In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be appr...

Dynamic Matrix Control (DMC) is a widely used model predictive controller (MPC) in industrial plants. The successful implementation of DMC in practical applications requires a proper tuning of the controller. The available tuning procedures are mainly based on experience and empirical results. This paper develops an analytical tool for DMC tuning. It is based on the application of Analysis of V...

Enhancing and controlling chaotic advection or chaotic mixing within liquid droplets is crucial for a variety of applications including digital microfluidic devices which use microscopic “discrete” fluid volumes (droplets) as microreactors. In this work, we consider the Stokes flow of a translating spherical liquid droplet which we perturb by imposing a time-periodic rigid-body rotation. Using ...

Automatic Tuning of Data-Intensive Analytical

Based on the energy model for disparity-tuned neurons, we calculate probability density functions of complex cell activity for random-dot stimuli. We investigate the effects of normalization and give analytical expressions for the disparity tuning curve and its variance. We show that while normalized and non-normalized complex cells have similar tuning curves, the variance is significantly lowe...

For a servo plant with an integrator, analytical tuning formulas for phase lead compensators with both gain and phase margin specifications are derived in this paper. Comparing with other tuning methods, this method is simple with a short tuning time. Simulation results under two typical cases show that this method is general, too. Copyright c ©2005 IFAC

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

Model predictive control (MPC) is an effective control strategy in the presence of system constraints. The successful implementation of MPC in practical applications requires appropriate tuning of the controller parameters. An analytical tuning strategy for MPC of first-order plus dead time (FOPDT) systems is presented when the constraints are inactive. The available tuning methods are generall...

We study the behaviour of the Ben-Yishai hypercolumn model 2] under presentation of oriented stimuli, having extended this model by including plastic aaerent (LGN to cortex) connections. We nd that Hebbian plasticity creates a self-organising map and show that constraining or modifying the standard Hebb rule in a particular way will lead to a contrast-insensitive tuning width, thus giving an ex...