The integration of look-ahead information into Model Predictive Control (MPC) frameworks has shown promise for intelligent transportation systems. However, transitioning Predictive Cruise Control (PCC) system research into practical application poses challenges due to numerous weighting parameters and increased computational demands in complex driving environments. Although the Weighted Sum Method is commonly used in PCC system research to balance fuel consumption and trip time objectives, it requires time-consuming weight tuning and often results in suboptimal performance due to fixed weighting parameters. To address this, this paper proposes a Utopia-tracking Model Predictive Control (UTM-MPC) controller, where the cost function is reformulated as the sum of the distances between the objectives and the average Utopia point over the prediction horizon. By analyzing the Pareto front of the PCC optimization problem under varying slope profiles extracted from digital map data, we demonstrate that the proposed UTM-MPC effectively leverages the geometric characteristics of the Pareto front to identify preferred trade-off solutions. The adaptive weighting mechanism—derived from the online-calculated Utopia point—enhances the robustness of the PCC system under complex and dynamic driving conditions. To mitigate the computational burden associated with integrating UTM-MPC into the MPC framework, we introduce a tailored neighboring extremal-based solving algorithm. Leveraging the receding horizon nature of MPC, this method requires only minimal updates to efficiently identify an optimal solution near the nominal trajectory from the previous sampling instance. Simulation results show that the UTM-MPC controller, with its adaptive weighting strategy, consistently outperforms the traditional Weighted Sum Method in terms of both fuel efficiency and trip time.

We present an integrated spatio-temporal framework for multi-range traction power and speed prediction for connected vehicles (CVs). It combines data-driven and model-based strategies to enable CVs energy efficiency optimization. The proposed framework focuses on urban arterial corridors with signalized intersections, and leverages the historical and real-time data collected from CVs and infrastructure to predict location-specific traction loads (e.g. acceleration at intersections), and augment them with time-specific speed profiles (e.g., stop duration at intersections). A Bayesian network is developed to provide a long-term load prediction informed by probabilistic analysis of historical traffic data at intersections and between intersections. Moreover, a shockwave profile model is adopted for modeling the queuing process at intersections by leveraging vehicle-to-infrastructure (V2I) communications, providing a short-range prediction of the vehicle speed with an enhanced accuracy. The benefits of the proposed load prediction framework are demonstrated for energy management of connected hybrid electric vehicles (C-HEVs). By incorporating the predicted loads into a multi-horizon model predictive controller (MPC), integrated power and thermal management of light-duty C-HEVs is enabled over real-world driving cycles, demonstrating a near globally-optimal fuel consumption over the entire trip with a < 1% deviation from dynamic programming (DP) results.

Accurate flow predictions are critical for energy-optimal path planning of AUVs with endurance requirements. However, the complex dynamics of ocean currents make it difficult to achieve accurate flow predictions. For an AUV with flow and location sensing capabilities, one can optimize vehicle actions so that the flow information collected along the vehicle path reduces flow prediction uncertainty, referred to as active flow perception. In this paper, we propose an energy-optimal path planning approach that incorporates active flow perception. The proposed approach achieves the objectives of vehicle energy consumption minimization and flow prediction uncertainty reduction. To quantify flow prediction uncertainty, an empirical flow model parameterized using the proper orthogonal decomposition (POD) is constructed based on historical data. Assuming negligible unmodeled dynamics in the POD model, the flow prediction uncertainty is evaluated by the Cramer-Rao (CR) bound of estimated model parameters. To establish active flow perception combined with energy optimal path planning, we formulate the cost to be minimized during path planning in terms of vehicle energy using estimated flow parameters and CR bound. Through simulations, the proposed approach is compared with approaches that plan energy-optimal paths using i) true flow and ii) flow predictions without active flow perception. Simulation results demonstrate the satisfactory energy-saving performance of the proposed approach.

Integrated power system combines electrical power for both ship service and electric propulsion loads by forming a microgrid. In this article, a battery/flywheel hybrid energy storage system (HESS) is studied to mitigate load fluctuations in a shipboard microgrid. This article focuses on how to determine the reference operation state of the flywheel, which depends on both future power load and the power split between the battery and flywheel. Two control strategies are proposed-an optimization-based approach and a lookup-table-based approach. Case studies are performed in different sea conditions, and simulation results demonstrate that the proposed control strategies outperform baseline control strategies in terms of power fluctuation mitigation and HESS power-loss reduction. A comparison between the two proposed approaches is performed, where their performances are quantified, the advantages and disadvantages of each strategy are analyzed, and the cases where they are most applicable are highlighted.

This paper considers a group of mobile sensing agents in a flow field and presents a distributed method for motion tomography (MT) that estimates the underlying flow field. MT formulates an underdetermined nonlinear system of equations as an inverse problem. Inspired by the Kaczmarz method which is an optimization approach for solving a linear system of equations, our previous work developed a nonlinear Kaczmarz method that solves the system of equations associated with MT. Considering distributed multi-agent systems for MT, this paper extends the nonlinear Kaczmarz method into a distributed framework. The distributed nonlinear Kaczmarz method is developed by formulating a constrained consensus problem that belongs to a class of projected consensus algorithms. To study the convergence and consensus for the method, its linear case is analyzed first and then its nonlinear case is discussed. The nonlinear case of the method is further validated through simulations by estimating a gyre flow field using mobile sensor networks with different numbers of neighboring agents. Resulting estimated flow fields are compared with a flow field estimated by its centralized counterpart.

A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost global navigation satellite system (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach in improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao-Blackwellized particle filter is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared with existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 m under open sky conditions.

Cooperative map matching (CMM) uses the global navigation satellite system (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. While increasing accuracy is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This paper presents a theoretical study that establishes a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule. The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the real traffic data. The contributions of this paper include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.

Cooperative localization with map matching has been shown to reduce Global Navigation Satellite System (GNSS) localization error from several meters to sub-meter level by fusing the GNSS measurements of four vehicles in our previous work. While further error reduction is expected to be achievable by increasing the number of vehicles, the quantitative relationship between the estimation error and the number of connected vehicles has neither been systematically investigated nor analytically proved. In this work, a theoretical study is presented that analytically proves the correlation between the localization error and the number of connected vehicles in two cases of practical interest. More specifically, it is shown that, under the assumption of small non-common error, the expected square error of the GNSS common error correction is inversely proportional to the number of vehicles, if the road directions obey a uniform distribution, or inversely proportional to logarithm of the number of vehicles, if the road directions obey a Bernoulli distribution. Numerical simulations are conducted to justify these analytic results. Moreover, the simulation results show that the aforementioned error decrement rates hold even when the assumption of small non-common error is violated.

A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectively avoids the need to use the unknown control input directly, and enables the estimation of the un-modeled dynamics with a relatively simple algorithm. Moreover, it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un-modeled dynamics. Finally, simulation studies are presented to demonstrate the effectiveness of the proposed method.

As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using thel1norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA2K, is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model.

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.