Data-Driven Adaptive Optimal Control for Flotation Processes with Delayed Feedback and Disturbance

Considering the presence of time-delay in actuators and nonvanishing disturbance, a data-driven control technique is developed based on adaptive dynamic programming (ADP) to solve the reagents control problem for flotation processes. First, the reagents control problem is converted into an optimal regulator control problem. Second, a policy iteration (PI) algorithm has been adopted to find desirable adaptive suboptimal controllers. Unlike conventional controllers (PID, MPC) design, the proposed method can achieve the desired control performance by only employing online production data without the complete knowledge of the underlying flotation process dynamics. Specifically, the flotation indexes (tailing grade and concentrate grade) are forced to track the desired values with disturbance rejection and keep reagents consumption to a minimum. The convergence and stability of the proposed data-driven optimal control method are given, and the efficacy is validated in the simulation environment with industrial data.