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Uncertainties in the quality, quantity, and operational time of used products pose a challenge to the management of remanufacturing systems. In addition, it becomes a necessity to optimize the operation of the remanufacturing system to balance the quality of products, remanufacturing efficiency, and service level. In this study, a stochastic discrete-time dynamical model is proposed to represent a remanufacturing system, where the relationship between the market satisfaction, inventory status, and operational actions is explicitly modeled. This includes production and inventory planning, resource allocation and acquisition. To handle uncertainties, a stochastic model predictive control approach is proposed to plan the actions that optimize the remanufacturing efficiency. Our results in the simulation examples show that: (a) without supplies, the remanufacturing system has better stability and robustness than a conventional manufacturing system with the same initial stocks; and (b) with insufficient initial stocks, the remanufacturing system demands fewer and more gradual supplies, thereby keeping the system stable. Finally, a sensitivity analysis is conducted for testing the performance of the remanufacturing system. By changing the operational action capacity, different state equilibria are discovered, which correspond to distinct system response characteristics. The study reveals notable managerial insights and effects of product commonality, demand patterns, and operational actions scheduling on the efficiency of the remanufacturing system.

This article proposes a flight control system for hydrofoiling catamaran sailboats. The main goal was to create a control system that could replace or guide the crew in the tracking of a commanded ride height. The control system exploits the coordination between the main lift source surface, the daggerboard foil, and the stabilization surfaces, the rudder foils. Compared with conventional aircraft, the daggerboard foil is the primary stabilization surface unlike a wing or the flaps of an aircraft. Both the model and the sea trials revealed a high potential sensitivity of the closed-loop systems to wave disturbances. This sensitivity arises from both the hydrodynamic effect of the waves on the foils and the noise in the ride height estimates due to the waves’ random water height variation. We describe the full state feedback (FSF) controller, for which the linear–quadratic regulator (LQR) theory-based gains were initially adopted and then modified with manual tuning, so that the developed system was able to perform almost as well as the highly trained helmsmen. Ancillary components of the control system, such as the ride height estimator, are also described.

Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor testing facility and made to track an inclined, circular path at different tangential velocities under ambient wind conditions. Controller performance was evaluated via multiple metrics, such as position tracking error, velocity tracking error, and onboard computation time. Challenges related to the use of computationally limited embedded hardware and flight in an outdoor environment are addressed with proposed solutions.

Connected and autonomous vehicles (CAVs) have situational awareness that can be exploited for optimal power and thermal management. In this article, we develop a hierarchical model predictive control (H-MPC) strategy for eco-cooling of CAVs, which reduces energy consumption through real-time prediction and multi-timescale and multi-layer optimization. The application of the proposed H-MPC is studied for battery thermal and energy management of an electric vehicle (EV). Our H-MPC approach addresses the uncertainty in the long-term preview of the vehicle speed through robust constraint handling to prevent constraint violation. The simulation results show that compared with a conventional battery thermal management (BTM) strategy, the proposed robust H-MPC saves the battery energy by up to 5.4% under the uncertainties in the long-term vehicle speed predictions in an urban CAV operation scenario.

This article provides an overview of fuzzy encoded Markov chains (FEMCs), which are finite-state Markov chains applied to transitions between fuzzy sets that encode signal or variable values. FEMCs can be used for modeling of dynamic systems, predicting/forecasting future signal values, for state estimation, and for the development of fuzzy rules for control. Under suitable assumptions, the state possibility distribution can be propagated using FEMC models in a similar manner as the state probability distribution using conventional Markov chain models. The article first discusses FEMC theory, procedures to identify FEMCs from data, and the use of FEMCs for forecasting and control. Then, we introduce, for the first time, observers for partially observable FEMCs. The observer theory is developed and computational approaches are presented. Finally, we briefly review some FEMC applications in the automotive domain.

