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Even in multidisciplinary aircraft design, most aspects of nonlinear controllability are only considered in later design phases. Hence, control considerations are not used to inform the overall design of the airframe. This paper presents a control-aware controller-agnostic design optimization framework that integrates nonlinear controllability constraints, which are informed by the solutions to optimal control problems, into the multidisciplinary design process. To implement the optimization, procedures to evaluate the sensitivity of the controllability constraint to variations in the design variables are defined. A supersonic aircraft case study illustrates the methodology and the steps involved in the process. The proposed approach enables the integration of control-related constraints into gradient-based design optimization.

This paper proposes a method for enlarging the region of attraction of Linear Model Predictive Controllers (MPC) when tracking piecewise-constant references in the presence of pointwise-in-time constraints. It consists of an add-on unit, the Feasibility Governor (FG), that manipulates the reference command so as to ensure that the optimal control problem that underlies the MPC feedback law remains feasible. Offline polyhedral projection algorithms based on multi-objective linear programming are employed to compute the set of feasible states and reference commands. Online, the action of the FG is computed by solving a convex quadratic program. The closed-loop system is shown to satisfy constraints, be asymptotically stable, exhibit zero-offset tracking, and display finite-time convergence of the reference.

In this article, we investigate coordinated receding-horizon control of vehicle speed and transmission gearshift for energy-efficient operation of automated battery electric vehicles (BEVs). The introduction of multispeed transmissions in BEVs enables manipulation of electric motor operating point under given vehicle speed and acceleration command, thus creating the opportunity to further improve BEV energy efficiency. However, co-optimizing vehicle speed and transmission gearshift leads to a mixed-integer nonlinear programming (MINLP) problem, and it is well known that solving MINLP problems is computationally very challenging. To address this challenge, we propose a novel continuous relaxation technique that enables the computation of solutions to the speed and gearshift co-optimization problem using off-the-shelf nonlinear programming solvers. After analyzing theoretical properties of the proposed relaxation technique, we demonstrate its effectiveness through simulation-based case studies, where we show that co-optimizing vehicle speed and transmission gearshift can lead to considerably greater energy efficiency than optimizing them separately or sequentially and the proposed relaxation technique can reduce the computational cost of the co-optimization problem to a level that is comparable to the time budget available for onboard implementation.

In this article, we propose a multihorizon model predictive control (MH-MPC) approach with applications to integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs). The proposed MH-MPC leverages preview and optimization over a short receding and a long shrinking horizon, where the accuracy of preview, model, and integration can be different over different horizons. Compared with a conventional MPC-based approach with a short prediction horizon and terminal cost, the MH-MPC improves fuel consumption to a level comparable to dynamic programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm. In this paper the authors present an ultra-light heuristic method for generating optimal Eco-Driving traces for Connected Autonomous Vehicles (CAVs) which indirectly minimizes the 1-norm of acceleration. This novel method produces EE improvements in line with previously implemented meta-heuristic methods while executing in a fraction of the time.

In this letter, we introduce an extension of the Action Governor (AG), which is an add-on, supervisory scheme to a nominal control loop to enforce safety-related requirements. This extension enables AG design based on discrete-time piecewise affine (PWA) system models with additive set-bounded disturbance inputs and non-convex exclusion-zone avoidance constraints. We establish theoretical properties and computational approaches for this robust version of AG, and illustrate its application to an autonomous vehicle control problem.

This letter proposes a set-theoretic Failure Mode and Effect Management (FMEM) strategy that handles stuck/jammed actuators and enforces pointwise-in-time state and control constraints. The approach exploits nesting between constraint admissible and recoverable sets to ensure the existence of a recovery sequence. A reference governor is applied to track reference commands while imposing constraint satisfaction using the remaining working actuators. Numerical results of an aircraft longitudinal flight application are reported.

Among the multitude of modern control methods, model predictive control (MPC) is one of the most successful [1] – [4] . As noted in “Summary,” this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), and nonlinear control methods, such as feedback linearization and sliding mode control. Although MPC is computationally intensive, it is more broadly applicable than Hamilton–Jacobi–Bellman-based control and more suitable for feedback control than the minimum principle. In many cases, the constrained optimization problem for receding-horizon optimization is convex, which facilitates computational efficiency [5] .

The operation of the air-conditioning (A/C) system can significantly increase the energy consumption of passenger vehicles. In this article, aiming at reducing the vehicle-level energy consumption in hot weather, an optimization-based energy-efficient control strategy for the A/C system, which is referred to as the eco-cooling, is developed and experimentally validated. The proposed eco-cooling strategy leverages the A/C system efficiency sensitivity to the vehicle speed and the thermal storage of the passenger cabin to coordinate the A/C operation with vehicle speed profile by actively shifting the A/C thermal load toward the more efficient region at higher vehicle speeds. The proposed strategy exploits model predictive control and incorporates speed preview information while enforcing constraints. The effectiveness of the control strategy is first demonstrated on a high-fidelity simulation model and then implemented experimentally on a hybrid electric vehicle. Repeatable vehicle tests show that, over a real-world city driving cycle, an average energy saving of 5.7% can be achieved at the vehicle level using the proposed eco-cooling strategy compared with a baseline A/C control strategy that runs A/C with a constant setting. This energy-saving is achieved, while the proposed eco-cooling strategy delivers a similar amount of cooling energy to the cabin compared with that of the baseline strategy with a 2.7% difference on average.

Spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) play significant roles in many space missions, including on-orbit servicing and active debris removal. Precise modeling and prediction of spacecraft dynamics can be challenging due to the uncertainties and perturbation forces in the spacecraft operating environment and due to the multilayered structure of its nominal control system. Despite this complication, spacecraft maneuvers need to satisfy required constraints (thrust limits, line-of-sight cone constraints, relative velocity of approach constraints, etc.) to ensure safety and achieve ARPOD objectives. This paper considers an application of a learning-based reference governor (LRG) to spacecraft ARPOD operations to enforce constraints without relying on a dynamic model of the spacecraft during the mission. Similar to the conventional reference governor (RG), the LRG is an add-on supervisor to a closed-loop control system, serving as a prefilter on the command generated by the ARPOD planner. The LRG modifies, if it becomes necessary, the reference command to a constraint-admissible value to enforce specified constraints. The LRG is distinguished, however, by the ability to rely on learning instead of an explicit model of the system; and it guarantees constraints’ satisfaction during and after the learning. In this paper, the LRG is applied to the control of combined translational and rotational motions of a chaser spacecraft, and three case studies with different sets of safety constraints and thruster assumptions are used to demonstrate the benefits of the LRG in ARPOD missions.

One solution to the problem of distributing the control action among redundant actuators with uncertain dynamics is employing an adaptive control allocator. This paper proposes a sliding mode controller which exploits a time-varying sliding surface to complement adaptive control allocation in the presence of actuator saturation. The proposed approach does not require error augmentation for tracking desired references, which diminishes the computational burden. Aerodata Model in Research Environment, which is an over-actuated aircraft model, is adopted to demonstrate the efficacy of the proposed controller in simulation studies.

In this paper, we introduce a set-theoretic approach for mobile robot localization with infrastructure-based sensing. The proposed method computes sets that over-bound the robot body and orientation under an assumption of known noise bounds on the sensor and robot motion model. We establish theoretical properties and computational approaches for this set-theoretic localization approach and illustrate its application to an automated valet parking example in simulations and to omnidirectional robot localization problems in real-world experiments. We demonstrate that the set-theoretic localization method can perform robustly against uncertainty set initialization and sensor noises compared to the FastSLAM.

This article studies the problem of affine transformation-based guidance of a multiquadcopter system (MQS) in an obstacle-laden environment. Such MQSs can perform a variety of cooperative tasks including information collection, inspection mapping, disinfection, and fire-fighting. The MQS affine transformation is an approach to a decentralized leader–follower coordination guided by $n+1$ leaders, where leaders are located at vertices of an $n$ -D simplex, called leading simplex , at any time $t$ . The remaining agents are followers by acquiring the desired affine transformation via local communication. Followers are contained in a rigid-size ball at any time $t$ but they can be distributed either inside or outside the leading simplex. By eigendecomposition of the affine transformation coordination, safety in a large scale MQS coordination can be ensured by constraining eigenvalues of the affine transformation. Given the initial and final configurations of the MQS, A* search is applied to optimally plan safe coordination of a large-scale MQS, minimizing the travel distance between the the initial and final configuration. The article also proposes a proximity-based communication topology for followers to assign communication weights with their in-neighbors and acquire the desired coordination with minimal computation cost.

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.

In this article, we treat the problem of distributed state estimation for medium- to large-scale linear systems. We consider a system, which consists of several subsystems, and assume that there already exist linear local estimators for the individual subsystems. These existing local estimators could be designed using algorithms of different types. We propose a systematic approach to connect the local estimators through information flows, such that a distributed state estimation scheme is formed for the linear system. The convergence and boundedness of the estimation error for the developed distributed scheme are proven. The proposed method is applied to a full-car active suspension system. The entire system is decomposed into five subsystems interacting with each other. A distributed estimation architecture is established based on the proposed method. The effectiveness of the proposed approach for the full-car suspension system is illustrated using simulation results, which confirm good estimation performance.

This paper presents the results of a model predictive controller (MPC) development for diesel engine air-path regulation. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and control constraints. The MPC controller is designed and verified using a high-fidelity engine model in GT-Power. The controller exploits a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that transient engine thermal dynamics influence the airpath dynamics, specifically the intake manifold pressure response, however, MPC demonstrates robustness against inaccuracies in modeling these thermal dynamics. In particular, we show that MPC can be successfully implemented using a rate-based prediction model with two inputs (EGR and VGT positions) identified from data with steady-state wall temperature dynamics, however, closed-loop performance can be improved if a prediction model (i) is identified from data with transient thermal dynamics, and (ii) has the fuel injection rate as extra model input. Further, the MPC calibration process across the engine operating range to achieve improved performance is addressed. As the MPC calibration is shown to be sensitive to the operating conditions, a fast calibration process is proposed., Comment: 2022 American Control Conference (ACC), June 8-10, 2022, Atlanta, GA, USA

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.

Eco-Driving is a critical technology for improving automotive transportation efficiency. It is achieved by modifying the driving trajectory over a particular route to minimize required propulsion energy. Eco-Driving can be approached as an optimal control problem subject to driving constraints such as traffic lights and positions of other vehicles. In this paper we demonstrate the connection between Eco-Driving and best interpolation in the strip which is a problem in approximation theory and optimal control. By exploiting this connection, we are able to generate optimal Eco-Driving trajectories that can be driven with an autonomous system and evaluate them using conventional, hybrid electric, and fully electric vehicle models from FASTSim software. Our results quantify the energy efficiency improvements that can be achieved with the proposed approach.

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.