Optimal and Robust Tuning of State Feedback Controller for Rotary Inverted Pendulum

Results on the design of robust memoryless state feedback controllers for uncertain time-delay systems with norm bounded uncertainty are presented. It is proved that the feasibility of a linear matrix inequality (LMI) problem is necessary and sufficient for the quadratic stabilisation of an uncertain time-delay system. A robust state feedback controller can be constructed using the corresponding feasible solution of the LMI problem. A procedure is given to select a suitable state feedback controller that is also suboptimal in the sense of minimising a bound on a quadratic performance index.

This paper deals with the design of robust state feedback controller based on fuzzy logic and its evaluation in adjusting the speed of an induction machine. The basic idea of the proposed controller is similar to the gain scheduling approach. The design is based on a reduced order state space model of the motor drive from which a family of local state space models covering the operating range of the drive system are defined. We then use the state feedback design concept to get a linear state feedback controller for each local model using pole placement topology. The performance of the fuzzy state feedback controller is evaluated under different operating regimes of the drive system.

A vector decomposition approach to F–M model realization and its application to robust observed-state feedback control

Decentralized robust guaranteed cost control for a class of interconnected singular large-scale systems with time-delay and parameter uncertainty

Novel direct adaptive robust state and output feedback controllers are presented for the output tracking control of a class of nonlinear systems with unknown system dynamics and disturbances. Both controllers employ a variable-structure radial basis function (RBF) network that can determine its structure dynamically to approximate unknown system dynamics. Radial basis functions are added or removed online in order to achieve the desired tracking accuracy and prevent to network redundancy. The raised-cosine RBF is employed to enable fast and efficient training and output evaluation of the RBF network. The direct adaptive robust output feedback controller is constructed by utilizing a high-gain observer to estimate the tracking error for the controller implementation. The closed-loop systems driven by the variable neural direct adaptive robust controllers are actually switched systems.

This paper presents a method of designing robust optimal controllers for digital control systems with control delay. A predictor similar to the Smith one is used. The robust optimal state feedback control law is obtained by dynamic programming method. The uncertainty in system parameters is taken care of by an additional term in the Riccati equation. The case of output feedback controller is also dicussed.

This paper describes a procedure for multivariable state feedback robust controller design. The plant in state space is given by the operational point description or in terms of a vector of slow varying physical parameters. Through solution of the Sylvester matrix equation, a nonunique static feedback controller, which assigns the prespecified closed-loop spectrum, is calculated. In addition, all the remaining feedback degrees of freedom are utilized to optimize a multiobjective function that reflects further design properties. The robust feedback gains is calculated through a three-phase computational algorithm. Numerical examples show that under the robust state feedback control, the closed-loop systems can both achieve satisfied transient characteristics and greatly reduce state trajectory sensitivity to small or large parameter variations in the plant. The pro-posed procedure is still applied to a VTOL aircraft model.

Wind energy is currently the fastest growing electricity source. To meet the output demand, wind turbines are becoming larger and more flexible leading to the problems of structural load especially in case of offshore turbines. Advanced control algorithms are developed to reduce the load, allowing to build larger turbines, and expand their lifetime. Observer-based control algorithms such as Linear-Quadratic-Gaussian LQG control which uses LQR to calculate the optimal observer and controller gains are commonly applied to wind turbines in literature. However the approach requires to calculate the observer and control gains separately. In addition, linear models used for parameter calculation may have errors with respect to the nonlinearities of wind turbines and induced to unmodeled dynamical properties. These modeling errors need to be considered to to guarantee the stability of the controlled system. Alternatively a robust design assuming bounds and limits of models have to be realized to guarantee stability while ignoring details of modeling. This paper proposes an optimal robust observer-based state feedback controller for large-scale wind turbines to realize multi objectives, including structural load mitigation and rotor speed regulation. The novel contribution is that the observer gain parameters, control gains, and integral action are optimized at the same time within H∞ mixed sensitivity framework to achieve desired performance with respect to power regulation, structural load mitigation, and also robustness for the wind turbine control system. The control performances have been verified by a high fidelity simulation software and are compared to those of a classical baseline controller.

In this paper, we study stability and performance properties of linear fractional transformation (LFT) parameter-dependent systems using duality theory and tools from convex analysis. A pair of conjugate functions, the convex hull and the maximum of a family of quadratic functions, are used for analysis and synthesis of LFT systems. Sufficient synthesis conditions for both robust state feedback and gain-scheduling output feedback control problems are formulated as a set of linear matrix inequalities (LMIs) with linear search over scalar variables. Finally, a numerical example is used to demonstrate the advantages of the proposed approaches.

Robust guaranteed cost control of uncertain fuzzy systems under time-varying sampling

The problem of designing robust non-fragile state feedback controllers for linear discrete systems with uncertainties and constant delays is studied. The system uncertainties are assumed to be polytopic, and the state feedback gain uncertainties are norm-bounded. The H/sub /spl infin// norm is used to describe system performance. A method of designing robust non-fragile H/sub /spl infin// controllers is proposed in terms of linear matrix inequalities (LMIs). Numerical examples are given to illustrate the proposed results.

This paper focuses on the synchronization of uncertain coronary artery chaos systems (CACSs) with the state and input time-varying delays under input saturation. First, the stability of the closed-loop system is established to ensure the synchronization between the diseased and healthy CACSs under input saturation by utilizing the local sector condition and the Lyapunov–Krasovskii functional (LKF) method. The use of Wirtinger inequality and the reciprocally convex method further reduces the conservatism. Second, by making use of the cone complementary linearization approach to deal with nonlinear terms, we obtained a state feedback controller. Finally, a robust state feedback controller is formulated to achieve the synchronization of the CACS under disturbances bounded by $L_{2}$ norm. The simulation results of the CACS synchronization are presented to demonstrate the effectiveness of the derived results, which provides a certain theoretical basis for curing diseases related to the coronary artery vessel.

The paper shows that a control strategy with disturbance rejection is able to reduce the control effort to a minimum, ensuring at the same time a desired performance level. The disturbance to be rejected is completely unknown, except for a sectorial bound. The control unit is endowed with an extended state observer which includes a disturbance dynamics, whose state tracks the unknown disturbance to be rejected. In summary, the novel contributions of the paper are the following. First, we derive a robust stability condition for the proposed control scheme, holding for all the nonlinearities that are bounded by a known (or estimated) maximum slope. Second, we propose a novel approach for designing the observer and state feedback gains, which guarantee robust closed-loop stability. Third, we show that the designed control system yields, with a minimum control effort, the same control performance as a robust state feedback control, which on the contrary may require a larger command activity. Two simulated case studies are presented to show the effectiveness of the proposed approach.

Optimal fuzzy robust state feedback control for a five DOF active suspension system

The deployment of Autonomous Underwater Vehicles (AUVs) for ocean observation is one of the important methods in marine scientific research. The stability of AUV attitude control determines the efficiency and safety of underwater operations. In practical systems, the actuators carried by AUVs have saturation, and the saturation of the actuators greatly affects the performance and stability of AUV attitude control. This article designs an anti-windup [Formula: see text] robust state feedback controller and an anti-windup [Formula: see text] robust dynamic output feedback controller for the pitch attitude control of AUVs to solve the pitch attitude control problem of AUVs subject to input constraints. Firstly, building a Linear-parameter varying (LPV) model for the AUV. Then, we design two anti-windup [Formula: see text] robust controllers based on LPV model. Simulation results verify the reasonable of LPV model and the effectiveness of proposed controllers. Proposed controllers can enable the system to quickly reach the desired attitude under input constraints.