Multi objective controller design for linear systems via optimal interpolation

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Ohio State University, NASA Lewis Research Center, National Technical Information Service, distributor , [Columbus, Ohio], Cleveland, Ohio, [Springfield, Va
Linear systems., Control systems design., Steady s
Other titlesMulti-objective controller design for linear systems via optimal interpolation
StatementHitay Ozbay.
Series[NASA contractor report] -- 207065, NASA contractor report -- NASA CR-207065.
ContributionsLewis Research Center., United States. National Aeronautics and Space Administration.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL17132167M

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MULTI-OBJECTIVE CONTROLLER DESIGN FOR LINEAR SYSTEMS VIA OPTIMAL INTERPOLATION SUMMARY We propose amethodology forthedesignofacontrollerwhich satisfiesasetofclosed-loop setofobjectivesconsistsof(i)poleplacement, (ii)decoupled command trackingofstep inputs atsteady-state.

This solution determines Q(O) and the remaining freedom in choosing Q(s) is used to satisfy objective (3).

We write Q(s) = (l/v(s))bar-Q(s) for a prescribed polynomial v(s). Bar-Q(s) is a polynomial matrix which is arbitrary except that Q(O) and the order of bar-Q(s) are : Hitay Ozbay. In this chapter, we present the recent results of Pareto optimal design of controls for nonlinear dynamical systems by using the advanced algorithms of multi-objective optimization.

The controls can be of linear PID type or nonlinear feedback such as sliding by: 2. Multi-objective MIMO optimal control design without zero interpolation. Abstract. In this article optimal controller designs which address the concerns of the H 2 and the l 1 norms is studied.

In the first problem a positive combination of the H 2 and the l 1 norms of the closed loop is by: 5. This paper addresses the design of Eigenstructure Assignment (EA) based Multi-Objective Dynamic State Feedback Controller (MODSFC) for linear discrete MIMO system.

In this paper, a multi-objective optimal design of delayed feedback control of an actively tuned vibration absorber for a stochastically excited linear structure is investigated. The simple cell mapping (SCM) method is used to obtain solutions of the multi-objective optimization problem (MOP).Cited by: DESIGN OF OPTIMAL LINEAR SYSTEMS BY MULTIPLE OBJECTIVES Abstract Traditional concepts of optimality focus on valuation of already given systems.

A new concept of designing optimal systems is proposed. Multi-objective linear programming (MOLP) is a model of optimizing a given system by multiple objectives. we introduce the multi-objective optimal control problem. Next, we introduce the concept of uncertainty and e ciency that we will use in this work.

Finally, we present some basic concepts of model predictive control. Multi-objective optimal control The basis for all considerations is the following general nonlinear multi-objective optimal.

Details Multi objective controller design for linear systems via optimal interpolation FB2

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. Example problems include analyzing design tradeoffs, selecting optimal product or process designs, or any other application where you need an optimal solution with tradeoffs between two or more conflicting objectives.

A typical multi-objective control problem imposes different specifications on different channels of the closed-loop system. In this paper we concentrate on those requirements that can be formulated in terms of the solvability of a linear matrix inequality (LMI).Cited by: A novel multi-objective ADP method is constructed to obtain the optimal controller of a class of nonlinear time-delay systems in this chapter.

Get this from a library. Multi objective controller design for linear systems via optimal interpolation. [Hitay Özbay; Lewis Research Center.]. ideal processor that can be used for solving multi-objective optimization problems (Deb, ).

In this paper, it is intended to apply a Multi-objective Evolutionary algorithm (MOEA) to a multipurpose reservoir operation problem.

The results obtained show the effectiveness of MOEAs for deriving optimal policies for multi-objective reser-voir. th objective in the optimal solution and q r is the proportional satisfaction amount of rth objective relative to the normalizing factor.

The objective function, maximizes multi-dimensional utility summed across all objectives. Each objective is weighted. The second equation sums the level of each objective into the variable glr. The third File Size: KB.

This example of optimal design of a paper mill is a simplification of the model used in. Multi-objective design optimization have also been implemented in engineering systems in circumstances such as control cabinet layout optimization, airfoil shape optimization using scientific workflows, design of nano-CMOS semiconductors, system on chip.

