Ali Khudhair Al-Jiboory

Guaranteed Performance State-Feedback Gain-Scheduling Control With Uncertain Scheduling Parameters

State-feedback gain-scheduling controller synthesis with guaranteed performance is considered in this brief. Practical assumption has been considered in the sense that scheduling parameters are assumed to be uncertain. The contribution of this paper is the characterization of the control synthesis that parameterized linear matrix inequalities (PLMIs) to synthesize robust gain-scheduling controllers. Additive uncertainty model has been used to model measurement noise of the scheduling parameters. The resulting controllers not only ensure robustness against scheduling parameters uncertainties but also guarantee closed-loop performance in terms of H2 and H1 performances as well. Numerical examples and simulations are presented to illustrate the effectiveness of the synthesized controller. Compared to other control design methods from literature, the synthesized controllers achieve less conservative results as measurement noise increases. [DOI: 10.1115/1.4031727]

LPV modeling of a flexible wing aircraft using modal alignment and adaptive gridding methods

One of the earliest approaches in gain-scheduling control is the gridding based approach, in which a set of local linear time-invariant models are obtained at various gridded points corresponding to the varying parameters within the flight envelop. In order to ensure smooth and effective Linear Parameter-Varying control, aligning all the flexible modes within each local model and maintaining small number of representative local models over the gridded parameter space are crucial. In addition, since the flexible structural models tend to have large dimensions, a tractable model reduction process is necessary. In this paper, the notion of σ-shifted [Formula: see text]- and [Formula: see text]-norm are introduced and used as a metric to measure the model mismatch. A new modal alignment algorithm is developed which utilizes the defined metric for aligning all the local models over the entire gridded parameter space. Furthermore, an Adaptive Grid Step Size Determination algorithm is developed to minimize the number of local models required to represent the gridded parameter space. For model reduction, we propose to utilize the concept of Composite Modal Cost Analysis, through which the collective contribution of each flexible mode is computed and ranked. Therefore, a reduced-order model is constructed by retaining only those modes with significant contribution. The NASA Generic Transport Model operating at various flight speeds is studied for verification purpose, and the analysis and simulation results demonstrate the effectiveness of the proposed modeling approach.

Linear Matrix Inequalities Approach to Input Covariance Constraint Control With Application to Electronic Throttle

In this paper, the input covariance constraint (ICC) control problem is solved by convex optimization subject to linear matrix inequalities (LMIs) constraints. The ICC control problem is an optimal control problem that is concerned to obtain the best output performance subject to multiple constraints on the input covariance matrices. The contribution of this paper is the characterization of the control synthesis LMIs used to solve the ICC control problem. Both continuousand discrete-time problems are considered. To validate our scheme in real-world systems, ICC control based on convex optimization approach was used to control the position of an electronic throttle plate. The controller performance compared experimentally with a well-tuned base-line proportional-integralderivative (PID) controller. Comparison results showed that not only better performance has been achieved but also the required control energy for the ICC controller is lower than that of the base-line controller. [DOI: 10.1115/1.4030525]

LMI Control Design With Input Covariance Constraint for a Tensegrity Simplex Structure

The Input Covariance Constraint (ICC) control problem is an optimal control problem that minimizes the trace of a weighted output covariance matrix subject to multiple constraints on the input (control) covariance matrix. ICC control design using the Linear Matrix Inequality (LMI) approach was proposed and applied to a tensegrity simplex structure in this paper. Since it has been demonstrated that the system control variances are directly associated with the actuator sizes for a given set of ℒ2 disturbances, the tensegrity simplex design example is used to demonstrate the capability of using the ICC controller to optimize the system performance in the sense of output covariance with a given set of actuator constraints. The ICC control design was compared with two other control design approaches, pole placement and Output Covariance Constraint (OCC) control designs. Simulation results show that the proposed ICC controllers optimize the system performance (the trace of a weighted output covariance matrix) for the given control covariance constraints whereas the other two control design methods cannot guarantee the feasibility of the designed controllers. Both, state feedback and full-order dynamic output feedback controllers have been considered in this work.Copyright

Improved synthesis conditions for mixed H2/H∞ gain-scheduling control subject to uncertain scheduling parameters

ABSTRACT The vast majority of the existing work in gain-scheduling (GS) control literature assumes perfect knowledge of scheduling parameters. Generally, this assumption is not realistic since for practical control applications measurement noises are unavoidable. In this paper, novel synthesis conditions are derived to synthesise robust GS controllers with mixed performance subject to uncertain scheduling parameters. The conditions are formulated in terms of parameterised bilinear matrix inequalities (PBMIs) that depend on varying parameters inside multi-simplex domain. The conditions provide practical GS controllers independent of the derivatives of scheduling parameters. That is, the designed controllers are feasible for implementation. Since bilinear matrix inequality problems are intractable, an iterative PBMI algorithm is developed to solve the developed synthesis conditions. By the virtue of this algorithm, conservativeness reduction is achieved with few iterations. Examples are presented to illustrate the effectiveness of the developed conditions. Compared to other design methods from literature, the developed conditions achieve better performance.

