constructed around the estimated coprime factors. The resulting model set and its robust control relevance are depicted.
TL;DR: In this article , a multiagent deep reinforcement learning (MADRL)-enabled framework for decentralized cooperative control of a novel dual-branch damping controller for both low-frequency oscillation (LFO) and ultralow-frequency (ULFO) was developed, where multiagents are centrally trained to obtain the coordinated adaptive control policy while being executed in a decentralized manner to provide the optimal parameter setting for each controller with only local states.
Abstract: This study develops a multiagent deep reinforcement learning (MADRL)-enabled framework for the decentralized cooperative control of a novel dual-branch (DB) damping controller for both low-frequency oscillation (LFO) and ultralow-frequency oscillation (ULFO). It has two branches, each of which consists of a proportional resonance (PR) and a second-order polynomial that is designed to handle target oscillation modes. To improve the robustness of the controller to system uncertainties, MADRL is developed, where multiagents are centrally trained to obtain the coordinated adaptive control policy while being executed in a decentralized manner to provide the optimal parameter setting for each controller with only local states. Comparisons with the IEEE 10-machine 39-bus system demonstrate that the proposed method achieves better robustness to uncertainties, lower communication delay, and single-point failure, as well as damping control performances for both LFO and ULFO.
TL;DR: In this article , the authors proposed a novel computational-efficient fast MPC (FMPC) design method for the M-WECs requiring complex linear hydrodynamic models.
Abstract: Recently, multi-float and multi-mode-motion wave energy converters (M-WECs) have been developed to improve energy conversion capability. Although model predictive control (MPC) can be very effective to solve the constrained energy maximization control problem of point absorber WECs, the increased complexity of the M-WEC hydrodynamics can bring significant challenges due to computational demand. This brief proposes a novel computational-efficient fast MPC (FMPC) design method for the M-WECs requiring complex linear hydrodynamic models. The controller design objective is to maximize the energy conversion with some available wave forecasting information and to satisfy state and control input constraints to ensure safe operation. The main advantage of the proposed FMPC is the reduced computational burden with a negligible impact on performance. A demonstrative numerical simulation based on a 1:50 laboratory-scale M-WEC design, M4, for which linear hydrodynamics has been verified experimentally, is presented to verify the efficacy of the proposed control method in terms of both computational load and energy output.
TL;DR: In this paper , a probabilistic drift-type nonstationary oscillating slow feature model is proposed to separate oscillating patterns and non-stationary variations from measured data, and the feature extractor parameters are estimated under a variational Bayesian framework to incorporate the prior information and obtain corresponding posterior distributions.
Abstract: Extraction of underlying patterns from measured variables is central to various data-driven control applications, such as soft-sensor modeling, statistical process monitoring, and fault detection and diagnosis. More often than not, the observed variables display nonstationary characteristics and oscillations due to the changes in operating conditions and problems in controller tuning. Such variations pose a great challenge to conventional feature extraction methods. Hence, we present a probabilistic drift-type nonstationary oscillating slow feature model that can separate oscillating patterns and nonstationary variations from measured data. Furthermore, the measurement noise of each variable is independently modeled to account for the fact that not all the observed variables have the same level of uncertainty. Finally, the feature extractor parameters are estimated under a variational Bayesian framework to incorporate the prior information and obtain corresponding posterior distributions. The proposed methodology is applied to solve a fouling monitoring problem for an industrial oil production process.
TL;DR: In this paper , an indoor simultaneous localization and mapping (SLAM) problem for a mobile robot measuring the phase of the signal backscattered by a set of passive radio ultra high frequency identification (ID) tags, deployed in unknown position on the ceiling of the environment, is considered.
Abstract: In this article, we consider an indoor simultaneous localization and mapping (SLAM) problem for a mobile robot measuring the phase of the signal backscattered by a set of passive radio ultra high frequency identification (ID) tags, deployed in unknown position on the ceiling of the environment. The solution approach is based on the introduction, for each radio frequency identification (RFID) tag observed, of a multihypothesis extended Kalman filter (MHEKF) which, based on the measured phases and on the wheel encoder readings, provides an estimate of the range and of the bearing of the observed tag with respect to the robot. This information is then used in an extended Kalman filter (EKF) solving the SLAM problem. Since an effective range and bearing estimate is available only after some steps, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed tag. As shown through numerical and experimental results, this makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects or even the unexpected change of the position of a tag.
