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For the chaotic systems with uncertain parameters and stochastic disturbance, in order to satisfy some optimal performance index when chaos control is achieved, the Lyapunov-based model predictive control (LMPC) is introduced. The LMPC scheme is concerned with an auxiliary controller which is constructed in advance. Based on the auxiliary controller and stochastic stability theory, it is shown that...
A recent lifting technique led to a computationally efficient Model Predictive Control (MPC) strategy in which the online optimization is performed using a univariate Newton-Raphson procedure. However this procedure solves a dual problem, preventing the use of warm starting or premature termination. By solving a primal problem, the current paper proposes a strategy that is more efficient than the...
A recent paper proposed an MPC methodology which achieved a considerable reduction in the online optimization by transferring some of the computational load to calculations that can be performed offline. The approach was based on an augmented autonomous state space formulations of the prediction dynamics and gained significantly in efficiency by imposing a terminal constraint at current time. The...
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