Abstract: Stiction analysis of a conventional or legacy valve operation in a fluid process plant is described. The method includes receiving a series of setpoint signals, SP, generating, by the controller, a series of controller output signals, OP, driving the valve operation by applying the OP, to the valve, measuring a series of process variable signals, PV, downstream of the valve, receiving the series of process variable signals at the controller, performing inverse modelling on the PV, to estimate a manipulated variable, , receiving and analyzing by a system processor, the SP, the OP, and the , and outputting the estimated stick values, S, the estimated jump values, J, an average of the estimated stick values, an average of the estimated jump values, a confidence interval for the average of the stick values, and a confidence interval for the average of the jump values.

Abstract: A method and system for compensating for stiction of a control valve in a pneumatically controlled valve system. A digital twin model of the pneumatically controlled valve system is generated. A current segment of data signals is received from a process measurement device connected to the pneumatically controlled valve system. Operation of the pneumatically controlled valve system is monitored by comparing the current segment to the digital twin model. Stiction is detected when the digital twin model directly detects stiction or when the nonlinearity and Gaussian index are above a certain threshold. A severity of the stiction is determined. Instructions for a stiction control device are used to generate control signals to be applied to the actuator to compensate for the severity of the stiction. Further, the digital twin model is displayed with a representation of the severity of the stiction.

Abstract: A method of determining a fouling location of a shell and tube heat exchanger is provided. Under the method, a simulation model of the heat exchanger is partitioned into multiple segments. Each segment corresponds to a different one of multiple operating scenarios of the heat exchanger. For each operating scenario, temperature data and pressure drop data are generated from the simulation model of the heat exchanger. The fouling location of the heat exchanger corresponds to a respective segment in each of the multiple operating scenarios. The temperature data and the pressure drop data are classified based on the multiple segments of the simulation model by inputting the temperature data and the pressure drop data to one or more machine learning classification algorithms. The fouling location and a value of accumulated fouling at the fouling location are determined based on the classified temperature data and the classified pressure drop data.

Abstract: A method and system for compensating for stiction of a control valve in a pneumatically controlled valve system. A digital twin model of the pneumatically controlled valve system is generated. A current segment of data signals is received from a process measurement device connected to the pneumatically controlled valve system. Operation of the pneumatically controlled valve system is monitored by comparing the current segment to the digital twin model. Stiction is detected when the digital twin model directly detects stiction or when the nonlinearity and Gaussian index are above a certain threshold. A severity of the stiction is determined. Instructions for a stiction control device are used to generate control signals to be applied to the actuator to compensate for the severity of the stiction. Further, the digital twin model is displayed with a representation of the severity of the stiction.

Abstract: In a method for failure, detection, operational data from a plurality of components in a system are received. A bank of submodels is created based on the operational data. The bank of submodels corresponds to a normal mode and one or more faulty modes of the system and is valid in different operating regimes of the system. Each of the banks of submodels has a respective weight (validity) and a respective suboutput. An output of the system is a weighted sum of the suboutputs of the submodels. The operational data is therefore processed to generate a validity profile through a constrained Kalman Filter (KCF) based multimodel fault detection and diagnosis (FDD). Subsequently, the validity profile is output. The validity profile is indicative of an operation state of the system at a given time, and the operation state includes a normal state and a fault state.

Abstract: This disclosure presents methods and systems of controlling a counter flow double pipe heat exchanger (DPHE) that includes a hot fluid pipe and a cold fluid pipe. In a method, a temperature error between a reference temperature and a temperature at an outlet of the hot fluid pipe of the counter flow DPHE is determined. A cold fluid mass flow rate is determined from an output of a proportional-integral-derivative (PID) controller based on the temperature error being input to the PID controller. The cold fluid mass flow rate is used for a cold fluid in the cold fluid pipe of the counter flow DPHE. The temperature error is controlled within a predefined range by utilizing parameters of the PID controller that are set by using a harmony search algorithm (HSA) to obtain a minimization of a cost function.

Abstract: In a method for failure detection, operational data from a plurality of components in a system are received. A bank of submodels is created based on the operational data. The bank of submodels corresponds to a normal mode and one or more faulty modes of the system and is valid in different operating regimes of the system. Each of the banks of submodels has a respective weight (validity) and a respective suboutput. An output of the system is a weighted sum of the suboutputs of the submodels. The operational data is therefore processed to generate a validity profile through a constrained Kalman Filter (KCF) based multimodel fault detection and diagnosis (FDD). Subsequently, the validity profile is output. The validity profile is indicative of an operation state of the system at a given time, and the operation state includes a normal state and a fault state.

Abstract: A pneumatic valve system that includes an actuator that pneumatically actuates a valve, and circuitry that calculates a control signal to control the actuator by compensating for nonlinear dynamic of the actuator using a stable inverse model of the valve, optimizes parameters of the stable inverse model such that a difference between output information of the pneumatic valve system and desired reference information is reduced, and outputs the control signal to control the actuator.

Abstract: A pneumatic valve system that includes an actuator that pneumatically actuates a valve, and circuitry that calculates a control signal to control the actuator by compensating for nonlinear dynamic of the actuator using a stable inverse model of the valve, optimizes parameters of the stable inverse model such that a difference between output information of the pneumatic valve system and desired reference information is reduced, and outputs the control signal to control the actuator.

