Abstract: A system for controlling a plant process wherein the plant is over-actuated with N+1 actuators and provides N performance variables is disclosed. The system includes an outer-loop controller operable to compare N performance variable values from the plant with a reference set point and provide a desired virtual control input to an inner-loop controller. The inner-loop controller receives the desired virtual control input from the outer-loop controller and provides real control inputs to the at least N+1 actuators. The N+1 actuators receive the real control inputs and subsequently provide an actual virtual control input to the plant. Upon receiving the actual virtual control input from the N+1 actuators the plant is operable to produce N updated performance variable values in an optimized manner.

Abstract: An embodiment is a method and apparatus to process an input signal. An analog automatic gain control (AGC) processor controls an analog adjustable gain of the input signal using a feedback mechanism. The analog AGC processor generates a first signal. A processing circuit transforms the first signal into a second signal. A digital AGC processor controls a digital adjustable gain of the second signal using a feed-forward mechanism.

Abstract: An adaptive control system is described. The system includes a control having a plurality of control parameters, the control parameters providing for control of an associated plant. The control parameters are tuned using a prediction error filter, the prediction error filter selecting values of the control parameters that minimize the values of a prediction error between actual and predicted values of an autoregressive process.

Abstract: A method for building robust model predictive controller universally applicable is presented based on the innate process characteristics independent of the method of control actuation. The method of universal MPC design permits proper configuration of requisite regulatory control loops for measured and unmeasured disturbance rejections consistent with the underlying innate process characteristics and their embedding within the overall process unit model predictive controller. The method of universal MPC design requires that manipulated variables process value based model (PV-based models) be used in control and optimization in place of the customary set point based models (SP-based models) or control output based models (OP-based models). The PV-based models are devoid of the manipulated variables regulatory controllers response and tuning. Based on the PV-based models, an alternate method of MPC called PV-based MPC is presented that is most robust and adaptable of possible three types of MPC.

Abstract: A robust digital controller is equipped with a high degree of approximation and is able to incorporate a novel two-degree-of-freedom robust digital control system without substantially considering the magnitude of the control inputs and there is provided its designing device. A control compensating means is configured as an integral type control system in which a discrete transfer function Wry (z) between a target value r and a controlled variable y is approximated to a higher-approximate quadratic approximate model transfer function Wm (z) and an arithmetic processing can be performed within the digital controller based on the model transfer function Wm (z). Further, the designing device automatically calculates parameters constituting the control system. Consequently, a robust digital controller can be easily realized that is equipped with a high degree of approximation as compared with a conventional approximate digital control system for realizing a first-order model and is robust against output noises.

Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.

Abstract: A method of controlling a pressure swing adsorption unit in which a manipulated variable such as a capacity factor used by such unit in setting the bed cycle time is updated with a manipulated variable computed within a controller. The controller has a feed forward level of control in which the updated manipulated variable is calculated from a probability function when the product is likely to go off spec as determined by the probability function. In such manner, an optimal manipulated variable can be calculated that will maximize recovery in a manner that the probability of the product going off spec will be acceptable. If a specific impurity within the product is above a targeted range, a change to the optimal manipulated variable is computed with the use of a feedback level of control utilizing fuzzy logic.

Abstract: An adaptive control device includes an identifier and a feedforward controller. The identifier estimates an unknown coefficient in a discrete-time transfer function model of a controlled object to identify the discrete-time transfer function model. Estimation is performed based on a manipulated variable supplied to the controlled object and a controlled variable of the controlled object to the given manipulated variable. When estimating each coefficient of the discrete-time transfer function model, the identifier estimates a single unknown coefficient in numerator in non-expanded form of the numerator.

Abstract: The present invention relates to a general-purpose analogical reasoning system. More specifically, the present invention relates to a high-performance, semantic-based hybrid architecture for analogical reasoning, capable of finding correspondences between a novel situation and a known situation using relational symmetries, object similarities, or a combination of the two. The system is a high-performance symbolic connectionist model which multiplexes activation across a non-temporal dimension and uses controlled activation flow based on an analogical network structure. The system uses incremental inference to stop inference early for object correspondence, uses initial mappings to constrain future mappings, uses inferred mappings to synchronize activation, and independent mapping based on roles, superficial similarity, or composites.

Abstract: A method is provided wherein a lithographic projection apparatus is used to print a series of test patterns on a test substrate to measure printed critical dimension as function of exposure dose setting and focus setting. A full-substrate analysis of measured critical dimension data is modeled by a response model of critical dimension. The response model includes an additive term which expresses a spatial variability of the response with respect to the surface of the test substrate. The method further includes fitting the model by fitting model parameters using measured critical dimension data, and controlling critical dimension using the fitted model.

