Abstract: A motor control wizard implements a simple workflow for creating an application-specific program for operation of a motor control system. The wizard prompts for selection of an application area, which sensitizes the system to tune certain motor control parameters in accordance with the demands of the selected application area. The wizard also prompts for selection of a target devices, such as a particular type of motor with a set of basic operating parameters. With the target device and application area known, the wizard runs an automatic adaptation step without requiring additional user-settable parameters. The adaptation step yields an adapted motor control program based characteristics of the motor control system obtained via the adaptation step. The wizard then confirms operation of the motor using the adapted program. Additional features allow the user to fine tune parameters beyond this set of initial configuration parameters.

Abstract: A motion control design system implements a performance specification-oriented design approach. The system allows a designer to define desired performance specifications that are to be satisfied by a motion system, and determines tuning parameters (e.g., controller bandwidth and associated tuning parameters) that will yield performance within the defined performance specifications. An identification process identifies system parameters of interest based on collected system data. After the user has entered the desired performance specifications, an optimization process determines a range of bandwidths that will satisfy all specified performance requirements, and selects an optimal bandwidth within this range. A tuning process generates the corresponding tuning parameters for a systematically designed motion controller.

Abstract: As speed operation range identification system for motion systems driven by permanent magnet synchronous motors (PMSMs) or induction motors leverages both characteristics of the motor as well as dynamic characteristics of the motion system—including the friction and load—to identify suitable maximum speeds for operation of the motion system in the normal speed and field weakening regions. The identification system can model both motor characteristics as well as real-time dynamics of the controlled mechanical system that may vary during operation. The system can apply an optimization algorithm to this model to determine suitable maximum speeds for operation in the normal speed and/or field weakening regions. The determined maximum speeds can be used to perform substantially real-time adjustments to motion profile limits or current reference values generated by the motor controller in order to ensure that the speed of the system remains below the determined maximum.

Abstract: A resonance estimation system implements resonance detection methods that can obtain accurate estimates of a motion system's resonance and amplitude without the need for a high-resolution encoder or high-frequency sampling. The system solves for resonance information by removing the slow motion dynamics and fast torque control dynamics from the measured speed transfer function in order to obtain resonance transfer function. Smoothing functions are applied to the obtained resonance frequency response data to remove spikes and obtain relatively smooth gain and phase curves for the resonance frequency response. The system then applies a searching algorithm to determine the locations of the phase peaks in the phase curve data, and uses these phase peak locations to locate the gain peaks in the gain curve data, which correspond to the resonance frequencies and amplitudes. This approach allows the gain peaks to be located even when analyzing non-ideal gain curves that are degraded by noise.

Abstract: A curve fitting system implements a multi-level curve fitting approach to obtain a mapping between variables of interest. According to this approach, the system establishes first curve fitting relationships between a first variable of interest and a second variable of interest for sets of values of the independent variables. Then, second curve fitting relationships are established between the coefficients of the previously established curve fitting relationships and one of the independent variables for multiple value sets of the remaining independent variables. These recursive curve fitting operations are repeated until curve fitting relationships are obtained for all of the independent variables. The system then generates mapping data based on the curve fitting relationships, where the mapping data defines a mapping from the first independent variable to the second independent variable.

Abstract: Systems and methods for estimating an inertia, a Coulomb friction coefficient, and a viscous friction coefficient for a controlled mechanical system are provided. In one or more embodiments, an inertia and friction estimation system can generate a torque command signal that varies continuously over time during a testing sequence. The velocity of a motion system in response to the time-varying torque command signal is measured and recorded during the testing sequence. The estimation system then estimates the inertia and the friction coefficients of the motion system based on the torque command data sent to the motion system and the measured velocity data. In some embodiments, the estimation system estimates the inertia and the friction coefficients based on integrals of the torque command data and the velocity data.

Abstract: A system for tuning a control system uses a simplified tuning procedure to generate robustly stabilizing tuning parameters that reduce or eliminate undesired system oscillations in the presence of long system dead times or phase lag. A control method is used to establish a relationship between the plant parameters of a controlled system and the tuning parameters of a parameterized active disturbance rejection controller determined to be optimal or substantially optimal for the control system. The plant parameters include the system gain, time constant, and dead time. Corresponding tuning parameters include the controller bandwidth and a system gain estimate. Using the system gain estimate as a tuning parameter can alleviate the influence of large dead times or phase lags on system response. Once established, these fixed relationships can be used to determine suitable tuning parameters for specific motion or process control applications based on the system gain and dominant constraints of the system.

Abstract: A field oriented control (FOC) system and method provides smooth field-oriented startup for three-phase sensorless permanent magnet synchronous motors (PMSMs) despite the absence of load information. The system uses the rotor flux projection on the d- or q-axis to determine whether the stator flux current reference being applied during reference startup phase is sufficient to spin the PMSM, thereby providing smooth operation during the reference startup phase and saving energy relative to applying rated current. The system also determines a suitable initial value for the stator torque current reference to use at the start of closed-loop sensorless FOC control mode based on an angle difference between the reference and estimated angles. Since this angle difference is reflective of the load on the PMSM, the selected initial value allows the system to achieve a smooth transition from reference startup mode to closed-loop sensorless FOC control mode.

