A Low Computational Cost, Prioritized, Multi-Objective Optimization Procedure for Predictive Control Towards Cyber Physical Systems

Abstract

Cyber physical systems consist of heterogeneous elements with multiple dynamic features. Consequently, multiple objectives in the optimality of the overall system may be relevant at various times or during certain context conditions. Low cost, efficient implementations of such multi-objective optimization procedures are necessary when dealing with complex systems with interactions. This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics. The methodology is used in the context of an Extended Prediction self-adaptive Control (EPSAC) strategy with prioritized objectives. The analysis indicates that the proposed algorithm is significantly lighter in terms of computational time. The combination with an input-output formulation for predictive control makes these algorithms suitable for implementation with standardized process control units. Three simulation examples from different application fields indicate the relevance and feasibility of the proposed algorithm.INDEX TERMS priority objectives, multi-objective optimization, model predictive control, steam power plant, unmanned aerial vehicle, drug regulatory network, interaction, safety.

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

“…It might be interesting to consider a prioritized version, in which objectives are given different levels of priority as a function of the current patient state. Such multi-objective prioritized methodology has been proposed in [74], with initial results for the anesthetichemodynamic system.…”

“…We have a long-standing tradition of applying predictive control to various dynamic states in the anesthetic, which has been also used in clinical practice [75], and hemodynamic regulation problem. The used predictive control methodology has been tailored from the generic EPSAC (Extended Predictive Self-Adaptive Control) algorithm given in [28] for SISO systems, adaptive in [76] and multi-objective prioritized optimization in [74]. In this section only the essential steps in obtaining the closed loop control results given in this paper are presented.…”

Ergonomic and Economic Office Light Level Control

“…Multi-objective distributed control has a generic baseline structure that consists of three sequential layers: safety, tracking performance, and energy [25,26]. Obviously, the highest priority is assigned to safety.…”

PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

“…It is necessary to obtain an expression that relates the value of m OF with γ. This is done using the value m OFi found in the linear Equations (28)- (32) for different values of γ. So, for 0 ≤ γ ≤ 4 we get the plot shown in Figure 3.…”

“…Internal Model Control (IMC) has been used as an alternative for designing and tuning PID controllers since 1980s [30]. It has resulted to be of particular interest in industry together with the PID algorithm [32], since the equations for the controller's parameters can be obtained from the transfer function of the process and the desired behavior of the closed-loop response; in most cases, only the closed-loop time constant is required as the user-defined tuning parameter, considering an appropriate trade-off between performance and robustness [20,[33][34][35][36][37]. Additional works, regarding IMC, that have been developed more recently can be found in [38][39][40][41][42][43][44].…”