Abstract: Methods and systems for controlling a cement finishing mill, and operating the mill at an optimal point, are disclosed. To determine an optimal point of operation, values of mill power and sound are collected and compared to values predicted by a model to determine if the mill is choking. This choking determination is used in one of two processes to determine an optimal point of operation for the mill. In the first process, as long as the mill is not choking, the amount of fresh feed input to the mill is incrementally increased; when choking occurs, the amount of fresh feed is decreased until choking ceases. In the second process, the amount of fresh feed input to the mill is increased and decreased in an oscillating manner over time, to find the amount of feed that results in the mill approaching, but never reaching, a choking state.

Abstract: A partial enumeration model predictive controller and method of predictive control for a multiple input, multiple output (MIMO) system, including providing a solution table with problem solutions to a model predictive control problem for the MIMO system over a partial parameter region; scanning the solution table for an optimal solution for current parameters; using the optimal solution to control the MIMO system when the optimal solution is in the solution table; and using an alternative solution to control the MIMO system when the optimal solution is not in the solution table.

Abstract: A position control apparatus includes a detecting unit configured to detect a position of a control target, a subtracting unit configured to subtract an output of the detecting unit from a target value, an iterative learning control circuit including a filter into which a deviation between the output of the detecting unit and the target value is input, where the iterative learning control circuit feeds forward a control input to the control target, and a parameter computing unit configured to compute a variation in a parameter of the control target. A characteristic of the filter is computed in accordance with the variation in the parameter of the control target.

Abstract: A vehicle stability control system includes a basic request driving force computing unit, a front and rear wheel load computing unit, a virtual turning radius estimating unit, a target value computing unit, and a vibration damping correction controlling unit. The basic request driving force computing unit computes a physical quantity so as to generate the basic request driving force requested by a driver. The front and rear wheel load computing unit detect a load applied to the wheels. The virtual turning radius estimating unit estimates a virtual turning radius. The target value computing unit computes a target value of a stability factor. The vibration damping correction controlling unit corrects the physical quantity so as to follow the target value. The system generates the driving force for the driving wheel.

Abstract: In a control loop for regulating a combustion process in a plant having a controlled system for converting material by way of the combustion, with at least one flame body being formed, the control loop having at least one observation device for imaging the flame body and further sensors to determine the state variables describing the state of the system in the plant, at least one regulator and/or a computer to evaluate the state variables and select suitable actions based on a process model, and adjustment devices for at least the supply of material and/or air that can be controlled by the actions, the process model provides specialized function approximators for various process dynamics, one of which function approximators is selected by a selector, and a regulator assigned to the selected function approximator is used to regulate the control loop.

Abstract: Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed.

Abstract: In one embodiment, a method for providing predictions pertaining to a future state of a manufacturing facility includes collecting data from various source systems in a manufacturing facility, and generating predictions pertaining to a future state of the manufacturing facility based on the collected data. The method further includes providing the predictions to recipient systems in the manufacturing facility.

Abstract: A temperature control scheme for a fuel cell stack thermal sub-system in a fuel cell system that uses a non-linear thermal model and disturbance rejection to provide an optimum stack temperature. The thermal sub-system includes a coolant loop directing a cooling fluid through the stack, a pump for pumping the cooling fluid through the coolant loop, and a radiator for cooling the cooling fluid outside of the fuel cell stack. The system includes a controller for controlling the speed of the pump so as to maintain the temperature of the stack at a desired temperature. The controller uses the thermal model to anticipate a temperature of the cooling fluid out of the fuel cell stack to control the speed of the pump.

Abstract: In a controlling device that uses a model for simulating characteristics of a control target to learn a method of generating a model input that satisfies a target model output value and generates an operation signal according to a learning result, a target model output value for satisfying an operation target value is determined from measured signals from the control target.

Abstract: One embodiment of the present invention provides a system that optimizes subset selection to facilitate parallel training of a support vector machine (SVM). During operation, the system receives a dataset comprised of data points. Next, the system evaluates the data points to produce a class separability measure, and uses the class separability measure to partition the data points in the dataset into N batches. The system then performs SVM training computations on the N batches in parallel to produce support vectors for each of the N batches. Finally, the system performs a final SVM training computation using an agglomeration of support vectors computed for each of the N batches to obtain a substantially optimal solution to the SVM training problem for the entire dataset.

