Abstract: A system and method for obtaining frequency-domain admittance/impedance using a few sets of time-series data. Utilizing an Eigensystem Realization Algorithm (ERA) or a dynamic mode decomposition (DMD), the input/output frequency-domain model reflecting voltage/current relationship is identified, and admittance/impedance identification is demonstrated for a grid-connected inverter-based resource (IBR) system. The proposed approach provides a powerful tool to replace the state-of-the-art harmonic injection methodology.

Abstract: A system includes a distributed saga log, a first service, a second service, a first saga execution coordinator (SEC) instance collocated to the first service, and a second SEC instance collocated to the second service. The first SEC instance is configured to manage a transaction for the first service, access the distributed saga log, perform a task identified in the distributed saga log, and document actions initiated by the first SEC instance in the distributed saga log. The distributed saga log is accessible to the first SEC instance and the second SEC instance. Additionally, the distributed saga log is configured to maintain saga processing information associated with the actions initiated by the first SEC instance and the actions initiated by the second SEC instance.

Abstract: Arrangements for dynamic system control evaluation and issue identification and mitigation are provided. In some examples, data may be received from a plurality of sources. The data may be received in batches at predetermined intervals or time periods, and/or as streaming data. In some examples, a first system control may be identified and a first system control value may be determined for the first system control. A plurality of threshold ranges associated with the first system control may be identified and the first system control value may be compared to the plurality of threshold ranges. Based on the comparing, the first system control value may be mapped to an objective score on a cyber health scale. The objective score may then be evaluated to determine whether an issue is occurring or is likely to occur. If so, one or more mitigation actions may be identified and implemented.

Abstract: An air handling unit (AHU) or rooftop unit (RTU) or other building device in a building includes one or more powered components and is used with a battery, and a predictive controller The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.

Abstract: A building automation system (BAS) includes building equipment located within a building and a BAS network configured to facilitate communications between the building equipment. The building equipment operate to affect a variable state or condition within the building. The BAS includes a BAS-BIM integrator configured to receive BAS points from the BAS network and to integrate the BAS points with a building information model (BIM). The BIM includes a plurality of BIM objects representing the building equipment. The BAS includes an integrated BAS-BIM viewer configured to use the BIM with the integrated BAS points to generate a user interface. The user interface includes a graphical representation of the BIM objects and the BAS points integrated therewith.

Abstract: A system and method for scheduling at least one event includes a component module arranged to receive component data regarding a plurality of possible operation decisions, a modelling module arranged to utilise the plurality of operation decisions to construct a Petri net model to simulate a real world system, a scheduling module arranged to utilise the Petri net and variable data regarding the plurality of operation decisions to determine a schedule of events that is feasible subject to a set of predetermined constraints, and an operation module arranged to provide the schedule of events to at least one external device.

Abstract: A measurement apparatus comprising at least one device interface adapted to connect an auxiliary measurement device and/or a device under test, DUT, to said measurement apparatus; a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence, AI, module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence, AI, module is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus.

Abstract: A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

Abstract: A model including a first co-simulation component and a second co-simulation component is analyzed. During execution of the model, the first co-simulation component outputs data to the second co-simulation component via a connection. The connection is declared as a continuous-time rate connection for input of the data into the second co-simulation component. Based on analyzing the model, the connection is identified as a discrete-continuous sample time connection based on data being communicated from the first co-simulation component to the second co-simulation component via the connection at a discrete-time rate when the model is executed in a co-simulation manner.

Abstract: A building management system includes a processing circuit configured to perform a system identification process to identify one or more parameters of a system model that predicts a behavior of a building system. The one or more parameters include one or more model parameters and one or more Kalman gain parameters. The system identification process includes identifying the one or more model parameters, generating an initial guess of the one or more Kalman gain parameters based on the training data and results of a simulation that uses the one or more model parameters, and identifying the one or more Kalman gain parameters by initializing a prediction error minimization problem with the initial guess. The building management system also includes a controller configured to control building equipment to affect the behavior of the building system based on predictions of the system model.