This article considers predictive control of the jet engine and electrical power distribution system of a more electric aircraft (MEA) with asynchronous distributed controllers. The shafts of the engine spools are mechanically coupled to the electrical generators of the microgrid, linking the dynamics of the two subsystems. As the subsystems and their controllers are designed by different entities, the preservation of subsystem privacy must be an integral part of any coordination mechanism and, due to the underlying subsystems having differing time scales in their dynamics, the subsystem control updates are not guaranteed to be synchronized. To address these requirements, a distributed model predictive control (D-MPC) algorithm based on the alternating direction method of multipliers (ADMM) is proposed, which accounts for and exploits the differing control update rates of the engine and power subsystem controllers while preserving privacy. An extension to the Algorithm that seeks to minimize the required communication volume by downsampling the interactions to the rate of the engine time scale is also presented. Simulation results on a high-fidelity nonlinear system model demonstrate that the distributed controllers can outperform a decentralized controller and their performance can match that of a fully centralized controller.

Incorporating traffic information in power management optimization process for electrified and connected vehicles offers opportunities for improving fuel economy. Integrating the management of thermal load (such as those used for heating, ventilation, & air conditioning (HVAC) of the passenger compartment, and for the battery cooling) with the power management process can provide even greater benefits for connected and automated vehicles (CAVs). However, given the relatively slow dynamics associated with the thermal subsystems, the lack of reliable power and thermal loads prediction over an extended prediction horizon is the main challenge for efficient thermal management using model predictive control (MPC). This paper presents a hierarchical two-layer MPC scheme which exploits vehicle speed and traffic preview predictions over short and long prediction horizons to schedule optimal thermal trajectories for the cabin and battery cooling in hybrid electric vehicles (HEVs) via a novel intelligent online constraint handling (IOCH) approach. These trajectories are next incorporated into the vehicle-level controller to determine the proper power split between electric motor and internal combustion engine (ICE). We present the development and experimental validation of control-oriented models used for prediction of the vehicle thermal dynamics and loads over a long planning horizon. Compared to a more traditional single-layer MPC approach, the proposed two-layer MPC shows that depending on the driving cycle and traffic conditions, 2.2% to 5.3% reductions in HEV fuel consumption can be achieved for urban driving and congested city driving cycles, respectively, in CAV operation scenario. This fuel economy improvement is a direct result of taking proactive actions through real-time prediction and optimization to avoid conservative and inefficient thermal responses, while enforcing cabin and battery operating constraints.

The Van Doorne continuously variable transmission (CVT) provides a continuous range of gear ratios (GRs) compared to a common discrete transmission, offering a potential improvement in driveline and vehicle efficiency. However, poor understanding of the dynamics of slip inherent to the CVT can lead to conservative control designs unable to realize the benefits. This brief presents a control architecture with the goal of estimating and controlling the CVT using recently developed models, given only sensors likely to be included on a production vehicle. The current state of the transmission and the disturbance torque from the driveline shaft are estimated by an extended Kalman filter (EKF). A model predictive controller (MPC) uses these estimates to optimize a feedback control sequence defined over a receding horizon to improve drivability and efficiency while enforcing safety constraints on the slip. The control scheme is validated through nonlinear simulations of a CVT, which demonstrate the ability to track both GR and slip set points while enforcing constraints.

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Efficient performance of a number of engineering systems is achieved through different modes of operation - yielding systems described as "hybrid", containing both real-valued and discrete decision variables. Prominent examples of such systems, in space applications, could be spacecraft equipped with 1) a variable-$I_{\text{sp}}$, variable-thrust engine or 2) multiple engines each capable of switching on/off independently. To alleviate the challenges that arise when an indirect optimization method is used, a new framework --- Composite Smooth Control (CSC) --- is proposed that seeks smoothness over the entire spectrum of distinct control inputs. A salient aftermath of the application of the CSC framework is that the original multi-point boundary-value problem can be treated as a two-point boundary-value problem with smooth, differentiable control inputs; the latter is notably easier to solve, yet can be made to accurately approximate the former hybrid problem. The utility of the CSC framework is demonstrated through a multi-year, multi-revolution heliocentric fuel-optimal trajectory for a spacecraft equipped with a variable-$I_{\text{sp}}$, variable-thrust engine., Comment: 51 pages, 9 figures; pre-print submitted to Acta Astronautica