Abstract: A pneumatic valve system that includes an actuator that pneumatically actuates a valve, and circuitry that calculates a control signal to control the actuator by compensating for nonlinear dynamic of the actuator using a stable inverse model of the valve, optimizes parameters of the stable inverse model such that a difference between output information of the pneumatic valve system and desired reference information is reduced, and outputs the control signal to control the actuator.

Abstract: Described herein a robot assisted method of deploying sensors in a geographic region. The method of deploying sensors is posed as a Markovian decision process. The robot assigns each grid cell in a map of the geographic region a reward value based on a surface elevation of the geographic region and a soil hardness factor. Further, the robot determines an action for each grid cell of the plurality of grid cells, wherein the action corresponds to an expected direction of movement of the robot in the grid cell. The robot computes a global path as a concatenation of actions starting from a first grid cell and terminating at a second grid cell. The method monitors the movement of the robot on the computed global path and computes a second path based on a deviation of the robot from the global path.

Abstract: Described herein a robot assisted method of deploying sensors in a geographic region. The method of deploying sensors is posed as a Markovian decision process. The robot assigns each grid cell in a map of the geographic region a reward value based on a surface elevation of the geographic region and a soil hardness factor. Further, the robot determines an action for each grid cell of the plurality of grid cells, wherein the action corresponds to an expected direction of movement of the robot in the grid cell. The robot computes a global path as a concatenation of actions starting from a first grid cell and terminating at a second grid cell. The method monitors the movement of the robot on the computed global path and computes a second path based on a deviation of the robot from the global path.

Abstract: Described herein a robot assisted method of deploying sensors in a geographic region. The method of deploying sensors is posed as a Markovian decision process. The robot assigns each grid cell in a map of the geographic region a reward value based on a surface elevation of the geographic region and a soil hardness factor. Further, the robot determines an action for each grid cell of the plurality of grid cells, wherein the action corresponds to an expected direction of movement of the robot in the grid cell. The robot computes a global path as a concatenation of actions starting from a first grid cell and terminating at a second grid cell. The method monitors the movement of the robot on the computed global path and computes a second path based on a deviation of the robot from the global path.

Abstract: Described herein a robot assisted method of deploying sensors in a geographic region. The method of deploying sensors is posed as a Markovian decision process. The robot assigns each grid cell in a map of the geographic region a reward value based on a surface elevation of the geographic region and a soil hardness factor. Further, the robot determines an action for each grid cell of the plurality of grid cells, wherein the action corresponds to an expected direction of movement of the robot in the grid cell. The robot computes a global path as a concatenation of actions starting from a first grid cell and terminating at a second grid cell. The method monitors the movement of the robot on the computed global path and computes a second path based on a deviation of the robot from the global path.

Abstract: A pneumatic valve system that includes an actuator that pneumatically actuates a valve, and circuitry that calculates a control signal to control the actuator by compensating for nonlinear dynamic of the actuator using a stable inverse model of the valve, optimizes parameters of the stable inverse model such that a difference between output information of the pneumatic valve system and desired reference information is reduced, and outputs the control signal to control the actuator.

Abstract: The control system and method for multi-vehicle systems provides nonlinear model predictive control (NMPC) to regulate navigation of multiple autonomous vehicles (mobile robots) operating under automatic control. The system includes an NMPC controller and an NMPC algorithm. The NMPC controller includes an optimizer, a state predictor, and a state estimator. Data compression is accomplished using a neural networks approach.

Abstract: A system and method of controlling quadrotor air vehicles (QRAV) that may include an additional two degrees of freedom for each of the four propellers of the QRAV. Each of the four rotors may be allowed to rotate (tilt) around two local axes selected from the x-axis (roll), y-axis (pitch), and z-axis (yaw). Control of the quadrotor including the additional two degrees of freedom allows thrust of each rotor to be direct in any direction of a semi-sphere. As a result, total control inputs of the QRAV may be increased to twelve, enabling smooth control to achieve superior and precise maneuverability. Additionally, the system and method is fault tolerant and capable of handling failures of any of the rotors. Commands to the propellers may be fully decoupled and achieved independently thereby giving pilots better control to execute difficult maneuvers.

Abstract: The adaptive control method for an unmanned vehicle with a slung load utilizes a feedback linearization controller (FLC) to perform vertical take off, hovering and landing of an unmanned aerial vehicle with a slung load, such as a quadrotor drone or the like. The controller includes a double loop architecture, where the overall controller includes an inner loop having an inner controller which is responsible for controlling the attitude angles and the altitude, and an outer loop having an outer controller responsible for providing the inner loop inner controller with the desired angle values. States, such as including roll, pitch, yaw and/or altitude, are selected as outputs and the feedback linearization technique is used.

Abstract: The robotic leader-follower navigation and fleet management control method implements SLAM in a group leader of a nonholonomic group of autonomous robots, while, at the same time, executing a potential field control strategy in follower members of the group.

Abstract: The multiphase flow measurement system and method includes video sensors and illumination along a pipe upstream, and a similar arrangement of sensors and illumination downstream displaced from the first location by a known distance utilized for velocity calculation between images at both locations. Image capturing and processing is done from video data at both locations. Objects corresponding to each flow phase in the video images are characterized and sorted by size, color, spectral properties, shapes, and pattern features using automatic pattern recognition. Image cross-correlation and pattern recognition are applied to each group of objects corresponding to one or more flow phases to estimate the velocity of each phase based on the delay calculation. Flow velocity is calculated from the weighted average of the phase velocities. Overall flow type is estimated using the estimated phase velocities and the areas occupied by the objects corresponding to each phase in the images.