Abstract: A diagnostic device diagnoses a state of a test object. The diagnostic device includes a physical model base diagnosis unit, a mathematical model base diagnosis unit, a model update necessity determination unit, and an update notification unit. The physical model base diagnosis unit is configured to perform the diagnosis using a feature quantity. The mathematical model base diagnosis unit is configured to perform the diagnosis using a machine learning model. The model update necessity determination unit is configured such that if diagnostic results by the physical model base diagnosis unit and the mathematical model base diagnosis unit differ, the model update necessity determination unit determines that at least one of a physical model or a mathematical model is necessary to be updated. The update notification unit is configured such that if the model update necessity determination unit determines that the update is necessary, the update notification unit notifies it.

Abstract: Vibrational behavior of a wellbore operation can be characterized as a vibrational map relating a modeled or sensed vibration level of the operation's equipment to operational parameters of the wellbore operation. The operational parameters can be controllable by settings of the equipment. The map can include multiple contour lines, each representing a set of adjacent coordinates of operational parameters that have the same vibrational level. Tracing a contour line can include determining vibration at a pre-tracing coordinate, adjusting the operational parameters until the vibration level to be mapped is reached at a tracing origin coordinate, then varying the operational parameters while keeping the vibration level constant until the tracing origin or a bound of the operational parameters is reached. Vibration levels having multiple, non-intersecting contour lines can be found by repeating tracing from a different pre-tracing coordinate.

Abstract: System, methods, and other embodiments described herein relate to simulating sensor data for a scene. In one embodiment, a method includes, in response to receiving a request to generate simulated sensor data for the scene, acquiring simulation data about the scene. The simulation data includes at least simulated information about the scene that is computer-generated. The method includes computing the simulated sensor data using a generative neural network that accepts the simulation data as an input and produces the simulated sensor data as an output. The simulated sensor data is a simulated perception of the scene by a sensor. The method includes providing the simulated sensor data as part of the scene.

Abstract: Provided is an apparatus including a plurality of agents that each set some devices among a plurality of devices provided in a facility to be target devices, wherein each of the plurality of agents includes a state acquiring section that acquires state data indicating a state of the facility; a control condition acquiring section that acquires control condition data indicating a control condition of each target device; and a learning processing section that uses learning data including the state data and the control condition data to perform learning processing of a model that outputs recommended control condition data indicating a control condition recommended for each target device in response to input of the state data.

Abstract: A control device (70) of a plant which controls operation amounts of a plurality of types of equipment constituting the plant, the control device includes a prediction model deriving unit (71) that derives a prediction model from which a monitoring target value is output, an adjustment amount calculating unit (72) that calculates adjustment amounts of operation amounts in the case where the monitoring target value becomes a desired value and calculates differences between the operation amounts of existing control and the calculated operation amounts as first adjustment amounts, a constraint condition setting unit (73) that sets constraint conditions based on a measurement value and operation conditions of the plant 1, and an operation amount calculating unit (74) that calculates second adjustment amounts to which the constraint conditions are applied and calculates a plurality of the adjusted operation amounts based on each of the calculated second adjustment amounts.

Abstract: Every time the haulage vehicle position data and the road surface condition data are received from a haulage vehicle 120, the segment including a position specified by the haulage vehicle position data, a reception time, and the road surface condition data are associated with one another for accumulation. Every time the repair vehicle position data and the work status data indicating a working state are received from a repair vehicle 130, the road surface condition data associated with the segment including a position specified by the accumulated repair vehicle position data is reset. Every time the road surface condition data is newly accumulated, a prediction function is determined to predict a chronological change in road surface conditions of the segment based on the road surface condition data accumulated on a segment-by-segment basis and a repair scheduled time-of-day is calculated according to the prediction function to output to an output apparatus.

