A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Generalised predictive current-mode control of passive front-end boost-type converters

In this work, an average current-mode control strategy based on a generalised predictive control formulation for passive front-end three-phase boost-type converters is proposed. A novel design procedure for the generalised predictive control strategy is introduced which considers both the cost function and disturbance model as design parameters to set the controller's dynamic response and robustness against component variations. A maximum robustness criterion was used for achieving stability up to a 70% inductance reduction with maximum possible bandwidth. The proposed strategy was compared against both a PI and a predictive deadbeat average current-mode control using both simulations and experimental results on a (Formula presented.) converter. The generalised predictive control presented less performance variations between different operating points than the PI controller. Also, the proposed strategy is more robust than the predictive deadbeat strategy, showing a better transient response with a 50% inductance reduction and remained stable for a 71% inductance reduction, while the predictive deadbeat could not. Finally, the proposed strategy achieved a 1.4% output voltage load transient response for a (Formula presented.) load power step, and a 2.8% output voltage line transient response for a (Formula presented.) input voltage step, outperforming existing state-of-the-art strategies.Fil: Judewicz, Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: González, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Fischer, Jonatan Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Martínez, Juan Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Carrica, Daniel Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentin

Improved performance of hard disk drive servomechanism using digital multirate control

Ph.DDOCTOR OF PHILOSOPH

Computational modelling of the human motor control system: Nonlinear enhancement of the adaptive model theory through simulation and experiment

Adaptive Model Theory (AMT) proposes that the brain forms and adaptively maintains inverse models of the world around it for adaptive feedforward control. This leading motor control theory unites principles of neurobiology, psychology and engineering. A modified version of AMT was developed with the capacity to control nonlinear systems, to predict signals with nonlinear statistical characteristics, and to perform simultaneous feedback and feedforward adaptive control. The modified version is called nonlinear Adaptive Model Theory or nAMT. An experimental study was also performed investigating inverse model formation in the human motor control system, the results of which were then compared with the nAMT model. A nonlinear dynamic system identification method was developed for nAMT to replace the linear structures employed by AMT. This method employs a neurobiologically-inspired locally-recurrent neural-network structure. A multi-layer adaptation algorithm was also developed specifically for this structure. Nonlinear AutoRegressive Moving-Average (NARMA) adaptive predictor structures replace the linear Moving Average (MA) predictor circuits used in AMT. Adaptive feedback control is augmented using a nonlinear dynamic forward model observer to improve the quality of the estimated response signal. Nonlinear dynamic inverse models are formed by placing the forward model in an internal feedback loop in which the gain function is adjusted to maintain stability. The internal inverse model motor-control hypothesis was tested experimentally in a study looking at human open-loop performance in a tracking task. The study was aimed at directly demonstrating the formation of an internal inverse model of a novel visuomotor relationship for feedforward control in the brain. The study involved 20 normal adult subjects who performed a pursuit random tracking task with a steering wheel for input. During learning the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Results showed a transfer of learning from the unblanked runs to the blanked runs for a static nonlinear system (14% median improvement between first 4 and last 4 runs, p = .001) thereby demonstrating adaptive feedforward control in the nervous system. No such transfer was observed for a dynamic linear system, indicating a dominant adaptive feedback control component. The observed open-loop responses showed a high-pass frequency response which could not be explained using traditional control-systems motor control models. Experimental results were compared with simulated results from the nAMT model. Results from the experimental study were used to verify and tune the computational model. The resulting simulations produced effects that mirrored the closed- and openloop characteristics of the experimental response trajectories. This supports the claim that an internal feedback loop is used for the inversion of external systems in the human brain. Other control-systems models (both AMT and feedback-error learning) would require substantial ad hoc modification to reproduce the observed disparity between closed- and open-loop results. In contrast, nAMT naturally reproduced the effect as a consequence of its novel nonlinear inversion method. In nAMT an inverse model is formed by embedding a forward model in an internal feedback loop incorporating a low derivative gain. The derivative loop-gain caused the inverse model to be relatively inaccurate at low frequencies, for which the feedback control loop was adequate, but to be increasingly accurate at higher frequencies. Maintenance of the loop-gain at the lowest possible levels maximizes the internal stability of the inverse. The simulation work confirmed that the nAMT model is capable of reproducing human behaviour under a wide range of conditions

Disturbance attenuation with multi-sensing servo systems for high density storage devices

Ph.DDOCTOR OF PHILOSOPH

Active Noise Control Using Modally Tuned Phase-Compensated Filters

An active noise control device or an active noise absorber (ANA) that is based on either resonant 2nd - order or 4th - order Butterworth filters is developed and demonstrated. This control method is similar to structural positive position feedback (PPF) control, with two exceptions: 1) acoustic transducers (microphone and speaker) can not be truly colocated, and 2) the acoustic actuator (loudspeaker) has significant dynamics that can affect performance and stability. Acoustic modal control approaches are typically not sought, however, there are a number of applications where controlling a few room modes is adequate. A model of a duct with speakers at each end is developed and used to demonstrate the control method, including the impact of the speaker dynamics. An all-pass filter is used to provide phase compensation and improve controller performance. Two companion experimental studies validated the simulation results. A single mode case using a resonant band-pass filter demonstrated nearly 10 dB of control in the first duct, while a multimodal case using two 4th - order Butterworth band-pass filters show both 10 dB of reduction in the fundamental mode and nearly 8.0 dB in the second