# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 2 MPC Implementation
### Other MPCs  
* **Decentralized and distributed MPC:** Each controller in a decentralised and/or distributed control system simply monitors and regulates local outputs and inputs. Decentralization has profound benefits for controller implementation and maintenance. During maintenance, some functional aspects of the overall process are interrupted, but the remainder of the components continue to function uninterrupted with local controllers in a closed-loop, as against total shutdown in the case of centralised control architectures. Similarly, redesigning a part of the process does not imply complete remodeling of the entire controller architecture, as would happen in the case of centralised control. Under decentralization, it is important to specify the applicable conditions for which the local closed-loop controller laws are capable of keeping the entire system stabilized. In the process industries, MPC techniques are generally utilised to solve large-scale multivariable control problems. An MPC formulates the control problem in the form of an optimization problem in which several (possibly competing) goals and constraints (state- and control-related) can be specified. Due to scalability and model maintenance issues, a centralised MPC is typically inadequate for large-scale networked systems. In light of the above, it makes sense to envisage decentralised model predictive control (DeMPC) and distributed model predictive control (DMPC) algorithms, which involve compartmentalizing a big optimization objective into multiple smaller units that iterate independently (DeMPC) or cooperatively (DMPC) to ultimately attain the overall system objective. The primary distinction between \"decentralised\" and \"distributed\" is the way information is shared among control regions. In DeMPC, local controllers make independent decisions. Prior control choices and measurements can only be provided before and after a decision is made. Communication considerations such as network delays and packet loss have no effect on the decision-making time for local control actions. Figure 6 depicts a DeMPC architecture wherein five distinct control regions are controlled individually by local MPC controllers. On the other hand, Figure 7 shows a corresponding DMPC layout wherein candidate control decisions may be exchanged and iterated during the decision-making process until local controllers agree on a stopping condition [52]. * **Feedback and feedforward MPCs:** Feedback correction is an inherent feature of MPCs, along with rolling optimization and predictive modeling characteristics [18]. The combination of MPC and feedback linearization (FL) has been popular among researchers for many years due to the ease of controllability of FL plants using linear MPCs [53]. For instance, [PERSON] et al. [54] applied a state feedback linearization (SFL)-enabled MPC to effectively control a pharmaceutical coolant temperature application. However, some researchers [55] have supported MPC architectures inclusive of feedback as well as feedforward control signals. This architecture overcomes the inherent drawback of purely feedback control loops with regards to the detection of system deviations after they have occurred. The feedforward and feedback loops act together to eliminate all measured and unmeasured system disturbances (Figure 8). [PERSON] et al. [55] also proposed linear MPC architectures with feedback as well as feedforward loops for multi-input and multi-output mobile robot systems. [PERSON] et al. [56] designed cascaded linearized feedback controllers to control animal cell concentrations and nutrients in a cultivation plant. [PERSON] et al. [57] incorporated a feedforward-feedback regulation regime to effectively control disturbances in a multiple-effect falling film evaporator system. [PERSON] et al. [18] applied active feedback correction in a trajectory-tracking controller for an unmanned vehicle to overcome system interference and uncertainties. Table 3 furnishes a featured summary of the above-discussed MPC variants.