(Multi-objective) Optimal control. AMIGO2 solves optimal control problems with flexibility in the objective functional, stimuli interpolation, and path and point constraints. The aim is to find time varying stimulation conditions to maximize or minimize a given objective related to cell performance or to a desired by: The large phase angle allowance design method of multi-objective optimal control system and a first class new type non-linear controller are presented.

Optimizing the liner optimal system by means of these results, a multi-objective optimal system with performance better than linear optimal system can by obtained.

In this paper, an example of radar servo system designed.

Description Multi objective controller design for linear systems via optimal interpolation EPUB

From a theoretical point of view, it can be formulated as one of the functions of multi-objective Optimal Control Theory. This paper presents a new multi-objective optimization method for an ATO system using Cellular Automata (CA). A CA model for an ATO system is applied to simulate train operation.

An optimal method for ATO is by: 7. Unlike the single optimization methods which return only a single solution, the multi-objective optimization algorithms return a set of solutions called the Pareto set and a set of the corresponding objective function values called the Pareto this thesis, we present a multi-objective optimal (MOO) design of linear and nonlinear control systems using two Cited by: 2.

This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world.

is quasi-convex and can be solved by similar techniques. It owes its name to the fact that is related to the largest generalized eigenvalue of the pencil (A (x), B (x)).Many control problems and design specifications have LMI is especially true for Lyapunov-based analysis and design, but also for optimal LQG control, H ∞ control, covariance control, etc.

This paper describes an interpolation algorithm in the multi-axis motion control system, which can achieve six-axis interpolation operations, greatly improving the processing efficiency. Using modular design idea on the Quartus II platform, by DDA interpolation theory, interpolation modules are built through VHDL.

And these interpolator modules are connected into Author: Hai Ming Shen, Kun Qi Wang, Yong You Tian. Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations.

Symmetric matrices, matrix norm and singular value decomposition. () Multi-objective controller design for discrete stochastic optimal systems with non-linear time-varying unmodelled dynamics and noise spectral uncertainties.

International Journal of Systems ScienceCited by: Model predictive control is powerful technique for optimizing the performance of constrained systems.

Constraints are present in all control systems due to physical, environmental and economic limits on plant operation, and the systematic handling of constraints provided by predictive control strategies allows for significant improvements in performance over conventional control.

Multi-objective Control. We have developed a new approach for solving multi-objective problems. This general approach makes it possible to simultaneously incorporate performance objectives stated in terms of weighted l1, H2, or H-infinity norm constraints while allowing for constraints on the response of a known fixed input.

The varying. 3 Quadratic Programming 1 2x TQx+q⊤x → min s.t. Ax = a Bx ≤ b x ≥ u x ≤ v (QP) Here the objective function f(x) = 12x⊤Qx+ q⊤xis a quadratic function, while the feasible set M= {x∈Rn |Ax= a,Bx≤b,u≤x≤v}is defined using linear functions.

One of the well known practical models of quadratic optimization problems is the least squares ap. optimal design of a multi-state weighted series-parallel system using physical programming and genetic algorithms 20 November | Asia-Pacific Journal of Operational Research, Vol.

28, No. 04 Preference-based maintenance planning for deteriorating bridges under multi-objective optimisation frameworkCited by: Local stability proof & controller design using linear methods ⇒ Global approach necessary (due to PID Controller Design for Nonlinear Systems 11/ Motivation PID Controller Design Feedforward Control Conclusion & Outlook Multi-objective optimisation of controller parameters considering.

() Design for aircraft engine multi-objective controllers with switching characteristics. Chinese Journal of Aeronautics() Optimal controller synthesis for a class of LTI systems via switched by:. Mathematical optimization (alternatively spelt optimisation) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives.

Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods .multi-objective optimal feedback control design. International Journal of Dynamics and Control, 1(3){, [11]L. Gru ne and M.

Stieler. Performance Guarantees for Multiobjective Model Predictive Control. In IEEE 56th Annual Conference on Decision and Control (CDC), pages {,   This so called SLC (Structured Linear Control) problem can be formulated with linear matrix inequalities (LMI’s) with a nonconvex equality constraint.

This class of prolems includes fixed order output feedback control, multi-objective controller design, decentralized controller design, joint plant and controller design, and other interesting Cited by: 5.