5 research outputs found

    Learning a controller for a coupled drives apparatus using VRFT strategy

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    The paper utilizes the Virtual Reference Feedback Tuning methodology for the iterative way of controller design and fine-tuning. It uses a series of experiments with no restriction on data generation to design an optimal controller of desired structure without the intermediate plant identification step. The approach is shown to be successful for the design and fine-tuning of a controller for a coupled drives system by means of adjusting a desired settling-time of a controlled variable

    A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

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    The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies

    Active Suppression ofAerofoil Flutter via Neural-Network-Based Adaptive Nonlinear Optimal Control

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    This thesis deals with active flutter suppression (AFS) on aerofoils via adaptive nonlinear optimal control using neural networks (NNs). Aeroelastic flutter can damage aerofoils if not properly controlled. AFS not only ensures flutter-free flight but also enables the use of aerodynamically more efficient lightweight aerofoils. However, existing optimal controllers for AFS are generally susceptible to modelling errors while other controllers less prone to uncertainties do not provide optimal control. This thesis, thus, aims to reduce the impact of the dilemma by proposing new solutions based on nonlinear optimal control online synthesis (NOCOS) according to online updated dynamics. Existing NOCOS methods, with NNs as essential elements, require a separate initial stabilising control law for the overall system, an additional stabilising tuning loop for the actor NN, or an additional stabilising term in the critic NN tuning law, to guarantee the closed-loop stability for unstable and marginally stable systems. The resulting complexity is undesired in AFS applications due to computational concerns in real-time implementation. Moreover, the existing NOCOS methods are confined to locally nonlinear systems, while aeroelastic systems under consideration are globally nonlinear. These make all the existing NOCOS algorithms inapplicable to AFS without modification and improvement. Therefore, this thesis solves the aforementioned problems through the following step-by-step approaches. Firstly, a four degrees-of-freedom (4-DOF) aeroelastic model is considered, where leading- and trailing-edge control surfaces of the aerofoil are used to actively suppress flutter. Accordingly, a virtual stiffness-damping system (VSDS) is developed to simulate physical stiffness in the aeroelastic system. The VSDS, together with a scaled-down typical aerofoil section placed in a wind tunnel, serve as an experimental 4-DOF aeroelastic test-bed for synthesis and validation of proposed AFS controllers that follow. Secondly, a Modified form of NN-based Value Function Approximation (MVFA), tuned by gradient-descent learning, is proposed for NOCOS to address the closedloop stability in a compact controller configuration suitable for real-time implementation. Its validity and efficacy are examined by the Lyapunov stability analysis and numerical studies. Thirdly, a systematic procedure based on linear matrix inequalities is further proposed for synthesising a scheduled parameter matrix to generalise the MVFA to to globally nonlinear cases, so that the new NN controller suits AFS applications. In addition, the extended Kalman filter (EKF) is proposed for the new NN controller for fast parameter convergence. An identifier NN is also derived to capture and update aeroelastic dynamics in real time to mitigate the impact of modelling errors. Wind-tunnel experiments were conducted for validation. Finally, a non-quadratic functional is introduced to generalise the performance index to tackle the problem where control inputs are constrained. The feasibility of including the non-quadratic cost function under the proposed control scheme based on the MVFA is examined via the Lyapunov stability analysis and was also experimentally evaluated through the wind-tunnel testings. The proposed NN controllers are compact in structure and shown capable of maintaining the closed-loop stability while eliminating the need for a separate initial stabilising control law for the overall system, an additional tuning loop for the actor NN, and an additional stabilising term in the critic NN tuning law. Under the new control schemes, online synthesised nonlinear control laws are optimal in the cases with and without constraints in control. Comparisons drawn with a popular linear-parameter-varying (LPV) controller in the form of the widely used linear quadratic regulator (LQR) in experiments show that the proposed NN controllers outperform the LPV-LQR algorithm and improve AFS from the optimal control perspective. Specifically, the proposed NN controllers can effectively mitigate the impact of modelling errors, successfully solving the mentioned dilemma involved in AFS. The results also confirm that the proposed NN controllers are suitable for real-time implementation.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

    The application of estimation and control techniques in 2 modes of exercise for the spinal cord injured

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    A spinal cord injury (SCI) can result in a loss of sensory and motor capacity, dysfunction of the autonomic nervous system and also in a number of secondary health conditions including muscular atrophy, cardiovascular disease and osteoporosis. The impact of these secondary health conditions may be reduced through exercise which loads the muscles, skeleton and central cardiovascular system. A number of new exercise methods are emerging in the field of rehabilitation. Functional electrical stimulation (FES) is a technique for inducing artificial muscular contractions that has been applied to facilitate cycling amongst adults with a spinal cord injury. Preliminary data has demonstrated the feasibility of FES cycling in the paediatric SCI population. The use of an electric motor to provide torque assistance where required allows the exercise to continue for longer periods and over a wider range of cadences. In this thesis, a feedback control system is devised whereby the cadence can be automatically controlled to reference levels using such a motor, and tested during FES cycling of children with an SCI. The use of robot-assisted body weight supported devices is gaining popularity in the rehabilitation world. Their application has thus far been focused on rehabilitation of gait via neural re-learning. However, robot-assisted gait can also elicit a significant cardiovascular response and thus has potential as a tool for exercise training and testing. In this thesis, a method for estimating the work rate contributed by an exercising subject is developed and then incorporated into a feedback control scheme where the objective is to regulate the work rate to reference values. This enables specific work rate profiles to be performed during robot-assisted gait as is often required for standard exercise tests and training. In addition to controlling the mechanical variables during exercise, it is also possible to control some of the physiological variables. A feedback system whose goal is to control the rate of oxygen uptake rate is developed which also incorporates the work rate control method. This allows a predetermined level of physiological response to be achieved so that the training is of sufficient intensity to promote improvements in physical capacity and fitness. This thesis examines the application of estimation and control techniques in two exercise modes for the spinal cord injured. The ultimate aim of the exercise is to reduce the severity of the secondary health conditions that spinal cord injured people face. The estimation and control algorithms allow the exercise to be regulated with respect to speed and intensity and therefore have utility in both training and testing applications
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