Equipping a spacecraft with multiple solar-powered electric engines (of the same or different types) compounds the task of optimal trajectory design due to presence of both real-valued inputs (power input to each engine in addition to the direction of thrust vector) and discrete variables (number of active engines). Each engine can be switched on/off independently and "optimal" operating power of each engine depends on the available solar power, which depends on the distance from the Sun. Application of the Composite Smooth Control (CSC) framework to a heliocentric fuel-optimal trajectory optimization from the Earth to the comet 67P/Churyumov-Gerasimenko is demonstrated, which presents a new approach to deal with multiple-engine problems. Operation of engine clusters with 4, 6, 10 and even 20 engines of the same type can be optimized. Moreover, engine clusters with different/mixed electric engines are considered with either 2, 3 or 4 different types of engines. Remarkably, the CSC framework allows us 1) to reduce the original multi-point boundary-value problem to a two-point boundary-value problem (TPBVP), and 2) to solve the resulting TPBVPs using a single-shooting solution scheme and with a random initialization of the missing costates. While the approach we present is a continuous neighbor of the discontinuous extremals, we show that the discontinuous necessary conditions are satisfied in the asymptotic limit. We believe this is the first indirect method to accommodate a multi-mode control of this level of complexity with realistic engine performance curves. The results are interesting and promising for dealing with a large family of such challenging multi-mode optimal control problems., Comment: 45 pages, 11 figures; Preprint submitted to Acta Astronautica

This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear system by the response of a linear model with a set-bounded prediction error, where a state-and-input dependent bound on the prediction error is explicitly characterized using logarithmic norms. The online optimization is reduced to a convex quadratic program with linear inequality constraints. Two numerical examples are reported., 8 pages, 2 figures

Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the exhaust gas recirculation valve position. The control development is based on a control-oriented model that captures the effects of throttle position, exhaust gas recirculation valve position, and spark timing on the combustion phasing. Under the assumption that in-cylinder pressure information is available, an adaptive extended Kalman filter approach is used to estimate the exhaust gas recirculation rate into the intake manifold based on combustion phasing measurements. The estimation algorithm is adaptive since the cycle-to-cycle combustion variability (output covariance) is not known a priori and changes with operating conditions. A linear quadratic regulator controller is designed to maintain optimal combustion phasing while maximizing exhaust gas recirculation levels during load transients coming from throttle tip-in and tip-out commands from the driver. During throttle tip-outs, however, a combination of a high exhaust gas recirculation rate and an overly advanced spark, product of the dynamic response of the system, generates a sequence of misfire events. In this work, an explicit reference governor is used as an add-on scheme to the closed-loop system in order to avoid the violation of the misfire limit. The reference governor is enhanced with model-free learning which enables it to avoid misfires after a learning phase. Experimental results are reported which illustrate the potential of the proposed control strategy for achieving an optimal combustion process during highly diluted conditions for improving fuel efficiency.

The state of the art of modelling, control, and optimisation is discussed for automated road vehicles that may utilise wireless vehicle-to-everything (V2X) connectivity. The appropriate tools to ad...

This paper describes a Markov decision process approach to a robust spacecraft mission control policy that maximizes the expected value of science reward assuming a circular orbit. The control policy that governs mission steps can be computed off-board or onboard depending upon the availability of communication bandwidth and on-board computational resources. This paper considers a sample science mission, where the spacecraft collects data from celestial objects viewable only within a certain orbit true anomaly window. Science data collection requires the spacecraft to slew its instrument(s) toward each target, and continue pointing in the direction of the target while the spacecraft traverses its orbit. Robustness and stochastic optimization of scientific reward, is achieved at the cost of computational complexity. Approximate dynamic programming (ADP) is exploited to reduce the computational time and effort to manageable levels and to treat larger problem sizes. The proposed ADP algorithm partitions the state-space based on true anomaly regions, enabling grouping of adjacent science targets. Results of a simulation case study demonstrate that our proposed ADP approach performs quite well for reasonable ranges of key problem parameters.