Abstract: A maximum acceleration identification system determines a suitable maximum acceleration for transitioning a given motion/motor system to a target position or velocity, taking the friction of the motion system into consideration. The maximum acceleration determined by the maximum acceleration identification system can then be used by the motion control system as the acceleration limit for generating motion profiles. Thus, motion profiles can be generated that are closer to the true maximum acceleration supported by the motion system without violating the mechanical and electrical constraints of the system as characterized in part by the viscous friction, resulting in a more time-optimal move. In some embodiments, the maximum acceleration identification system can automatically set the maximum acceleration of the control system's profile generator to be equal to the derived value, thereby eliminating the need for the maximum acceleration to be selected and set by the system designer.

Abstract: Robustly stabilizing controller bandwidth for a controlled mechanical system is determined as a function of the system's estimated inertia and dominant system parameters that define constraints on the bandwidth. In one or more embodiments, a bandwidth model is derived that defines a relationship between robustly stabilizing controller bandwidth and system gain over a range of reasonable system uncertainties. Using the model, a suitable controller bandwidth can be determined for a given motion control system given only estimates of the system gain and the dominant parameters(s) that constrain the bandwidth. In an example two-inertia system, the system gain and dominant parameter can comprise system inertia and mechanical coupling stiffness, respectively. Accordingly, estimates of the system inertia and coupling stiffness can be provided to the system, which determines a suitable controller bandwidth for the motion control system characterized by the estimates.

Abstract: A field oriented control (FOC) system and method provides smooth field-oriented startup for three-phase sensorless permanent magnet synchronous motors (PMSMs) despite the absence of load information. The system uses the rotor flux projection on the d- or q-axis to determine whether the stator flux current reference being applied during reference startup phase is sufficient to spin the PMSM, thereby providing smooth operation during the reference startup phase and saving energy relative to applying rated current. The system also determines a suitable initial value for the stator torque current reference to use at the start of closed-loop sensorless FOC control mode based on an angle difference between the reference and estimated angles. Since this angle difference is reflective of the load on the PMSM, the selected initial value allows the system to achieve a smooth transition from reference startup mode to closed-loop sensorless FOC control mode.

Abstract: Systems and methods for estimating an inertia and a friction coefficient for a controlled mechanical system are provided. In one or more embodiments, an inertia estimator can generate a torque command signal that varies continuously over time during a testing sequence. The velocity of a motion system in response to the time-varying torque command signal is measured and recorded during the testing sequence. The inertia estimator then estimates the inertia and/or the friction coefficient of the motion system based on the torque command data sent to the motion system and the measured velocity data. In some embodiments, the inertia estimator estimates the inertia and the friction coefficient based on integrals of the torque command data and the velocity data.

Abstract: Systems and methods are provided for generating a constraint-based, time-optimal motion profile for controlling the trajectory of a point-to-point move in a motion control system. A profile generator can calculate an ST-curve motion profile that includes a jerk reference that varies continuously over time for at least one of the motion profile segments, thereby producing a smooth, time-optimal trajectory. The profile generator can create the motion profile to conform to a set of motion constraints provided by the user. The profile generator also supports calculation of time-optimal motion profiles having segments that align to the sample time of the motion control system. In some embodiments, the profile generator can efficiently generate the motion profile by performing reference calculations only for those segments that will be used in the final motion profile for a given point-to-point move.

Abstract: Systems and methods for estimating an inertia and a friction coefficient for a controlled mechanical system are provided. In one or more embodiments, an inertia estimator can generate a torque command signal that varies continuously over time during a testing sequence. The velocity of a motion system in response to the time-varying torque command signal is measured and recorded during the testing sequence. The inertia estimator then estimates the inertia and/or the friction coefficient of the motion system based on the torque command data sent to the motion system and the measured velocity data. In some embodiments, the inertia estimator estimates the inertia and the friction coefficient based on integrals of the torque command data and the velocity data.

Abstract: Systems and methods are provided for generating a constraint-based, time-optimal motion profile for controlling the trajectory of a point-to-point move in a motion control system. A profile generator can calculate an ST-curve motion profile that includes a jerk reference that varies continuously over time for at least one of the motion profile segments, thereby producing a smooth, time-optimal trajectory. The profile generator can create the motion profile to conform to a set of motion constraints provided by the user. The profile generator also supports calculation of time-optimal motion profiles having segments that align to the sample time of the motion control system. In some embodiments, the profile generator can efficiently generate the motion profile by performing reference calculations only for those segments that will be used in the final motion profile for a given point-to-point move.

Abstract: Systems and methods for estimating an inertia and a friction coefficient for a controlled mechanical system are provided. In one or more embodiments, an inertia estimator can generate a torque command signal that varies continuously over time during a testing sequence. The velocity of a motion system in response to the time-varying torque command signal is measured and recorded during the testing sequence. The inertia estimator then estimates the inertia and/or the friction coefficient of the motion system based on the torque command data sent to the motion system and the measured velocity data. In some embodiments, the inertia estimator estimates the inertia and the friction coefficient based on integrals of the torque command data and the velocity data.