Abstract: A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

Abstract: A radio frequency (RF) communication system may include of mobile vehicles, with each mobile vehicle including RF equipment and a controller. The controller may be configured to operate the RF equipment, determine a reward matrix based upon possible positional adjustments of the mobile vehicle and associated operational parameters of the mobile vehicle, and implement a positional adjustment of the mobile vehicle based upon the reward matrix. The system may also include an oversight controller configured to update respective reward matrices of the mobile vehicles.

Abstract: The present disclosure provides a plant abnormality detection system and method, which can learn the plant data collected in real time through a plurality of prediction models having different characteristics to generate a prediction value having the highest accuracy to diagnose the abnormality thereof, thus detecting accurately the abnormality of the plant to early provide alarm.

Abstract: A controller emulator, coupled to an interface that exposes the controller emulator to inputs from external sources, provides one or more control signals to a process simulator and a deep learning process. In response, the process simulator simulates response data that is provided to the deep learning processor. The deep learning processor generates expected response data and expected behavioral pattern data for the one or more control signals, as well as actual behavioral pattern data for the simulated response data. A comparison of at least one of the simulated response data to the expected response data and the actual behavioral pattern data to the expected behavioral pattern data is performed to determine whether anomalous activity is detected. As a result of detecting anomalous activity, one or more operations are performed to address the anomalous activity.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for information display.

Abstract: The present disclosure describes an actuator system comprising an actuator unit that is configured to be positioned next to a structure; and an adaptive controller unit that is configured to receive a command input for the actuator unit and output an actuator command based on a reference model of a physical system that includes the actuator unit and the structure, wherein the actuator command does not alter trajectories of the reference model. In various embodiments, the uncertain dynamical system of the physical system is augmented with the actuator dynamics to provide improved stability.

Abstract: Optimizing a process includes identifying a plurality of optimizable variables, determining first and second set points for each optimizable variable, running first trials using the first or second set point to obtain an output having at least one measurable parameter, calculating a delta between a required value and the measured value of each parameter of each of the outputs, calculating a first true residual for the first set point and a second true residual for the second set point for each of the optimizable variables, calculating an interpolant between the first and second true residuals and an extrapolant outside the first and second true residuals for each of the optimizable variables, and applying a set of identification rules to select an optimized set point for each of the optimizable variables selected from either the interpolant or the extrapolant for each of the optimizable variables.

Abstract: In the formation of a solid object by progressively adding extruded materials to a workpiece, a mechanism is disclosed for transposing the paths by which two or more extrusion nozzles travel to deposit materials. Paralleling a surface contour of the object being formed, the paths for two or more substantially continuous extruded traces are directed in complementary fashion to cause a first extrusion trace to form the object surface while a second extrusion trace is deposited behind the first trace. At another position along the surface contour, the paths of the extruded traces may cross over one another to allow the second extruded trace to define the object surface. Where the first and second traces have a different color or visual appearance, the disclosed mechanism enables fine graphic features to efficiently be integrated into the surface of the object being formed.

Abstract: The present disclosure relate to methods and apparatus for temperature sensing and control during substrate processing. Substrate temperatures during processing, which are difficult to measure directly, may be determined by examination of deposited film properties or by measuring changes in power output over time of the substrate heating apparatus. Temperatures are determined for many substrates during processing, showing how substrate temperatures change over time, and the temperature changes are then used to build models via machine learning techniques. The models are used to adjust heating apparatus setpoints for future processing operations.

Abstract: The present disclosure relates to a method for the application-specific setting of a field device, which is installed in a defined application in a process in automation engineering and determines or monitors at least one process variable of a medium, comprising: recording a diagnostic message which occurs or has occurred on the field device during a defined interval, analyzing which of the diagnostic messages are tolerable and/or plausible in the defined application, assigning an application-specific notice with regard to tolerability and/or plausibility to each diagnostic message that has arisen in the defined application, clearing the diagnostic message if this is classified as tolerable and/or plausible in the defined application, and using the application-specific setting of the field device, which clears the diagnostic message classified as tolerable and/or plausible.

