Model predictive control has proved its effectiveness in many industrial applications. Predictive algorithms are available in various commercial control packages but their implementation costs could be considerable. Control of industrial processes is often realized using small industrial controllers that offer only simple control structures, like PID control loop. The aim of the paper is to der...

MBPC is a feedback-control methodology suitable to enforce efficiently hard constraints on the variables of the controlled system. It is shown that the method hinges upon a constrained open-loop optimal control problem along with the adoption of the so-called receding-horizon control strategy. In the important case of time-invariant linear saturated ANCBI systems, MBPC algorithms can be devised...

Model predictive control, MPC, is a model-based control philosophy that select control actions by on-line optimization of objective functions. Design methods based on MPC have found wide acceptance in industrial process control applications, and have been thoroughly studied by the academia. Most of the work so far have relied on linear models of different sophistication because of their advanta...

Most academic control schemes for MIMO systems assume all the control variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper s...

Animate vision systems, biological or robotic, employ gaze control systems to acquire, fixate, and stabilize images. Our goal is to guild robust gaze control behaviour from cooperating lower-level visual reflexes. Predictive control strategies can cope with time delayed, multi-rate, and interacting controls. Solutions are explored through simulation incorporating ten primitive gaze control capa...

Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems. We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nominal setting.