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In this paper, a new constrained multivariable predictive control strategy based on T-S modeling approach and Particle Swarm Optimization is considered. The proposed method determines the control actions by minimizing a nonlinear criterion based on Particle Swarm Optimization algorithm. At optimization stage, the proposed algorithm integrates the Alienor method in order to find a set of solutions...
Steady-state control error occurs in case of unmeasured disturbances or disagreement between the model and the actual behaviour of the system because, state-space controllers do not have integral character. This problem (offset-free reference tracking) of state-space controllers has been observed in both theoretical and application areas (industrial MPC Implementation IDCOM, DMC, and QDMC IDCOM-M)...
This paper investigates the performance of two Multivariable Model Predictive Control (MPC) strategies: selfish and solidary. These strategies are based on the main ideas developed in the EPSAC (Extended Prediction Self-Adaptive Control) approach to MPC. A two degree of freedom (2DOF) helicopter simulation has been chosen to illustrate these concepts, as it represents a complicated and challenging...
Teaching multivariable control usually involves a certain level of mathematical sophistication and hence requires some labaratorial exemplification of the material given in formal lectures. This paper reports on a hands-on approach to multi-variable control education via the implementation of a model predictive controller on a two-input, two output coupled drive apparatus. This scaled-down system...
This paper proposes a new mathematical method to solve min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Since the resulting optimization problem is non convex, a deterministic global optimization technique is adopted to solve it which is...
Predictive control seems to be a promising approach that can help to improve properties of existing ventilation systems applied in road tunnels. Advantages of predictive control result mainly from its ability to solve both SISO and MIMO tasks, to have regard for dynamics of process changes in a broad extent, to compensate effect of measurable and non-measurable failures and to formulate the task as...
This paper presents the empirical modeling of the gaseous pilot plant which is a kind of interacting series process with presence of nonlinearities. In this study, the discrete-time identification approach based on subspace method with N4SID algorithm is applied to construct the state space model around a given operating point, by probing the system in open-loop with variation of input signals. Three...
Unit load system of power plant is a double-input and double-output multivariable system with big inertia. The conventional control method cannot get satisfactory result. In this paper, the system is firstly decomposed into two double-input and single-output subsystems and two MAC controllers are designed accordingly. By solving the two controllerspsila equations, predictive control sequence is obtained...
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