Abstract: The disclosure concerns a method for model-based optimization, especially calibration, of a technical device, especially an internal combustion engine.

Abstract: A parallel control method for an intelligent workshop is provided, comprising the following steps: step A: constructing a parallel control simulation platform; step B: establishing a parallel execution mechanism; and, step C: correcting and optimizing a parallel control system. A parallel control system for an intelligent workshop is provided, comprising: an MES module configured to issue production instructions to unit management modules; the unit management modules configured to convert the received production instructions into machine instructions and synchronously issue the machine instructions to underlying PLCs by a bus control network module, and drive the parallel control simulation platform and a field device to move by a soft PLC and a hard PLC; the bus control network module configured to establish a communication network among the MES module, an SCADA module, an industrial personal computer, physical devices and a whole-line simulation model; and, the SCADA module.

Abstract: Properties of a physical system are measured and used to update estimated states of the system in an iterative manner. At each iteration, a state update weight is assigned for each state and the states are predicted from previous estimated states. System states are predicted from prior estimates and then updated dependent upon the measurements and the state update weights to provide updated estimated states. In addition, a prior covariance matrix of state errors is updated dependent upon the state update weights to provide an estimation error covariance matrix that is consistent with the updated estimated states. The updated state may be equivalent to a weighted sum of a prior estimated state and an initial updated estimated state. The approach provides improvements to a variety of estimators, including least squares estimators and estimators such as the Extended, Schmidt and Unscented Kalman Filters and the Rao-Blackwellized Particle Filter.

Abstract: Described herein are frameworks, devices and methods configured for enabling display for facility information and content, in some cases via touch/gesture controlled interfaces. Embodiments of the invention have been particularly developed for allowing an operator to conveniently access a wide range of information relating to a facility via, for example, one or more wall mounted displays. While some embodiments will be described herein with particular reference to that application, it will be appreciated that the invention is not limited to such a field of use, and is applicable in broader contexts.

Abstract: A method to identify physical parameters of a mechanical load includes the following steps: applying a first control signal to a mechanical device in a control circuit; measuring a first return signal; and using a power density spectrum of the first return signal to stipulate an excitation signal for the mechanical device.

Abstract: Disclosed are an autonomous driving method of implementing adaptive autonomous driving for a user based on device information that is obtained from a device of the user, and an autonomous vehicle. In particular, disclosed are an autonomous driving method of implementing autonomous driving appropriate for a situation of the user by controlling a function of an autonomous vehicle that corresponds to device information obtained from a wearable device, and the autonomous vehicle.

Abstract: When a required pressure ratio RP* is less than or equal to a calculation switching pressure ratio RPwot, an engine control unit calculates, as a target opening degree TA*, a throttle opening degree TA at which a throttle upstream-downstream pressure ratio RP is the required pressure ratio RP*. When the required pressure ratio RP* exceeds the calculation switching pressure ratio RPwot, the engine control unit obtains a switching point opening degree TAwot, a switching point load factor KLwot and a maximum load factor KLmax, and calculates the target opening degree TA* as a value that has a linear relationship with the required load factor KL* between the switching point opening degree TAwot and the maximum opening degree TAmax.

Abstract: A method for processing operating data (e.g., position, setpoint, and pressure) for a valve assembly. The method is configured to associate characteristics of operation for the valve assembly with a root cause and/or a contributing factor. In one embodiment, the method can assign a first amplitude with a value that quantifies movement or “jump” of the valve stem that results from stick-slip on the valve assembly. The method can also assign a second amplitude with a value that quantifies a change in the data for the setpoint. The method can further ascertain the relationship or “position” of the first amplitude relative to the second amplitude, or vice versa. The method can use the relationship between the first amplitude and the second amplitude to indicate the root cause of the operation of the valve assembly.

