1,032 research outputs found
Multi - objective sliding mode control of active magnetic bearing system
Active Magnetic Bearing (AMB) system is known to inherit many nonlinearity effects due to its rotor dynamic motion and the electromagnetic actuators which make the system highly nonlinear, coupled and open-loop unstable. The major nonlinearities that are associated with AMB system are gyroscopic effect, rotor mass imbalance and nonlinear electromagnetics in which the gyroscopics and imbalance are dependent to the rotational speed of the rotor. In order to provide satisfactory system performance for a wide range of system condition, active control is thus essential. The main concern of the thesis is the modeling of the nonlinear AMB system and synthesizing a robust control method based on Sliding Mode Control (SMC) technique such that the system can achieve robust performance under various system nonlinearities. The model of the AMB system is developed based on the integration of the rotor and electromagnetic dynamics which forms nonlinear time varying state equations that represent a reasonably close description of the actual system. Based on the known bound of the system parameters and state variables, the model is restructured to become a class of uncertain system by using a deterministic approach. In formulating the control algorithm to control the system, SMC theory is adapted which involves the formulation of the sliding surface and the control law such that the state trajectories are driven to the stable sliding manifold. The surface design involves the transformation of the system into a special canonical representation such that the sliding motion can be characterized by a convex representation of the desired system performances. Optimal Linear Quadratic (LQ) characteristics and regional pole-clustering of the closed-loop poles are designed to be the objectives to be fulfilled in the surface design where the formulation is represented as a set of Linear Matrix Inequality optimization problem. For the control law design, a new continuous SMC controller is proposed in which asymptotic convergence of the system’s state trajectories in finite time is guaranteed. This is achieved by adapting the equivalent control approach with the exponential decaying boundary layer technique. The newly designed sliding surface and control law form the complete Multi-objective SMC (MO-SMC) and the proposed algorithm is applied into the nonlinear AMB in which the results show that robust system performance is achieved for various system conditions. The findings also demonstrate that the MO-SMC gives better system response than the reported ideal SMC (I-SMC) and continuous SMC (C-SMC)
Applications of Nonlinear Control Using the State-Dependent Riccati Equation
This thesis examines the relatively new theory of nonlinear control using state dependent coefficient factorizations to mimic linear state space systems. The control theory is a nonlinear quadratic approach, analogous to linear quadratic regulation. All implementations examined in this thesis are done strictly numerically. This thesis is meant to provide a proof of concept for both satellite control and for an artificial pancreas to regulate blood glucose levels in diabetics by automatic insulin injection. These simulations represent only a first step towards practical use of the NQR method, and do not address noise rejection or robustness issues
Koopman Operator Theory and The Applied Perspective of Modern Data-Driven Systems
Recent theoretical developments in dynamical systems and machine learning have allowed researchers to re-evaluate how dynamical systems are modeled and controlled. In this thesis, Koopman operator theory is used to model dynamical systems and obtain optimal control solutions for nonlinear systems using sampled system data. The Koopman operator is obtained using data generated from a real physical system or from an analytical model which describes the physical system under nominal conditions. One of the critical advantages of the Koopman operator is that the response of the nonlinear system can be obtained from an equivalent infinite dimensional linear system. This is achieved by exploiting the topological structure associated with the spectrum of the Koopman operator and the Koopman eigenfunctions. The main contributions of this thesis are threefold. First, we provide a data-driven approach for system identification, and a model-based approach for obtaining an analytic change of coordinates associated with the principle Koopman eigenfunctions for systems with hyperbolic equilibrium points. A new derivation of the Hamilton-Jacobi equations associated with the infinite time horizon nonlinear optimal control problem is obtained using the Koopman generator. Then, a learning algorithm called Koopman Policy Iteration is used to obtain the solution to the infinite horizon nonlinear optimal fixed point regulation problem without state and input constraints. Finally, the finite time nonlinear optimal control problem with state and input constraints is solved using a receding horizon optimization approach called dual mode model predictive control using Koopman eigenfunctions. Evidence supporting the convergence of these methods are provided using analytical examples
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained
nonlinear optimal control. We generalize the well-known iLQR algorithm to
different multiple-shooting variants, combining advantages like
straight-forward initialization and a closed-loop forward integration. All
algorithms have similar computational complexity, i.e. linear complexity in the
time horizon, and can be derived in the same computational framework. We
compare the full-step variants of our algorithms and present several simulation
examples, including a high-dimensional underactuated robot subject to contact
switches. Simulation results show that our multiple-shooting algorithms can
achieve faster convergence, better local contraction rates and much shorter
runtimes than classical iLQR, which makes them a superior choice for nonlinear
model predictive control applications.Comment: 8 page
Control Engineering
Control means a speci?c action to reach the desired behavior of a system. In the control of industrial processes generally technological processes, are considered, but control is highly required to keep any physical, chemical, biological, communication, economic, or social process functioning in a desired manner
Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion.
A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV
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