In this paper, we consider optimization of trajectories for automotive vehicle rollover testing. In particular, worst-case trajectories that are most likely to cause rollover accidents are determined through trajectory optimization. Our approach combines online local-model identification and gradient-based input update, and can be applied to black-box type models, e.g., a high-fidelity vehicle dynamics model given as a simulation code and not as an explicit set of equations. With our approach, a library of worst-case trajectories corresponding to different operating conditions (e.g., vehicle mass, road surface conditions, etc.) can be constructed and subsequently used in hardware tests.

We propose a novel vision-based constrained control solution to execute aircraft landing on an unknown runway. The method is based on an extended command governor which is tailored for a specific class of constrained linear systems with a single uncertain modeling parameter. The computational burden is reduced by exploiting a robust strongly returnable set in place of a robust invariant set and computing this set through the use of a binomial expansion. Numerical results illustrate the ability to ‘safely’ align the aircraft with the runway while satisfying the specified terminal constraints to execute the flare. Here the terminal constraints are efficiently handled by transforming them into time-varying constraints.

In this paper, we consider Newton–Kantorovich type methods for solving control-constrained optimal control problems that appear in model predictive control. Conditions for convergence are established for an inexact version of the Newton–Kantorovich method applied to variational inequalities. Based on these results, two groups of algorithms are proposed to solve the optimality system. The first group includes exact and inexact Newton and Newton–Kantorovich implementations of the sequential quadratic programming. In the second group, exact and inexact Newton and Newton–Kantorovich methods are developed for solving a nonsmooth normal map equation equivalent to the optimality system. Numerical simulations featuring examples from the aerospace and automotive domain are presented, which show that inexact Newton–Kantorovich type methods can achieve significant reduction of the computational time.

This paper introduces a continuous-time constrained control strategy, which mimics the behavior of a traditional model predictive control scheme using dynamic feedback. The approach is based on the idea that the solution of the optimal control problem can be embedded in the internal states of a dynamic control law running in parallel to the system. Using input-to-state stability arguments, it is shown that if the controller dynamics are sufficiently fast with respect to the plant dynamics, the interconnection between the two systems is asymptotically stable. Additionally, it is shown that, by augmenting the proposed scheme with an add-on unit known as an explicit reference governor, it is possible to guarantee recursive feasibility and limit the residual of the optimal control problem, regardless of any changes in the desired set point. Numerical examples featuring aerospace applications demonstrate the effectiveness of the proposed scheme.

This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a 1.5 kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (SOC) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial SOC value. If starting from a high SOC value, the battery energy is used till a turnpike value of the SOC is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to 2.7 h of flight time.

This paper presents an optimization-based technique for triggering measurements in a hidden Markov model so that the number/rate of measurements is minimized while maintaining accuracy of the state estimates to satisfy a prescribed constraint. Here, the accuracy of a state estimate is characterized by the entropy of the distribution associated with the estimate. After introducing the general approach, this paper also discusses its application to precision agriculture. In particular, we consider site-specific monitoring of soil nitrate levels and show through simulations that the required number of measurements can be reduced with the proposed technique compared to conventional strategies where measurements are taken at a fixed rate.

In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches for safety assurance. Our safety system consists of two modules namely handcrafted safety and dynamically-learned safety. The handcrafted safety module is a heuristic safety rule based on common driving practice that ensure a minimum relative gap to a traffic vehicle. On the other hand, the dynamically-learned safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, the dynamically-leaned safety module incorporates a model lookahead beyond the immediate reward of reinforcement learning to predict safety longer into the future. If one of the future states leads to a near-miss or collision, then a negative reward will be assigned to the reward function to avoid collision and accelerate the learning process. We demonstrate the capability of the proposed framework in a simulation environment with varying traffic density. Our results show the superior capabilities of the policy enhanced with dynamically-learned safety module.

