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A stochastic model predictive control (MPC) framework is presented in thispaper for nonlinear affine systems with stability and feasibility guarantee. Wefirst introduce the concept of stochastic control Lyapunov-barrier function(CLBF) and provide a method to construct CLBF by combining an unconstrainedcontrol Lyapunov function (CLF) and control barrier functions. Theunconstrained CLF is obtained from its corresponding semi-linear system throughdynamic feedback linearization. Based on the constructed CLBF, we utilizesampled-data MPC framework to deal with states and inputs constraints, and toanalyze stability of closed-loop systems. Moreover, event-triggering mechanismsare integrated into MPC framework to improve performance during samplingintervals. The proposed CLBF based stochastic MPC is validated via an obstacleavoidance example.

Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems