Performance assessment of MIMO systems under partial information

Minimum variance (MV) can characterize the most fundamental performance limitation of a system, owing to the existence of time-delays/infinite zeros. It has been widely used as a benchmark to assess the regulatory performance of control loops. For a SISO system, this benchmark can be estimated given the information of the system time delay. In order to compute the MIMO MV benchmark, the interactor matrix associated with the plant may be needed. However, the computation of the interactor matrix requires the knowledge of Markov parameter matrices of the plant, which is rather demanding for assessment purposes only. In this paper, we propose an upper bound of the MIMO MV benchmark which can be computed with the knowledge of the interactor matrix order. If the time delays between the inputs and outputs are known, a lower bound of the MIMO MV benchmark can also be determined

Non-linear predictive GMV control

A nonlinear predictive generalized minimum variance (NPGMV) control algorithm is introduced for the control of nonlinear multivariable systems. The plant model is represented by a series combination of a nonlinear operator, which is assumed finite-gain stable, and a linear state-space model, which can include time delays and unstable modes. The main contribution is to incorporate predictive action into the recently introduced Nonlinear GMV controller by defining a multi-step cost index and using a minimum-variance form of the usual GPC cost function. The solution is very different to traditional nonlinear model predictive control, providing a solution which is similar to fixed model based controllers. This does not provide the same constrained optimization features but it does give a controller which is very simple to implement

Supervisory multiple-model approach to multivariable lambda and torque control of sI engines

The problem of simultaneous air-fuel ratio regulation and torque tracking in a spark ignition engine with electronic throttle control is considered. The proposed methodology involves the use of a set of piecewise-affine models to represent the nonlinear engine dynamics. These models are the basis of a supervisory multiple-model control scheme, which, in its simplest form, consists in switching among the predefined bank of controllers. In the following a monitoring signal generator is driven by a bank of observers, and a supervisor ensures the robustness of the switching scheme. An optimal linear-quadratic cost function enables the trade-off between emissions performance and drivability to be adjusted. Simulation results using the data obtained from a vehicle with a 5.3L V8 engine on Federal Test Procedure (FTP) driving cycles are presented, with a nonlinear regression model of the engine identified from the FTP data. The results indicate that both tight lambda regulation and fast torque tracking are possible using the proposed designs