This paper addresses the hierarchical optimization of speed and gearshift control for battery electric vehicles using short-range traffic information. To achieve greater electric motor efficiency, a multi-speed transmission is employed, whose control involves discrete-valued gearshift signals. To overcome the computational difficulties in solving the integrated speed-and-gearshift optimal control problem that involves both continuous and discrete-valued optimization variables, we propose a hierarchical procedure to decompose the integrated hybrid problem into purely continuous and discrete sub-problems, each of which can be efficiently solved. We show, by simulations in various driving scenarios, that the hierarchical optimization of speed and gearshift control can achieve greater energy efficiency than other typical approaches.

In this brief, we consider the constrained optimization problem underpinning model predictive control (MPC). We show that this problem can be decomposed into an unconstrained optimization problem with the same cost function as the original problem and a constrained optimization problem with a modified cost function and dynamics that have been precompensated according to the solution of the unconstrained problem. In the case of linear systems subject to a quadratic cost, the unconstrained problem has the familiar LQR solution and the constrained problem reduces to a minimum-norm projection. This implies that solving linear MPC problems is equivalent to precompensating a system using LQR and applying MPC to penalize only the control input. We propose to call this a constraint-separation principle and discuss the utility of both constraint separation and general decomposition in the design of MPC schemes and the development of numerical solvers for MPC problems., 8 pages, 2 figures, submitted to Automatica

We consider a finite-horizon continuous-time optimal control problem with nonlinear dynamics, an integral cost, control constraints and a time-varying parameter which represents perturbations or uncertainty. After time-discretization of the problem we employ a Model Predictive Control (MPC) algorithm with a shrinking horizon, which uses a "prediction"/forecast for the uncertain parameter and (possibly inexact) measurements of the state vector, and generates a piecewise constant control signal by solving auxiliary open-loop control problems. In our main result we derive an estimate of the difference between the MPC-generated control and the optimal feedback control, both obtained for the same value of the perturbation parameter, in terms of the step-size of the discretization and the measurement error. We also give an estimate for a norm of the difference between the MPC-generated control and the optimal open-loop control in the problem with the "true" value of the uncertain parameter, depending on the prediction error.

Translational relative motion of a spacecraft in a gravity field can be induced by orientation changes. As orientation changes can be effected by electrically driven momentum exchange devices, for instance, reaction wheels, such maneuvers do not require fuel or thrust and hence can be performed during propulsion system failures or when the onboard fuel is limited. In this paper, a multi-mode controller is developed to effect such maneuvers which switches between control gains and intermediate target trajectories to extend the closed-loop domain of attraction of a conventional LQR controller and to satisfy state and control constraints.

We consider a control constrained nonlinear optimal control problem under perturbations represented by a parameter which is a function of time. Our main assumptions involve smoothness of the functi...

This paper addresses the problem of vehicle rollover avoidance using reference governors (RGs) applied to modify the driver steering input in vehicles with an active steering system. Several RG designs are presented and tested with a detailed nonlinear simulation model. The vehicle dynamics are highly nonlinear for large steering angles, including the conditions where the vehicle approaches a rollover onset, which necessitates RG design changes. Simulation results show that RG designs are effective in avoiding rollover. The results also demonstrate that the controllers are not overly conservative, adjusting the driver steering input only for very high steering angles. IEEE

In this paper, we provide a solution to the HJB equation associated with LQR-type problems for fully-actuated left-invariant control systems on compact connected Lie groups. We obtain the corresponding algebraic/differential Riccati equation and in turn generalize some of the results in Berkane and Tayebi (2015) to a broader class of Lie groups. Closed-loop stability results are also derived.

The objective of drift counteraction optimal control is to maximize the time that a given system satisfies prescribed constraints. This paper describes novel open-loop and receding horizon/model pr...

