Sliding mode control on receding horizon: Practical control design and application

Sliding mode control (SMC) is to keep the system to a stable differential manifold. Model predictive control (MPC) calculates the control input by solving an optimization problem on receding horizon. The method of receding horizon sliding control (RHSC) includes the predicted information into the SMC design by combining SMC and MPC. Considering the modeling error and measurement noise, there are model-mismatch and disturbance problems in control practice. This paper combines the demonstrated method of RHSC with a state-augmented Kalman filter addressing the model mismatch and disturbance problem. The proposed scheme has been applied to the air system of an advanced heavy-duty engine. The results have shown the capability of tracking the reference signal during a step-response test and the convergence rate to the target signal is 10% faster than MPC.

Automated summary

This paper combines receding horizon sliding control (RHSC) with a state-augmented Kalman filter to address model mismatch and disturbance problems, showing faster convergence rate than MPC in tracking reference signal in an advanced heavy-duty engine air system.