1,721,032 research outputs found
A Global Linearization Approach to Control and State Estimation of a Voltage Source Converter: HVDC System
No full textA control method is developed for the VSCHVDC
system, that is for an AC to DC voltage source
converter connected to the electricity grid through a high
voltage DC transmission line terminating at an inverter. By
showing that the VSC-HVDC system is a differentially flat
one, its transformation to the linear canonical form becomes
possible. This is a global input–output linearization procedure
that results into an equivalent dynamic model of the
VSC-HVDC system for which the design of a state feedback
controller becomes possible. Moreover, to estimate and
compensate for modeling uncertainty terms and perturbation
inputs exerted on the VSC-HVDC model it is proposed to
include in the control loop a disturbance observer that is based
on the Derivative-free nonlinear Kalman Filter. This filtering
method makes use of the linearized equivalent model of the
VSC-HVDC system and of an inverse transformation which
is based on differential flatness theory and which finally provides
estimates of the state variables of the initial nonlinear model. The performance of the proposed VSC-HVDC control
scheme is evaluated through simulation experiments
Nonlinear optimal control for wind power generators comprising a multi-mass drivetrain and a DFIG
No full textThe article proposes a nonlinear optimal (H-infinity) control method for a wind power generation system comprising a two-mass drivetrain and a Doubly-Fed Induction Generator (DFIG). Comparing to the 6-th order dynamic model of the DFIG, the state-space model of the considered power generation unit is extended after including in it the dynamics of the drivetrain. To solve the associated control problem, the dynamic model of the power generation unit undergoes approximate linearization around a temporary operating point which is recomputed at each time-step of the control algorithm. The linearization procedure relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the linearized state-space description of the power unit, an H-infinity controller is developed. This stands for the solution of the optimal control problem for the wind power system under model uncertainty and external perturbations. For the computation of the controller's feedback gain an algebraic Riccati equation is repetitively solved at each iteration of the control method. Moreover, with the use of Lyapunov analysis the global asymptotic stability of the control scheme is proven. Finally, to implement state estimation-based control without the need to measure the entire state vector of the power unit, the H-infinity Kalman Filter is used as a robust observer
An adaptive neurofuzzy H-infinity control method for bioreactors and biofuels production
No full textA novel adaptive neurofuzzy H-infinity control approach to feedback control and stabilization of the nonlinear dynamical model of bioreactors used in biofuels production is developed. The form and the parameters of the differential equations that constitute the dynamic model of the bioreactor are considered to be unknown, while there is only knowledge about the order of the system. The model of the controlled system undergoes approximate linearization round a temporary equilibrium which is recomputed at each iteration of the control algorithm. The linearization procedure makes use of Taylor series expansion and the computation of Jacobian matrices. For the approximately linearized model of the bioreactor it is possible to design a stabilizing H-infinity feedback controller, provided that knowledge about the matrices of the linearized state-space description is available. Neurofuzy networks are used to estimate the unknown dynamics of the system and its Jacobians. The computation of the feedback controller's gain comes from the solution of an algebraic Riccati equation taking place at each iteration of the control method, and this allows the implementation of the H-infinity feedback controller. The learning rate of the neurofuzzy approximators is chosen from the requirement the first derivative of the system's Lyapunov function to be always a negative one, thus assuring the stability of the control loop. The global asymptotic stability and the robustness properties of the control method are proven through Lyapunov stability analysis
A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs
No full textVoltage-source inverter-fed Permanent Magnet Synchronous Machines are widely used in industry (for instance for the actuation of robotic and mechatronic systems, of cranes, in water pumping stations) as well as in transportation systems (for the traction of trains and electric vehicles). The present article proposes a nonlinear optimal control approach for voltage source inverter-fed Permanent Magnet Synchronous Machines (VSI-PMSMs). The nonlinear dynamic model of VSI-PMSMs undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. This temporary operating point is defined by the present value of the voltage source inverter-fed PMSM state vector and by the last sampled value of the machine's control inputs vector. The linearization relies on Taylor series expansion and on the calculation of the system's Jacobian matrices. For the approximately linearized model of the voltage source inverter-fed PMSM an H-infinity feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem for the voltage source inverter-fed PMSM under model uncertainty and external perturbations. For the computation of the controller's feedback gain an algebraic Riccati equation is iteratively solved at each time-step the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis
A nonlinear H-infinity control approach for autonomous truck and trailer systems
