  A stochastic model predictive control (MPC) framework is presented in this
paper for nonlinear affine systems with stability and feasibility guarantee. We
first introduce the concept of stochastic control Lyapunov-barrier function
(CLBF) and provide a method to construct CLBF by combining an unconstrained
control Lyapunov function (CLF) and control barrier functions. The
unconstrained CLF is obtained from its corresponding semi-linear system through
dynamic feedback linearization. Based on the constructed CLBF, we utilize
sampled-data MPC framework to deal with states and inputs constraints, and to
analyze stability of closed-loop systems. Moreover, event-triggering mechanisms
are integrated into MPC framework to improve performance during sampling
intervals. The proposed CLBF based stochastic MPC is validated via an obstacle
avoidance example.