In this paper, we develop two zonotope-based set-membership estimation algorithms for identification of time-varying parameters in linear static models, where both additive and multiplicative uncertainties are treated explicitly. The two recursive algorithms can be differentiated by their ways of processing the data and required computations. The first algorithm, which is referred to as Cone And Zonotope Intersection (CAZI), requires solving linear programming problems at each iteration. The second algorithm, referred to as the Polyhedron And Zonotope Intersection (PAZI), involves linear programming as well as an optimization subject to linear matrix inequalities (LMIs). Both algorithms are capable of providing tight overbounds of the feasible solution set (FSS) in an application to health monitoring of marine engines. Furthermore, PAZI algorithm applied to mini-batches of measurement data leads itself to further analysis of the relation between the estimation results at different iterations. In addition, an example of identifying time-varying parameters is also reported.

The objective of drift counteraction optimal control (DCOC) problem is to compute an optimal control law that maximizes the expected time of violating specified system constraints. In this paper, we reformulate the DCOC problem as a reinforcement learning (RL) one, removing the requirements of disturbance measurements and prior knowledge of the disturbance evolution. The optimal control policy for the DCOC is then trained with RL algorithms. As an example, we treat the problem of adaptive cruise control, where the objective is to maintain desired distance headway and time headway from the lead vehicle, while the acceleration and speed of the host vehicle are constrained based on safety, comfort, and fuel economy considerations. An informed approximate Q-learning algorithm is developed with efficient training, fast convergence, and good performance. The control performance is compared with a heuristic driver model in simulation and superior performance is demonstrated.

This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver’s state (i.e., distracted or attentive). Second, existing driving performance metrics, such as steering entropy, steering wheel reversal rate, and lane offset variance, are reviewed against the processed naturalistic driving data. Furthermore, a nonlinear autoregressive exogenous (NARX) driving model is developed to predict vehicle speed based on the range (distance headway), range rate, and speed history. For each driver, the NARX model is then trained on the attentive driving data. We show that the prediction error is correlated with driver distraction. Finally, two features, steering entropy and mean absolute speed prediction error from the NARX model are selected, and a support vector machine is trained to detect driving distraction. Prediction performances are reported.

This paper develops and compares two stochastic strategies that manage glider flight in a dynamic environment, to tackle an open problem concerning the necessity, applicability, complexity, and validity of using an environment model when making flight management decisions. Strategy performance is compared on two surveillance and pursuit tasks that require optimal control: the maximization of expected range while maintaining altitude within given limits, and the maximization of expected range while following a moving ground vehicle within a prescribed distance and maintaining altitude within given limits. Both tasks involve flight management decisions of glider airspeed and the time-to-spend climbing in a randomly encountered thermal. The first strategy is stochastic drift counteraction optimal control (SDCOC), a dynamic programming method that relies on a model. Here, SDCOC uses a computationally inexpensive yet accurate environment model that reflects transition probabilities between updrafts and downdrafts as well as thermal locations and strengths based on existing glider flight data. The second strategy is selective evolutionary generation, a model-free Markov chain Monte Carlo method that is not subject to the curse of dimensionality, efficiently searches for an optimal control in an online and tunable fashion, and easily adapts to a dynamic environment. However, this strategy requires a tolerance for learning and decision-space exploration. Both strategies perform satisfactorily, and showcase an environment/decision-space exploitation-exploration tradeoff.

There is a rapidly growing interest in the use of cloud computing for automotive vehicles to facilitate computation and data intensive tasks. Efficient utilization of on-demand cloud resources holds a significant potential to improve future vehicle safety, comfort, and fuel economy. In the meanwhile, issues like cyber security and resource allocation pose great challenges. In this paper, we treat the resource allocation problem for cloud-based automotive systems. Both private and public cloud paradigms are considered where a private cloud provides an internal, company-owned internet service dedicated to its own vehicles while a public cloud serves all subscribed vehicles. This paper establishes comprehensive models of cloud resource provisioning for both private and public cloud-based automotive systems. Complications such as stochastic communication delays and task deadlines are explicitly considered. In particular, a centralized resource provisioning model is developed for private cloud and chance constrained optimization is exploited to utilize the cloud resources for best Quality of Services. On the other hand, a decentralized auction-based model is developed for public cloud and reinforcement learning is employed to obtain an optimal bidding policy for a “selfish” agent. Numerical examples are presented to illustrate the effectiveness of the developed techniques.

