Control of Inverted Pendulum system by using a new robust model predictive control strategy

Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles

“…Because the relationship between the T isg and the value of SOC has been illustrated previously, during each prediction step the parameter μ i should be optimized to satisfy the constraints according to the reference value of SOC. The load prediction can provide the P ice-dmd , which also includes the rotation speed ω according to the characteristic map of the ICE, and the voltage of the battery can be calculated through formula s (8) and (9), while μ i can be repeatedly calculated through the T isg and the initial value of the battery voltage until the voltage difference is smaller than the preset threshold δ p . As stated in formula (25), the main optimization task of the primer controller is to reduce the difference between the reference value SOC ref and the actual SOC under a certain ω dmd , since the load P ice-dmd has been obtained through the load prediction module, thus the ideal rotation speed trajectory of ω under a certain load can also been decided, and then the optimization problems of the P ice-dmd can be repeatedly calculated through formula (43) until the difference between the reference value of SOC and the actual SOC value is smaller than the preset threshold δ p .…”

“…MPC has been widely used in EMS for HEVs . On the basis of comparing three different control strategies that do not depend on future driving cycle information, a model based strategy that does not rely on prior knowledge of future driving conditions is proposed in .…”

An LMI framework to design robust MPC for a class of nonlinear uncertain systems

“…In the following some recent works are reviewed. In [1][2][3][4][5] algorithms proposed to solve state feedback robust MPC technique with polytopic uncertainties, where constraints on the control effort (input) were handled by adding another LMI to the LMI sets. In [1] the concept of asymptotically stable invariant ellipsoid and LMIs is used to develop an efficient on-line formulation of robust constrained MPC algorithm.…”

“…In [2] disturbance model was included in controllers design to enhance the robustness of MPC to achieve offset-free control. Simultaneously, some well-known applications of predictive control is successfully applied to permanentmagnet synchronous motor (PMSM) with antiwindup compensator [3], coupled tank systems [4], inverted pendulum system [5], speed control of a two-mass system [6], continuous stirred tank reactor (CSTR) problem [7][8], integrating systems at the presence of model uncertainty [9], and process with time-delay uncertainty like temperature control of a typical air-handling unit [10], based on extended Kalman filter [11] and based on recurrent neural networks [12]. It is worth mentioning that most of the works consider the linear system formulation to develop their robust model predictive control [1][2][3][4][5].…”