Abstract: In one embodiment, at least one parameter set for at least one harmonic of a continuous wave (CW) signal is digitally generated in response to a parameter set for the CW signal. In response to the parameter set for the CW signal, the CW signal is synthesized; and in response to the at least one parameter set for the at least one harmonic of the CW signal, at least one nulling tone is synthesized. The CW signal and the at least one nulling tone are amplified; and the amplified CW signal and the at least one amplified nulling tone are summed to produce a linearized amplified CW signal. Other embodiments are also described.

Abstract: Activities within various environments (e.g., industrial control environment) can be automated through a number of interchangeable modules configured to perform an action or series of actions. Each interchangeable module can be dedicated to a specific function or task (e.g., quality control, performance) and can take into consideration various business aspects. A sequence in which the actions should be performed can be assigned and the appropriate module automatically initiated based on the sequence. A user can manually select a module in order to have a particular function automatically implemented, such as inserting a quality control module into a rack so that functions relating to quality control are automatically implemented. When a different task is desired, the quality control module can be removed and replaced with a different module.

Abstract: Systems and methods that provide for adaptive processes in an industrial setting. Historian data, in conjunction with current collected data, can be converted into decision making information that is subsequently employed for modifying a process in real time. A process trend component, which is associated with a controller, can access historian data (e.g., trends collected via historians) to determine/predict an outcome of a current industrial process. Such enables a tight control and short reaction time to correcting process parameters.

Abstract: Systems and methods that correlate among disparate pieces of synchronized data, collected from an “internal” data stream (e.g., history data collected from an industrial unit) and an “external” data stream (e.g., traffic data on network services). A process trend component that determines/predicts an outcome of an industrial process and facilitates diagnostics/prognostics of an industrial system. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process. Such enables a tight control and short reaction time to process parameters, and for a modification thereof.

Abstract: A method of generating a decision tree for a seismic to simulation workflow, including: identifying a plurality of elements of the seismic to simulation workflow; receiving a plurality of modeling scenarios for each of the plurality of elements, where each of the plurality of modeling scenarios is associated with a realization of the seismic to simulation workflow; receiving a plurality of probabilities for the plurality of modeling scenarios; and generating a decision tree comprising a plurality of nodes and a plurality of branches in response to said plurality of modeling scenarios, where each level of the decision tree is associated with one of the plurality of elements, where the plurality of nodes are associated with the plurality of modeling scenarios, and where the plurality of branches are based on the plurality of probabilities.

Abstract: A system and method for predicting operation of a plant or process receive an input value from the plant or process. An integrity of a non-linear model corresponding to a local input space of the input value may be determined. The non-linear model may include an empirical representation of the plant or process. If the integrity is above a first threshold, non-linear model may be used to provide a first output value. However, if the integrity is below the first threshold, a linearized first principles model may be used to provide a second output value. The linearized first principles model may include an analytic representation of the plant or process. Additionally, the analytic representation of the plant or process may be independent of the empirical representation of the plant or process. The first output value and/or the second output value may be usable to manage the plant or process.

Abstract: An appliance control system is described for controlling a plurality of appliances. The system includes a control station located remotely from the appliances and having a control unit for controlling the plurality of appliances, at least one client station being associated with at least one of the plurality of appliances and being adapted for allowing a user to make requests to the control station for using the at least one of the plurality of appliances, actuators connected to the control station and the at least one of the plurality of appliances for receiving control signals from the control station and controlling the at least one of the plurality of appliances, and sensors connected to the water appliance and the control station for recording information about the at least one of the plurality of appliances and providing the information to the control station.

Abstract: Systems and methods associated with configurable advanced process control (APC) application development are described. One embodiment includes a computing system that includes configuration files corresponding to APC clients. The computing system also includes computational models corresponding to the configuration files. The computing system also includes a Rapid Advanced Control Enabler (RACE). A RACE may select a configuration file to process based, at least in part, on a request received from an APC client. A RACE may also dynamically load a computational model that corresponds to the selected configuration file. A RACE may also execute the dynamically loaded computational model and provide a response to the APC client from which the request was received.

Abstract: A technique is disclosed for reducing an error in a controlled variable via model predictive control. A predicted error in the controlled variable is determined for a forward-looking control horizon based upon measured or computed variables. The integral of the predicted error is computed. If the error or the integral exceed a tolerance for a determined time period, the model predictive control algorithm is modified to drive the error or the integral to within a tolerance. The modifications to the control algorithm may include changes to coefficients for terms based upon the error and/or the integral of the error.