Shape-based methods are becoming popular in low-thrust trajectory optimization due to their fast computation speeds. In existing shape-based methods constraints are treated at the acceleration level but not at the thrust level. These two constraint types are not equivalent since spacecraft mass decreases over time as fuel is expended. This paper develops a shape-based method based on a Fourier series approximation that is capable of representing trajectories defined in spherical coordinates and that enforces thrust constraints. An objective function can be incorporated to minimize overall mission cost, i.e., achieve minimum Δ V . A representative mission from Earth to Mars is studied. The proposed Fourier series technique is demonstrated capable of generating feasible and near-optimal trajectories. These attributes can facilitate future low-thrust mission designs where different trajectory alternatives must be rapidly constructed and evaluated.

The P-difference between two sets A and B is the set of all points, C , such that the sum of B to any of the points in C is contained in A . Such a set difference plays an important role in robust model predictive control and set-theoretic control. In this paper, we show that an inner approximation of the P-difference between two sets described by collections of polynomial inequalities can be computed using Sums of Squares Programming. The effectiveness of the procedure is shown with some computational examples.

Motivated by practical applications, the paper highlights MPC formulations suitable for counteracting system drift and the effects of large measured disturbances/set-point changes, and for overcoming limitations of underactuation. For drift counteraction, where the objective is to maximize the time or yield until the system trajectory exits a prescribed set, defined by system safety constraints, operating limits and/or efficiency requirements, MPC, based on mixed integer or conventional linear programming, can lead to effective solutions for higher order systems than possible with dynamic programming and value iterations based methods. Such solutions can have broad applicability including for fuel optimal Geostationary Orbit (GEO) station keeping, spacecraft Low Earth Orbit (LEO) maintenance, underactuated spacecraft attitude control, hybrid electric propulsion energy management, glider flight management, and the development of driving policies for adaptive cruise control and autonomous driving. The paper also highlights the capability of MPC to achieve non-smooth local stabilization of underactuated systems that can be exploited for attitude control of underactuated spacecraft with reaction wheel failures. Related results for systems that cannot be globally stabilized by continuous feedback are also mentioned. Finally, opportunities to handle large load and set-point changes with reference governors are discussed.

In this paper, we present a falsification scheme for nonlinear systems with input and/or state delays without relying on their explicit models. We formulate the falsification of predefined specifications as an optimal control problem, and exploit a functional gradient based method to solve it. Since the approach does not require an explicit model of the system, it is potentially applicable to industrial models of high complexity or directly to experimental hardware. We consider the falsification of a gasoline engine air-to-fuel ratio control system as a case study.

Under terminal constraints, the set of states such that a model-predictive control strategy is feasible, and thus stable, is equivalent to the viable-reachable set of the controlled system. We prove that viscosity and storage function approaches to viability theory are equivalent to each other. Based on these results we propose inner and outer bounds of the feasible set by storage functions, which for polynomial systems can be computed using sum-of-squares programmes. Numerical, discrete-time and continuous-time examples are given for lateral car dynamics and the forced Van der Pol oscillator , respectively.

This paper investigates a comfort-based route planner that considers both travel time and ride comfort. We first present a framework of simultaneous road profile estimation and anomaly detection with commonly available vehicle sensors. A jump-diffusion process-based state estimator is developed and used along with a multi-input observer for road profile estimation. The estimation framework is evaluated in an experimental test vehicle and promising performance is demonstrated. Second, three objective comfort metrics are developed based on factors such as travel time, road roughness, road anomaly, and intersection. A comfort-based route planning problem is then formulated with these metrics and an extended Dijkstra's algorithm is exploited to solve the problem. A cloud-based implementation of our comfort-based route planning approach is proposed to facilitate information access and fast computation. Finally, a real-world case study, comfort-based route planning from Ford Research and Innovation Center, Michigan to Ford Rouge Factory Tour, Michigan, is presented to illustrate the efficacy of the proposed route planning framework.

Motivated by the growing need to accommodate large transient thrust and electrical load requests in future more-electric aircraft, a coordinated control strategy for a gas turbine engine, generator...