Abstract: A system for determining fire danger within a building composed of a plurality of building zones comprises: a human presence detector configured to detect a human presence measurement; a fire detector configured to detect a fire measurement; a gas detector configured to detect gas measurement; a storage device to store at least one of an evacuations plan, flammable material index, and human movement prediction model; a fire danger management system coupled to storage device, the fire danger management system including: a fire danger rating generation module to determine a fire danger rating for each building zone in response to at least one of the evacuations plan, flammable material index, and human movement prediction model; and a fire danger index generation module to determine a fire danger index for each building zone in response to at least one of the fire danger rating, human presence measurement, fire measurement, and gas measurement.

Abstract: A computer system is configured to process alarm activations received from technical systems, where an alarm activation represents a deviation of the technical status of a technical system from normal. The system includes: a data storage interface for receiving alarm activations in data storage, where the recorded alarm activations correspond to alarms; a data processor for: determining, from the recorded alarm activations, time intervals for alarm analysis; and computing similarity measures between the time intervals that depend on the occurrence of the recorded alarm activations in the time intervals, and where the contribution of an alarm activation to the similarity of two time intervals is reduced with an increasing occurrence of the alarm in the time intervals; and a user interface configured to provide pairs of time intervals to an operator of the one or more technical systems that include time intervals with similarity measures indicating similar alarm.

Abstract: A plant control system includes a plant system and a control system controlling the plant system. Runtime conditions of an operating point of the plant control system are received. The runtime conditions include a runtime state of the plant system, a runtime output of the plant system, and a runtime control action applied to the plant system. Reference conditions of a reference point corresponding to the operating point are determined. Stability radius measures of a state difference, an output difference, and a control action difference are computed.

Abstract: A method and controller for creating a digital twin of a pump. The method includes receiving, by a controller, a specification curve corresponding to a centrifugal pump. The method includes building and executing, by the controller, a first model of the centrifugal pump, based on the specification curve. The method includes receiving sensor data corresponding to and during the operation of the pump. The method includes updating the first model according to the sensor data to produce an updated model, and storing the updated model as a digital twin of the centrifugal pump.

Abstract: A factory management and monitoring system includes a processing unit structured to receive data from a plurality of sensors structured to monitor one or more factories or machines included in the factories, to analyze the received data, and to generate a user interface including the received data or information resulting from analysis of the received data, and a display structured to display the user interface.

Abstract: Embodiments are directed to computer methods and systems that build and deploy a pattern model to detect an operating event in an online plant process. To build the pattern model, the methods and systems define a signature of the operating event, such that the defined signature contains a time series pattern for a KPI associated with the operating event. The methods and systems deploy the pattern model to automatically monitor, during online execution of the plant process, trends in movement of the KPI as a time series. The methods and systems determine, in real-time, a distance score between a range of the monitored time series and the time series pattern contained in the defined signature. The methods and systems automatically detect the operating event in the online industrial process based on the determined distance score, and alter parameters of the process (e.g., valves, actuators, etc.) to prevent the operating event.

Abstract: A method of inverse reinforcement learning for estimating cost and value functions of behaviors of a subject includes acquiring data representing changes in state variables that define the behaviors of the subject; applying a modified Bellman equation given by Eq. (1) to the acquired data: q(x)+gV(y)?V(x)=?ln{pi(y|x))/(p(y|x)} (1) where q(x) and V(x) denote a cost function and a value function, respectively, at state x, g represents a discount factor, and p(y|x) and pi(y|x) denote state transition probabilities before and after learning, respectively; estimating a density ratio pi(y|x)/p(y|x) in Eq. (1); estimating q(x) and V(x) in Eq. (1) using the least square method in accordance with the estimated density ratio pi(y|x)/p(y|x), and outputting the estimated q(x) and V(x).

