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A novel model based local ramp metering method is presented in the paper by means of incident scheduled freeway traffic control solution. First, second order macroscopic freeway model is used with appropriate incident parametrization to describe eventual and unattended traffic variation caused by off-nominal traffic conditions (e.g. accidents). These traffic anomalies are captured by adequate model parameters, i.e. incident parameters that can be on-line estimated. The paper is motivated by incident scheduled ramp-meter solution to encounter real-time incident parameter information. The main idea is to use local freeway control solution triggered by available incident parameter values. The proposed approach is local in the sense of considering only non-coordinated ramp meter solution, first. Furthermore, we apply locally optimal (linearized) control solution to satisfy throughput maximization objective. The formal controller synthesis involves parameters to correct, compensate the effect of incidents. The proposed method is evaluated and compared to other existing approach by using simulation environment.

Similarity state transformations between equivalent State-Space (SS) representations of discrete-time Linear Parameter-Varying (LPV) systems are investigated. Based on previous results, it is known that to characterize all equivalent LPV-SS representations, the statetransformation matrix must depend dynamically on the scheduling variable. However, preserving static dependence of a LPV-SS representation, i.e. characterizing all equivalent SS representation with static dependence, has primary importance both in control and identication. Therefore, rst, the state transformation problem is investigated from an algebraic (behavior) point of view, then conditions are developed to guarantee preservation of the static dependence in similarity state transformations. Additional geometric interpretation of the obtained results, together with computational approach to synthesize state-transformations are also developed. An illustrative example is provided to demonstrate the validity of the obtained results.

The paper investigates the problem of uncertainty modeling and constrained robust control of urban traffic. Linear polytopic approach is used by state-space representations to describe the uncertain network system. In order to handle model mismatches, robust and infinite horizon model predictive control (MPC) method is proposed. The control strategy is an efficient method to reduce travel time and improve homogeneous traffic flow under changing model conditions. Centralized numerical solution has been carried out as a solution of Linear Matrix Inequalities (LMI) by using semidefinite programming (SDP). Closed-loop control results were tested in simulation environment taking alternative model uncertainty levels into account.

Using induced L2-norm minimization, a robust controller was developed for insulin delivery in Type I diabetic patients. The high-complexity nonlinear diabetic patient Sorensen-model was considered and Linear Parameter Varying methodology was used to develop open loop model and robust controller. Considering the normoglycemic set point (81.1 mg/dL), a polytopic set was created over the physiologic boundaries of the glucose-insulin interaction of the Sorensen-model. In this way, Linear Parameter Varying model formalism was defined. The robust control was developed considering input and output multiplicative uncertainties with two additional uncertainties from those used in the literature: sensor noise and worst case design for meal disturbance (60 g carbohydrate).

In this paper, we present the application of a novel Linear Parameter-Varying (LPV) system identification approach on a DC motor setup with an unbalanced disc. This setup is used as a real-life application of the closed-loop LPV subspace identification technique described in van Wingerden and Verhaegen [2009], this work demonstrates the identification procedure and the effectiveness of the algorithm in identifying nonlinear models using real data.

This paper presents a new Fault detection and Identification approach Connected to Subspace Identification (FICSI) for a special class of nonlinear system casted to Linear but Parametrically Varying (LPV) form. The model based algorithm utilizes the specific nonlinear nature of the LPV system along the past horizon to construct the output predictor. Similar with its LTI counterpart in a companion paper ([Dong and Verhaegen (2008)]), the FICSI-LPV avoids projecting the residual vector onto the parity space of the extended observability matrix, and hence produces residuals more sensitive to faults than the parity space approach (PSA) for LPV systems does. Asymptotically unbiased condition and algorithm are also proposed for fault estimation. The difference of FICSI from the existing fault detection and estimation approaches based on PSA or moving horizon estimation (MHE) can also be attributed to the fact that FICSI does not require an LPV state space model, but a sequence of Markov parameters mapping the I/O measurements and the scheduling parameters to the residual, which can be estimated in closed-loop.

The paper investigates the reconfigurable features of the Subspace Predictive Control scheme for Linear Parameter Varying Systems (SPC LPV). This model independent predictive approach combines the predictor based subspace identification and (constrained) predictive control for affine LPV systems. By means of using recursive update of the identified closed loop Markov parameters, the SPC LPV can be used for Fault Tolerant Control. The proposed algorithm is implemented and applied in a rotation speed tracking for a nonlinear DC motor.

The paper presents a method for the constrained Moving Horizon Estimation of origin-destination (OD) matrices for traffic networks. In traffic systems split ratios are important parameters for OD estimation and for control problems as well. The paper treats the general constrained Moving Horizon Estimation problem for traffic systems modelled in terms of linear time varying system and solves the split rate estimation process. The estimation is subjected to equality and inequality constraints. A numerical example is solved to demonstrate the Moving Horizon Estimation of split variables.

The paper examines the conditions for isolating the unknown input detection from the effects of the measurements noise in the important family of positional control problems. The study is motivated by the need of improving the homing performance of interceptor missiles against randomly maneuvering targets. The required isolation is possible if, in addition to noisy relative position measurements, noise-free measurement of the line of slight rate or the relative lateral velocity is available. Although with noisy measurement of line of slight rate the isolation of the input is not possible, the detection filter designed for the noise-free case succeeds to provide a satisfactory estimate of target acceleration, which is needed for an improved homing performance.

In this article the disturbance attenuation problem arising in Failure Detection and Identification (FDI) using detection filters is discussed. The filter is designed using the general Fundamental Problem of Residual Generator(FPRG) algorithm and it is assumed that decoupling the failure modes from the deterministic disturbance is not possible. The disturbance attenuation is achieved by applying H ∞ filtering concepts. Numerical example is shown to demonstrate the effect, of the worst case disturbance rejection on residuals that are sensitive to failure directions.