The paper treats a class of optimal control problems for deterministic nonlinear discrete-time systems with the objective of maximizing the time or total yield until prescribed constraints are violated. Such problems are referred to as drift counteraction optimal control (DCOC) problems as the corresponding control policy may be viewed as optimally counteracting drift imposed by disturbances or system dynamics. We derive conditions for the existence of an optimal solution. The optimal control policy is characterized by the value function and a new algorithm based on proportional feedback is presented that obtains the value function faster than conventional dynamic programming algorithms. In addition, an approximate dynamic programming (ADP) approach using Gaussian process regression is formulated based on the new algorithm. Two numerical examples are reported, a time maximization problem for a van der Pol oscillator and a satellite life extension problem.

Jump–diffusion processes (JDPs) involve a combination of jumps (Poisson process) and diffusions (Wiener process). JDPs can be used to model large classes of disturbances in engineering applications, such as road disturbances to a car, wind disturbances to an airplane, and system parameter perturbations. This paper develops a road anomaly detector by exploiting an optimal state estimator for systems driven by JDP in combination with the multi-input observer. State estimation with the JDP-based estimator is shown to have better performance than a Kalman filter when jumps, such as potholes and bumps, are present. The road anomaly detector is implemented in an experimental test vehicle and its experimental validation results are reported.

This paper focuses on high-level control and decision-making for an autonomous car. We develop a hybrid probabilistic model that describes the motion of a car and its surrounding traffic on a two-lane highway/road, where the acceleration of the car and the lane changes serve as control variables. Using approximate dynamic programming (ADP) techniques and an enhanced version of the value iteration algorithm, a control policy is obtained that maximizes the expected time that the car maintains a prescribed minimum (safe) headway. Simulation results for different settings are provided to validate the approach.

This paper proposes an adaptive control allocation approach for over-actuated systems with actuator saturation. The methodology can tolerate actuator loss of effectiveness without utilizing the control input matrix estimation, eliminating the need for persistence of excitation. Closed loop reference model adaptive controller is used for identifying adaptive parameters, which provides improved performance without introducing undesired oscillations. The modular design of the proposed control allocation method improves the flexibility to develop the outer loop controller and the control allocation strategy separately. The ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method using simulation results.

We consider the problem of robust model predictive control for linear sampled– data dynamical systems subject to state and control constraints and additive and bounded disturbances. We propose a rigid tube model predictive control algorithm utilizing recent and topologically compatible notions for the sampled–data forward reach sets as well as robust positively invariant sets. The proposed method inherits almost all desirable features associated with rigid tube model predictive control of discrete-time systems, and, in addition, it ensures robust constraint satisfaction and safety in a continuous-time sense.

This paper considers the application of a reference governor scheme to a lighter-than-air wind-energy system subject to wind turbulence. The turbulence is treated as the output of the von Karman model, which can be represented by a linear filter with a normally distributed disturbance input. The conventional reference governor, which enforces constraints robustly, has to be heuristically tuned in order to be applied to stochastic systems. In this work, the reference governor is generalized to the stochastic setting in order to handle stochastic disturbances. A simulation is presented, which shows satisfactory constraint enforcement with properties that are superior to the heuristically-tuned reference governor results of the authors’ previous work.

A free-piston engine (FPE) requires stabilizing feedback control to manage piston motion and ensure safe operation. A high-fidelity model, which simulates the FPE behavior by combining dynamic, thermodynamic, and hydraulic submodels, is exploited as an engine surrogate for control development. A control-oriented model implicitly describes the FPE piston turnaround position using energy balance principles for an idealized Otto cycle. Local eigenvalues of the control-oriented model suggest that the FPE system is open-loop unstable. The control system relies on dynamic inversion and state feedback to stabilize the plant and track a target piston turnaround position. A robust reference governor is introduced to manage load changes, such that the piston position does not violate constraints, even in the presence of parameter uncertainty. In high-fidelity simulations, the reference governor constrains piston turnaround position to ±0.5 mm of the target during a load change.