No full textA nonlinear optimal control method is developed for autonomous truck and trailer systems. Actually, two cases are distinguished: (a) a truck and trailer system that is steered by the front wheels of its truck, (b) an autonomous fire-truck robot that is steered by both the front wheels of its truck and by the rear wheels of its trailer. The kinematic model of the autonomous vehicles undergoes linearization through Taylor series expansion. The linearization is computed at a temporary operating point that is defined at each time instant by the present value of the state vector and the last value of the control inputs vector. The linearization is based on the computation of Jacobian matrices. The modeling error due to approximate linearization is considered to be a perturbation that is compensated by the robustness of the control scheme. For the approximately linearized model of the autonomous vehicles an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is confirmed through Lyapunov analysis. It is shown that the control loop exhibits the H-infinity tracking performance which implies elevated robustness against modeling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven. Finally, to implement state estimation-based control for the autonomous vehicles, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed
Flatness-based control in successive loops for robotic manipulators and autonomous vehicles
No full textThe control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator and (b) control of a 3-DOF autonomous underwater vessel. The novel control method can simplify significantly the solution of the nonlinear control problem for robotic manipulators and vehicles. Unlike global linearisation-based control schemes, the proposed flatness-based method in successive loops does not need any changes in state variables of complicated state-space model transformations
Flatness-based adaptive control of synchronous reluctance machines with output feedback
No full textThe present article proposes an adaptive neurofuzzy control method that is capable of compensating for model uncertainty and parametric changes of Synchronous Reluctance Machines (SRMs), as well as for lack of measurements for the SRMs state vector elements. First it is proven that the SRM's model is a differentially flat one. This means that all its state variables and its control inputs can be written as differential functions of key state variables which are the so-called flat outputs. Moreover, this implies that the flat output and its derivatives are linearly independent. By exploiting differential flatness properties it is shown that the 4-th order SRM model can be transformed into the linear canonical form. For the latter description, the new control inputs comprise unknown nonlinear functions which can be identified with the use of neurofuzzy approximators. The estimated dynamics of the electric machine is used by a feedback controller thus establishing an indirect adaptive control scheme. Moreover, to improve the robustness of the control loop a supplementary control term is computed using H-infinity control theory. Another problem that has to be dealt with comes from the inability to measure the complete state vector of the SRM. Thus, a state-observer is implemented in the control loop. The stability of the considered observer-based adaptive control approach is proven using Lyapunov analysis
Nonlinear H-infinity control for hybrid excited synchronous generators
No full textThe model of a hybrid excited synchronous generator is analyzed and a nonlinear optimal (H-infinity) control method is proposed for it. This type of generator receives primary excitation at its stator's winding through an AC/DC and DC/AC converter, and auxiliary excitation at a secondary winding that is fed by an AC to DC converter. Through the hybrid excitation scheme more control inputs are applied to the generator, thus achieving better performance for the system's control loop. To implement the proposed control method the dynamic model of the generator undergoes approximate linearization around a temporary operating point which is recomputed at each time-step of the control algorithm. The linearization procedure relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the approximately linearized model of the hybrid excited synchronous generator a stabilizing H-infinity feedback controller is designed. To compute the controller's feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control method. The global stability properties of the control scheme are proven through Lyapunov stability analysis
A nonlinear optimal control approach for the Lotka-Volterra dynamical system
No full textA nonlinear optimal (H-infinity) control method is developed for the Lotka-Volterra dynamical system. First, differential flatness properties are proven. The state-space description undergoes linearization, at each sampling instance, with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Next, for the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. Global stability properties are proven through Lyapunov analysis. Finally, the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops
A nonlinear optimal control method for bioreactors and biofuels production
No full textA nonlinear optimal H-infinity control approach is proposed for bioreactors aiming at improved biofuels production. The dynamic model of the bioprocess taking place in the bioreactor undergoes approximate linearization round temporary equilibria which are recomputed at each iteration of the control method. The linearization makes use of Taylor series expansion and of the computation of the system's Jacobian matrices. For the approximately linearized model of the bioprocess an H-infinity feedback controller is designed. The feedback gain of the controller is found from the repetitive solution of an algebraic Riccati equation, taking place at each iteration of the control method. The stability of the proposed control scheme is evaluated through Lyapunov analysis. First, it is demonstrated that the control system satisfies the H-infinity tracking performance criterion, which signifies robustness against modelling uncertainty and external perturbations. Moreover, under moderate conditions it is proven that the control loop is globally asymptotically stable. The proposed control method solves finally the nonlinear optimal control problem for bioreactors in a computational efficient and of proven convergence manner
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