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...

For speed sensorless induction motors under field-oriented control (FOC), where the motor speed and angle are not measured, the speed control tracking bandwidth is mainly limited by the convergence rate of the state estimator. Prevailing speed-sensorless induction motors suffer significant performance degradation from removing the encoder, which limits their applications to fields requiring low...

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...

In this paper, one presents a transient state performance optimization of vectorcontrolled induction motor (IM) drives operating in the field-weakening region. The vector control scheme, based on the rotor flux field-orientation (RFOC), is considered to ensure maximum torque motor operation regime. Although in the high speed region, the measure of rotor speed and the sensitivity to parameters e...

The paper shows the design of a robust control structure for the speed sensorless vector control of the IM, based on the mixed sensitivity linear parameter variant (LPV) H∞ theory. The controller makes possible the direct control of the flux and speed of the motor with torque adaptation in noisy environment. The whole control system is tested by intensive simulations and according to the result...

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.

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...

Due to simplicity and convenience, Model Predictive Control, which consists in optimizing future decisions based on a pessimistic deterministic forecast of the random processes, is one of the main tools for stochastic control. Yet, it suffers from a large computation time, unless the tactical horizon (i.e. the number of future time steps included in the optimization) is strongly reduced, and la...