Abstract: A machine learning includes a state observation unit that observes, as state variables representing a current state of an environment, PID control parameter data indicating the a parameter of the PID control during machining, machining condition data indicating a machining condition of the machining, and machining environment data relating to a machining environment of the machining, a determination data acquisition unit that acquires, as determination data, tool life determination data indicating an appropriateness determination result relating to depletion of the life of a tool during the machining, and cycle time determination data indicating an appropriateness determination result relating to the cycle time of the machining, and a learning unit that learns the machining condition and the machining environment of the machining, and the parameter of the PID control in association with each other.

Abstract: A method includes obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays and performing spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding a worst-case cutoff frequency over all output channels for each process input, designing the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.

Abstract: A monitoring and maintenance system that utilizes imperial and theoretical data to compare parts, vehicles, users, regions, wear intensity indexes over time and tracking information to provide a sophisticated data collection system for heavy-duty equipment or rental equipment. This tracking is designed to better the specifications, designs, training, preventative maintenance, and replacement wear understanding of fleet management.

Abstract: There is provided a technique for detecting a fault of a device from an error in device data. According to the technique described herein, there is provided a substrate processing apparatus including: a pipe heater configured to heat a gas pipe; a temperature detecting unit provided at the pipe heater and configured to detect a temperature of the gas pipe; a control unit configured to control the pipe heater based on device data representing the temperature of the gas pipe measured by the temperature detecting unit by executing a process control program to adjust an electrical power applied to the pipe heater; a memory unit configured to store a monitored item table; and a device status monitoring unit configured to execute a device status monitoring program.

Abstract: A system for control of the air path of an internal combustion engine including a feed-forward controller and a feed-back controller. The feed-forward controller configured to in a sampling period, obtain model parameter values, incorporate the modeled parameter values and reference values into an optimization for a nonlinear model predictive control, perform a Newton method iteration of the optimization in order to determine a solution, and issue commands that control inputs for engine operation based on the solution. The feed-back controller configured to obtain modeled parameter values, obtain measured parameter values based on the operating condition of the engine, incorporate the modeled parameter values, measured parameter values, and reference values into an optimization for a nonlinear model predictive control, perform a Newton method iteration of the optimization in order to determine a solution, and issue commands that control inputs for engine operation based on the solution.

Abstract: A control system and a control method are provided. A control device in the control system includes a computation processing unit related to control of a control target, a collection unit that executes a process of collecting data associated with the control target, and a monitoring processing unit that executes a process of monitoring a state of the control target and includes a feature quantity generation unit that executes a process for generating a feature quantity from the collected data, and a detection unit that executes a for detecting an abnormality occurring in the control target using the generated feature quantity and an abnormality detection parameter suitable for detection of an abnormality occurring in the control target that is set based on a result of machine learning. An information processing device executes emulation of the monitoring process using the data associated with the control target from the control device.

Abstract: A method includes repeatedly identifying one or more values for one or more model parameters of at least one model associated with a process. The one or more values for the one or more model parameters are identified using data associated with the process. The method also includes clustering the values of the one or more model parameters into one or more clusters. The method further includes identifying one or more additional values for the one or more model parameters using additional data associated with the process. In addition, the method includes detecting a mismatch between the at least one model and the process in response to determining that at least some of the one or more additional values fall outside of the one or more clusters. The values could be clustered using a support vector machine.

Abstract: A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level controller, and a plurality of low-level airside controllers. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces. The high-level controller is configured to generate a plurality of airside subsystem energy targets, each airside subsystem energy target corresponding to one of the plurality of airside subsystems and generated based on a thermal capacitance of the one or more building spaces to which heating or cooling is provided by the corresponding airside subsystem. Each low-level airside controller corresponds to one of the airside subsystems and is configured to control the airside HVAC equipment of the corresponding airside subsystem in accordance with the airside subsystem energy target for the corresponding airside subsystem.

Abstract: A method for controlling an operation of a machine exhibiting symmetries in dynamics of the machine, a cost of the operation of the machine, and constraints on the operation of the machine performs iteratively a control optimization until a termination condition is met to produce an optimal control input and controls the machine according to the optimal control input.