Experimental Study on an Electric Variable Valve Timing Actuator: Linear Parameter Varying Modeling and Control

This paper presents experimental investigation results of an electric variable valve timing (EVVT) actuator using linear parameter varying (LPV) system identification and control. For the LPV system identification, a number of local system identification tests were carried out to obtain a family of linear time-invariant (LTI) models at fixed engine speed and battery voltage. Using engine speed and battery voltage as time-varying scheduling parameters, the family of local LTI models is translated into a single LPV model. Then, a robust gain-scheduling (RGS) dynamic output-feedback (DOF) controller with guaranteed H1 performance was synthesized and validated experimentally. In contrast to the vast majority of gain-scheduling literature, scheduling parameters are assumed to be polluted by measurement noises and the engine speed and battery voltage are modeled as noisy scheduling parameters. Experimental and simulation results show the effectiveness of the developed approach. [DOI: 10.1115/1.4036539]

Static output-feedback robust gain-scheduling control with guaranteed H2 performance

Abstract This paper develops synthesis conditions for Static Output-Feedback (SOF) Gain-Scheduling (GS) control with guaranteed upper bound of H 2 performance for continuous-time Linear Parameter Varying (LPV) systems, where measurements of scheduling parameters are affected by uncertainties or measurement noise. The control problem is solved through an iterative two-stage design procedure. In the first stage, parameter-dependent state-feedback controller is obtained to minimize upper-bound of the H 2 performance. Then, this controller is used as input to the second stage to synthesize Robust Gain-Scheduling (RGS) static output-feedback gain with minimal H 2 performance. In both stages, the synthesis conditions are given in terms of Parametrized Linear Matrix Inequalities (PLMIs). Robust SOF (parameter-independent) controller can be synthesized as special case of the developed synthesis conditions. Two examples have been presented to illustrate the benefit of the proposed approach. One is an academic numerical example from literature and the other one is a realistic LPV model of Electric Variable Valve Timing (EVVT) actuator for automotive engines that utilizes engine speed and vehicle battery voltage as time-varying scheduling parameters.

LPV Modeling and Control for Active Flutter Suppression of a Smart Airfoil

In this paper, a novel technique of linear parameter varying (LPV) modeling and control of a smart airfoil for active flutter suppression is proposed, where the smart airfoil has a groove along its chord and contains a moving mass that is used to control the airfoil pitching and plunging motions. The new LPV modeling technique is proposed that uses mass position as a scheduling parameter to describe the physical constraint of the moving mass, in addition the hard constraint at the boundaries is realized by proper selection of the parameter varying function. Therefore, the position of the moving mass and the free stream airspeed are considered the scheduling parameters in the study. A state-feedback based LPV gain-scheduling controller with guaranteed H∞ performance is presented by utilizing the dynamics of the moving mass as scheduling parameter at a given airspeed. The numerical simulations demonstrate the effectiveness of the proposed LPV control architecture by significantly improving the performance while reducing the control effort.

Switching State-Feedback LPV control with uncertain scheduling parameters

This paper presents a new method to design Robust Switching State-Feedback Gain-Scheduling (RSSFGS) controllers for Linear Parameter Varying (LPV) systems with uncertain scheduling parameters. The domain of scheduling parameters are divided into several overlapped subregions to undergo hysteresis switching among a family of simultaneously designed LPV controllers over the corresponding subregion with the guaranteed ℋ∞ performance. The synthesis conditions are given in terms of Parameterized Linear Matrix Inequalities that guarantee both stability and performance at each subregion and associated switching surfaces. The switching stability is ensured by descent parameter-dependent Lyapunov function on switching surfaces. By solving the optimization problem, RSSFGS controller can be obtained for each subregion. A numerical example is given to illustrate the effectiveness of the proposed approach over the non-switching controllers.

Robust Gain-Scheduling observers for continuous-time Linear Parameter-Varying systems

In this paper, the problem of designing Robust Gain-Scheduling observers for continuous-time Linear Parameter-Varying systems via parameter-dependent Lyapunov function is addressed. The scheduling parameters are assumed to be imprecisely measured, i.e., corrupted with additive noise. Multi-simplex modeling approach is utilized to model the time-varying parameters and associated uncertainties. Sufficient conditions are given for the synthesis problem in terms of Parameterized Linear Matrix Inequalities. The designed observer not only guarantees asymptotic stability of estimation error dynamics but also minimizes the estimation errors under exogenous disturbances. Both, ℋ2 and ℋ∞ performances have been considered. An illustrative numerical example is presented to demonstrate the benefit of the developed approach.