TL;DR: In this paper , the authors link optimization approaches from hierarchical least-squares programming to instantaneous prioritized whole-body robot control and show that the hierarchical Newton's method without complicated adaptations is not appropriate in the acceleration domain.
Abstract: This work links optimization approaches from hierarchical least-squares programming to instantaneous prioritized whole-body robot control. Concretely, we formulate the hierarchical Newton’s method which solves prioritized nonlinear least-squares problems in a numerically stable fashion even in the presence of kinematic and algorithmic singularities of the approximated kinematic constraints. These results are then transferred to control problems which exhibit the additional variability of time. This is necessary to formulate acceleration-based controllers and to incorporate the second-order dynamics. However, we show that the Newton’s method without complicated adaptations is not appropriate in the acceleration domain. We therefore formulate a velocity-based controller which exhibits second-order proportional derivative (PD) convergence characteristics. Our developments are verified in toy robot control scenarios as well as in complex robot experiments which stress the importance of prioritized control and its singularity resolution.
TL;DR: In this paper , a model predictive control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components is presented. But the authors consider both series and parallel configurations and investigate the performance of algorithms for solving this problem.
Abstract: We present a model predictive control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components. Series and parallel configurations are considered. By combining a point-mass aircraft dynamical model with models of electrical losses and losses in the gas turbine, the fuel consumed over a given future flight path is minimized subject to constraints on the battery, electric motor, and gas turbine. The optimization is formulated as a convex problem under mild assumptions and its solution is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. We investigate the performance of algorithms for solving this problem. An alternating direction method of multipliers (ADMM) algorithm is proposed and compared with a general purpose convex interior point solver. We also show that the ADMM implementation reduces the required computation time by orders of magnitude in comparison with a general purpose nonlinear programming solver, making it suitable for real-time supervisory energy management control.
TL;DR: In this article , a compact maximum correntropy-based Kalman filter (CMC-KF) is derived based on the proposed metric, which performs well both with and without non-Gaussian noises.
Abstract: This brief investigates the maximum correntropy-based Kalman filtering problem for exoskeleton orientation by fusing signals from accelerometers and gyroscopes. The conventional error state Kalman filter (ESKF) has been applied to many applications for orientation estimation. However, its performance degenerates remarkably with external acceleration. In this brief, the influence of the external acceleration is analyzed and the dilemma of the conventional ESKF is declared. To address this issue, a weighted correntropy and a novel correntropy-induced metric (CIM) are provided. Then, a compact maximum correntropy-based Kalman filter (CMC-KF) is derived based on the proposed metric, which performs well both with and without non-Gaussian noises. Finally, a compact maximum correntropy-based ESKF (CMC-ESKF) is designed for orientation estimation of exoskeletons. A series of experiments are conducted to verify the effectiveness of the proposed method. Results reveal that the proposed algorithm is significantly better than the conventional ESKF and the gradient descent (GD) method, especially with external accelerations.
TL;DR: In this article , an adaptive algorithm based on modifier adaptation is proposed to deal with the structural plant-model mismatch by estimating the mismatch in the cost and constraints of the optimal control problem.
Abstract: Cold atmospheric plasmas (CAPs) are increasingly used for applications requiring the processing of heat- and pressure-sensitive (bio)materials. A key challenge in model-based control of CAPs arises from the high-computational requirements of theoretical plasma models as well as lack of mechanistic understanding of plasma-surface interactions. Thus, control strategies that rely on simple, physics-based models that can be adapted to mitigate plant-model mismatch will be particularly advantageous for CAP applications. This article presents an optimal control approach for controlling the nonlinear and cumulative effects of CAPs delivered to a target surface using a simple system model. Through parsimonious input parameterization, the solution to the optimal control problem (OCP) is given by an arc sequence that does not include any singular arcs. A data-driven adaptive algorithm based on modifier adaptation is proposed to deal with the structural plant-model mismatch by estimating the mismatch in the cost and constraints of the OCP. The adaptive approach is shown to converge to a Karush–Kuhn–Tucker (KKT) point of the OCP for the true system. Moreover, a control strategy based on feedback linearization and derivative estimation is proposed for online tracking of path constraints in the presence of disturbances and model uncertainty. The proposed approach is demonstrated by simulations and real-time control experiments on a kilohertz-excited atmospheric pressure plasma jet in helium, in which the plasma treatment time is minimized while delivering a desired amount of nonlinear thermal effects to the target surface.
