Mixed Control of MIMO Systems via Convex Optimization
نویسندگان
چکیده
Mixed performance control problems have been the object of much attention lately. These problems allow for capturing different performance specifications without resorting to approximations or the use of weighting functions, thus eliminating the need for trial-and-error-type iterations. In this paper we present a methodology for designing mixed l1=H1 controllers for MIMO systems. These controllers allow for minimizing the worst case peak output due to persistent disturbances, while at the same time satisfying anH1-norm constraint upon a given closedloop transfer function. Therefore, they are of particular interest for applications dealing with multiple performance specifications given in terms of the worst case peak values, both in the time and frequency domains. The main results of the paper show that 1) contrary to the H2=H1 case, the l1=H1 problem admits a solution in l1, and 2) rational suboptimal controllers can be obtained by solving a sequence of problems, each one consisting of a finite-dimensional convex optimization and a four-block H1 problem. Moreover, this sequence of controllers converges in the l1 topology to an optimum.
منابع مشابه
H∞ smith predictor design for time-delayed MIMO systems via convex optimization
A new method for robust fixed-order H∞ controller design for uncertain time-delayed MIMO systems is presented. It is shown that the H∞ robust performance condition can be represented by a set of convex constraints with respect to the parameters of a linearly parameterized primary controller in the Smith predictor structure. Therefore, the parameters of the primary controller can be obtained by ...
متن کاملExponential Estimates Of A Class Of Time-Delay Nonlinear Systems With Convex Representations
This work introduces a novel approach to stability and stabilization of nonlinear systems with delayed multivariable inputs; it provides exponential estimates as well as a guaranteed cost of the system solutions. The result is based on an exact convex representation of the nonlinear system which allows a Lyapunov–Krasovskii functional to be applied in order to obtain sufficient conditions in th...
متن کاملRobust Linear MIMO in the Downlink: A Worst-Case Optimization with Ellipsoidal Uncertainty Regions
This paper addresses the joint robust power control and beamforming design of a linear multiuser multiple-input multiple-output (MIMO) antenna system in the downlink where users are subjected to individual signal-to-interference-plus-noise ratio (SINR) requirements, and the channel state information at the transmitter (CSIT) with its uncertainty characterized by an ellipsoidal region. The objec...
متن کاملReceive antenna selection in diversely polarized MIMO transmissions with convex optimization
In this paper, we present a low complexity approach to receive antenna selection for capacity maximization, based on the theory of convex optimization. By relaxing the antenna selection variables from discrete to continuous, we arrive at a convex optimization problem. We show via extensive Monte-Carlo simulations that the proposed algorithm provides performance very close to that of optimal sel...
متن کاملConvex optimization theory applied to joint beamforming design in multicarrier MIMO channels
This paper addresses the joint design of transmit and receive beamvectors for a multicarrier MIMO channel within the general and powerful framework of convex optimization theory. From this perspective, a great span of design criteria can be easily accommodated and efficiently solved even though closedform expressions may not be available. Among other criteria, we consider the minimization of th...
متن کامل