Abstract: A distributive adaptive control system for HVAC control and a method for controlling the temperature in a building with one or more zones is disclosed. The control system and method are based on a design that may accommodate buildings with multiple interconnected thermal zones. The system includes a controller for each zone of the building. Each controller is designed to regulate temperature while attenuating the effect of directly neighboring zones, wall temperature, weather conditions and heat gains. The control mechanism does not require any prior accurate knowledge of system parameters but instead calibrates itself to meet the needs of each thermal zone. An appropriate adaptive law may be used for learning the building and HVAC system parameters and auto-calibrating the controller. The proposed system and method can extend the life of the HVAC by compensating for a wide range of tear and wear and other defects in the equipment.

Abstract: A distributive adaptive control system for HVAC control and a method for controlling the temperature in a building with one or more zones is disclosed. The control system and method are based on a design that may accommodate buildings with multiple interconnected thermal zones. The system includes a controller for each zone of the building. Each controller is designed to regulate temperature while attenuating the effect of directly neighboring zones, wall temperature, weather conditions and heat gains. The control mechanism does not require any prior accurate knowledge of system parameters but instead calibrates itself to meet the needs of each thermal zone. An appropriate adaptive law may be used for learning the building and HVAC system parameters and auto-calibrating the controller. The proposed system and method can extend the life of the HVAC by compensating for a wide range of tear and wear and other defects in the equipment.

Abstract: The control of flexible systems is often difficult due to the exact frequencies of the elastic modes being hard to identify. These flexible modes may change over time, or vary between units of the same system. The variation in the modal dynamics may cause a degradation in performance or even instabilities unless compensated for by the control scheme. Controllers designed for these types of systems use notch filters for mode suppression. However variation in the parameters of the flexible modes may cause the need for wide notch filters. An adaptive scheme is proposed which uses an online estimator based on plant parameterization. The scheme may not use probe signals and may not rely on exact parameter identification of the unknown parameters. Instead it may continuously update itself to cancel the effect of the flexible modes by been able to identify the effect of the modal dynamics on the performance of the system.

Abstract: The control of flexible systems is often difficult due to the exact frequencies of the elastic modes being hard to identify. These flexible modes may change over time, or vary between units of the same system. The variation in the modal dynamics may cause a degradation in performance or even instabilities unless compensated for by the control scheme. Controllers designed for these types of systems use notch filters for mode suppression. However variation in the parameters of the flexible modes may cause the need for wide notch filters. An adaptive scheme is proposed which uses an online estimator based on plant parameterization. The scheme may not use probe signals and may not rely on exact parameter identification of the unknown parameters. Instead it may continuously update itself to cancel the effect of the flexible modes by been able to identify the effect of the modal dynamics on the performance of the system.

Abstract: Analysis of a linear time-invariant system's sampled output data can identify the unknown continuous-time transfer function of the system. The method excites the system with two orthogonal sinusoids and constructs a linear complex equation that relates the sampled output points to the unknown values of the transfer function. This method can identify the continuous-time dynamics of a hard disk drive system in which the number of servo bursts and the spinning speed of the disk fix the sampling frequency of the output. In the hard disk case the input excitation signal is passed through a specially designed pre-filter and the sampled output data are modified to account for the control input and external disturbances. The method can also be used to reconstruct the output of the system even in the presence of aliasing provided the input excitation can be approximated by a sum of sinusoids with known frequencies.

Abstract: A servo control apparatus and method controls systems at least partially on the basis of an observable variable that has an absolute value functional relationship with the controlled variable and does not change sign for positive and negative variations from a nominal value. When applied to the positional control of an object, the control system observes a value of a position error signal and maps that signal to two different potentially correct displacement values. Two estimators within the control system are initiated, one using the positive displacement and the other using the negative displacement, and the two estimators each predict the future position of the object and the corresponding position error signal for each estimated position. A new position error signal is detected and compared to the two estimated position error signals.

Abstract: A servo control apparatus and method controls systems at least partially on the basis of an observable variable that has an absolute value functional relationship with the controlled variable and does not change sign for positive and negative variations from a nominal value. When applied to the positional control of an object, the control system observes a value of a position error signal and maps that signal to two different potentially correct displacement values. Two estimators within the control system are initiated, one using the positive displacement and the other using the negative displacement, and the two estimators each predict the future position of the object and the corresponding position error signal for each estimated position. A new position error signal is detected and compared to the two estimated position error signals.