TL;DR: In this article, the resilient unscented Kalman filtering fusion issue is investigated for a class of nonlinear systems under the dynamic event-triggered mechanism where each sensor node transmits the measurement information to its corresponding local filter in an intermittent way.
Abstract: In this article, the resilient unscented Kalman filtering fusion issue is investigated for a class of nonlinear systems under the dynamic event-triggered mechanism where each sensor node transmits the measurement information to its corresponding local filter in an intermittent way. Compared with its static counterpart, the dynamic event-triggered scheme is capable of scheduling the frequency of data transmission in a more efficient way, thereby better reducing communication burden and energy consumption. In addition, for each local filter, the variation of the filter gain is characterized by a multiplicative noise term. To cope with the intractable problem of computing the cross covariance between local filters, the sequential covariance intersection fusion strategy is introduced into the proposed distributed fusion framework. Finally, the proposed algorithm is applied to a maneuvering target tracking scenario with multiple unmanned aerial vehicles, and both numerical simulations and hardware experiments are provided to elucidate the effectiveness and practicality of the proposed filtering scheme.
TL;DR: In this article , a learning-based adaptive optimal control approach for flotation processes subject to input constraints and disturbances using adaptive dynamic programming (ADP) along with double-loop iteration is presented.
Abstract: This article presents a learning-based adaptive optimal control approach for flotation processes subject to input constraints and disturbances using adaptive dynamic programming (ADP) along with double-loop iteration. First, the principle of the operational pattern is adopted to preset reagents’ addition based on the feeding condition. Then, this article leverages a deep learning model, which is composed of multiple neural layers to detect flotation indexes directly from the raw froth images. After that, the tracking error between the detected flotation indexes and the reference values can be minimized by using ADP-based double-loop iteration. Particularly, a policy-iteration (PI) method is utilized for the proposed learning-based ADP algorithm. In the inner loop, the optimal control problem is formulated as a linear quadratic regulator (LQR) problem using the low-gain feedback design method. In the outer loop, the design parameters, i.e., weighting matrices, are tuned automatically to satisfy the input constraints. Finally, the analytical results demonstrate that the proposed scheme can guarantee asymptotic tracking in the presence of actuator saturation and disturbances.
TL;DR: In this article , a fault diagnosis methodology for rotating machinery with limited expert interaction is proposed, where a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation.
Abstract: Recently, research on the fault diagnosis of rotating machinery, especially for the compound or unknown cases, has drawn increasing attention. Some advanced learning-based approaches have achieved good fault diagnosis performance to some degree. However, in practical applications, how to utilize prior knowledge as fully as possible for fault diagnosis with constraints of limited expert interaction remains an open issue. In this brief, a fault diagnosis methodology of rotating machinery with limited expert interaction is proposed. With related feature extraction techniques, a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation. In addition, the broad learning system (BLS) is exploited to achieve fast incrementally updating or retrain procedures with high diagnostic accuracy in different diagnosis scenarios. Several experiments are conducted on a real-world rotating machinery fault diagnosis (RMFD) experimental platform. Compared with other existing advanced approaches, the diagnosis performance of the proposal shows high stability and flexibility. The annotation cost of experts is also significantly reduced, which makes the proposal more suitable for dealing with practical problems.
TL;DR: In this paper , a probabilistic drift-type nonstationary oscillating slow feature model is proposed to separate oscillating patterns and non-stationary variations from measured data, and the feature extractor parameters are estimated under a variational Bayesian framework to incorporate the prior information and obtain corresponding posterior distributions.
Abstract: Extraction of underlying patterns from measured variables is central to various data-driven control applications, such as soft-sensor modeling, statistical process monitoring, and fault detection and diagnosis. More often than not, the observed variables display nonstationary characteristics and oscillations due to the changes in operating conditions and problems in controller tuning. Such variations pose a great challenge to conventional feature extraction methods. Hence, we present a probabilistic drift-type nonstationary oscillating slow feature model that can separate oscillating patterns and nonstationary variations from measured data. Furthermore, the measurement noise of each variable is independently modeled to account for the fact that not all the observed variables have the same level of uncertainty. Finally, the feature extractor parameters are estimated under a variational Bayesian framework to incorporate the prior information and obtain corresponding posterior distributions. The proposed methodology is applied to solve a fouling monitoring problem for an industrial oil production process.