Abstract
We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem.
Highlights
Optimization-based controllers, in general, and model predictive control (MPC) systems, in particular, represent an extraordinary success case in the history of automation in the process industries [1]
We addressed the problem of achieving the optimal asymptotic economic performance using the economic model predictive control (EMPC) algorithms despite the presence of plant-model mismatch
After reviewing the standard techniques in offset-free tracking MPC and economic MPC, we presented an example where available MPC formulations fail in achieving the optimal asymptotic closed-loop performance
Summary
Optimization-based controllers, in general, and model predictive control (MPC) systems, in particular, represent an extraordinary success case in the history of automation in the process industries [1]. For an increasing number of applications, this separation of information and purpose is no longer optimal nor desirable [2]. An alternative to this decomposition is to take the economic objective directly as the objective function of the control system. In this approach, known as economic model predictive control (EMPC), the controller optimizes directly, in real time, the economic performance of the process, rather than tracking a setpoint
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