Abstract: An observation system configured to observe at least one known state variable of an observed system includes a plurality of filters that are configured to receive a system input, and generate the at least one unknown state variable. Generating the unknown state variable includes processing the system input, a plurality of known state variables, and a time varying mode vector. An inverse system configured to observe at least one inverse state variable of an original system includes a plurality of filters that are configured to receive a system input, and generate the at least one inverse state variable. Generating the inverse state variable includes processing the system input, a plurality of known state variables associated with the original system, and a time varying mode vector associated with the original system.

Abstract: In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.

Abstract: A method for controlling a process variable as it approaches a predetermined value (setpoint) so that the setpoint is not exceeded. The method employs a time domain polynomial equation in a feedback configuration and utilizes a controller that acts as an On/Off controller until the process variable approaches setpoint. As the process variable approaches setpoint, the controller acts as a fast responding analog controller thereby “tailoring” a control variable to precisely bring the process variable to the setpoint without exceeding or overshooting the setpoint.

Abstract: A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: A method for feeding sheets includes feeding a first sheet to a location for flipping sheets with respect to a target position before the first sheet is fed passed the target position. A second face of the first sheet is fed passed the target position. The first sheet is fed to a second location for flipping sheets with respect to the target position. The second location is different than the first location. A first face of the first sheet is fed passed the target position.

Abstract: Systems, methodologies, media, and other embodiments associated with simulating a processor performance state by controlling a thermal management signal are described. One exemplary system embodiment includes a data structure for storing bit patterns that facilitate controlling a GPIO (General Purpose Input Output) block and addresses of locations to which the bit patterns can be written. The example system may also include a logic configured to receive a request to produce a performance state in a processor and to cause a frequency and voltage to be established in the processor in response to a thermal management signal being generated in response to writing the bit pattern(s) to the address(es).

Abstract: A method for pre-distorting an input for a device is provided. A partial set of known data pairs is acquired during a closed-loop device calibration period. The partial set of known data pairs is searched for at least one missing data pair, and at least one data value is interpolated for the missing data pair. An augmented set of data pairs, including the known data pairs and the interpolated data value, is stored in a lookup table. During an open-loop operation period subsequent to the closed-loop device calibration period, the device input is pre-distorted based on the augmented set of data pairs stored in the lookup table.

Abstract: Apparatus, Systems, and Methods are provided for controlling motion of a spacecraft. One apparatus includes a non-contacting actuator and a passive mechanical system coupled in parallel with one another. A system includes a payload, a bus, and a hybrid actuator including a non-contacting actuator and a passive mechanical system coupled in parallel, and coupled between the bus and the payload. The system also includes an inertial actuator configured to maneuver the bus to maintain a relative position and/or attitude of the bus with respect to the payload. One method includes receiving a signal instructing a first controller to change the position and/or attitude of a payload and utilizing a hybrid system to change the position and/or attitude of the payload. The method also includes receiving the signal at a second controller and utilizing a system to change a position and/or attitude of the bus independent of the payload.

Abstract: A system for controlling a plant process wherein the plant is over-actuated with N+1 actuators and provides N performance variables is disclosed. The system includes an outer-loop controller operable to compare N performance variable values from the plant with a reference set point and provide a desired virtual control input to an inner-loop controller. The inner-loop controller receives the desired virtual control input from the outer-loop controller and provides real control inputs to the at least N+1 actuators. The N+1 actuators receive the real control inputs and subsequently provide an actual virtual control input to the plant. Upon receiving the actual virtual control input from the N+1 actuators the plant is operable to produce N updated performance variable values in an optimized manner.

Abstract: A model predictive control apparatus includes an initial solution generation section, an abnormality handling section, and an objective function adjustment section. The abnormality handling section performs initial solution generation when it is determined that performed optimization is abnormal. The initial solution generation section generates an initial optimal value of a future time series control input in accordance with an initial objective function, without reference to a candidate value of the time series control input. The objective function adjustment section adjusts the current objective function so that the current objective function varies stepwise with time toward the normal objective function when it is determined that the current objective function is different from the normal objective function.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: A controller for motor activation providing accurate and repeatable position changes by pressing and releasing a push button switch. Repeatable position changes are made in an advance direction by triggering a digital counter for a predetermined number of cycles of a reference clock signal. Backlash in retard motion of the motor is reduced by similarly asserting a retard motor input for an amount of time determined by another digital counter with a following advance correction made automatically after the retard signal is applied, by applying a predetermined retard-advance movement amount, as again counted by a digital counter. The advance binary amount, the retard binary amount and the retard-advance binary amount of set through binary switch inputs to respective counters to count the respective time periods (TG3, TG1, and TG2).

