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As well-known, model predictive control is closely related to optimal control. This paper studies relationships between them and provides a unified framework for optimality analysis of model predictive controllers (MPC). The optimality is evaluated by comparing total performance of MPC with finite and infinite horizon optimal cost. Based on relaxed value iteration method, upper and lower bounds of...
To track the wide range of operating points of fast time-varying processes, a novel multiple model off-line predictive control algorithm is presented. The proposed method is a combination of multiple model strategy and predictive control. Firstly, we locally describe the original nonlinear system around an operating point employing linear time varying (LTV) model. Then the offline model predictive...
In this paper, a new data-driven model predictive control (MPC) is considered based on bilinear subspace identification. The system's nonlinear behavior is described with a bilinear subspace predictor structure in MPC framework. Thus, the MPC formulation results in a fixed structure objective function with constraints regardless of the underlying nonlinearity. Therefore, a bilinear predictive control...
An off-line min-max MPC strategy is proposed for constrained nonlinear systems, whose trajectories can be embedded within those of a bank of linear parameter varying models via an embedding scheme. The off-line min-max MPC algorithm is used to determine the control law for each LPV embedding model corresponding to invariant ellipsoidal sets constructed off-line. The control law which will be implemented...
In this paper, a new data-driven model predictive control (MPC) is considered based on a bilinear subspace method. Being a subclass of nonlinear systems, bilinear system is useful to approximate a class of nonlinear systems and implement predictive control in many circumstances. Therefore, a bilinear predictive control is implemented by exploiting the structural properties of the identified bilinear...
For a class of nonlinear systems described by Wiener model, the model parameter identification problem is equivalent to the nonlinear minimization problem with the estimated parameters as the optimized variables subjected to some equality and inequality constraints. Then the particle swarm optimization (PSO) algorithm is used to obtain the optimal solution to the minimization problem (i.e. the optimal...
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