Abstract: A controller and a method of controlling a process tool is provided, in which machine constants used for calibrating manipulated variables of the control algorithm are explicitly introduced into the process model, thereby providing an enhanced controller behavior immediately after the introduction of new measurement values of the machine constants.

Abstract: A method of predictive control for a single input, single output (SISO) system, including modeling the SISO system with model factors, detecting output from the SISO system, estimating a filtered disturbance from the output, determining a steady state target state from the filtered disturbance and a steady state target output, populating a dynamic optimization solution table using the model factors and a main tuning parameter, and determining an optimum input from the dynamic optimization solution table. Determining an optimum input includes determining a time varying parameter, determining a potential optimum input from the time varying parameter, and checking whether the potential optimum input is the optimum input.

Abstract: Thermomechanical pulp is an important process for producing fibrous mass used in papermaking. A two-level control strategy that stabilizes and optimizes the refining process has been developed. The Stabilization layer consists of a multivariable model predicative range controller that regulates the refiner line operations. The Quality Optimization layer provides the pulp quality control as measured by an online pulp quality (freeness, fibre length) sensor. This control startegy leverages the natural decoupling in the process. The modular design technique is able to handle multiple refiner lines that empty into a common latency chest. A global optimizer is also used to integrate and coordinate the two layers for enhanced constraint handling.

Abstract: A system for embedding real-time Model Predictive Control (MPC) in a System-on-a-Chip (SoC) devices is provided. In the system, a microprocessor is connected to an auxiliary unit or application-specific matrix coprocessor. The microprocessor can control the operation of the MPC algorithm, i.e., carry out the tasks of input/output for the MPC algorithm, initialize and send the appropriate commands to auxiliary unit and receive back the optimal control moves or instructions from auxiliary unit. The auxiliary unit can operate as a matrix coprocessor by executing matrix operations, e.g. addition, multiplication, inversion, etc., required by the MPC algorithm.

Abstract: A method for improving a link schedule used in a communications network is disclosed. While the method applies generally to networks that operate on a scheduled communications basis, it is described in the context of a Foundation FIELDBUS. The method includes: scheduling sequences and their associated publications according to their relative priority, per application; minimizing delays between certain function blocks, and between certain function blocks and publications; and grouping certain publications. Accordingly, advantages such as latency reduction, schedule length reduction, and improved communications capacity are gained.

Abstract: A PID parameter adjustment device includes: a model storage section (1) for storing an equation model to be controlled; a PID controller storage section (2) for storing a control algorithm; a constraint condition storage section (3) for storing a constraint condition for the operation; a simulation operation section (5) for executing simulation of a control system according to the constraint condition; an ideal control result storage section (6) for storing an ideal control response characteristic; an evaluation function operation section (7) for calculating the evaluation function value indicating the closeness between the simulation result and the ideal control response characteristic; and a PID parameter search operation section (8) for executing simulation while modifying the PID parameter and searching a PID parameter at which the evaluation function value is optimal.

Abstract: A system and method for predicting operation of a plant or process receive an input value from the plant or process. An integrity of a non-linear model corresponding to a local input space of the input value may be determined. The non-linear model may include an empirical representation of the plant or process. If the integrity is above a first threshold, non-linear model may be used to provide a first output value. However, if the integrity is below the first threshold, a linearized first principles model may be used to provide a second output value. The linearized first principles model may include an analytic representation of the plant or process. Additionally, the analytic representation of the plant or process may be independent of the empirical representation of the plant or process. The first output value and/or the second output value may be usable to manage the plant or process.

Abstract: The present invention relates to a steady state optimization method incorporating dynamic variance correction for dynamic variations of both independent variables and dependent variables of a dynamic system. The dynamic variance correction is based on measured variance of the variables and a weighing factor for each of the variables. The dynamic variance correction offers an effective method of dynamic violations avoidance of controlled variables for a model predictive controller without having to constantly adjust the tuning weights in response to changing dynamical conditions.

Abstract: An optimization technique for use in driving a process plant controller, such as a model predictive controller, uses an organized, systematic but computationally simple method of relaxing or redefining manipulated, control and/or auxiliary variable constraints when there is no feasible optimal solution within pre-established constraints, to thereby develop an achievable solution for use by the controller. The optimization routine uses penalized slack variables and/or redefines the constraint model in conjunction with the use of penalty variables to develop a new objective function, and then uses the new objective function to determine a control solution that bests meets the original constraint limits.