## 2 Special Issue
This work starts with a short characterisation of MPC and general control performance assessment approaches, including experiment-based, model-based and data-driven methods. The author brings attention to the fact that assessing MPC control quality requires a suitable, often specific, methodology and comparative indicators. Next, control performance assessment methods designed specifically for MPC are reviewed. In particular, non-Gaussian and fractal methods are emphasised because they are robust against industrial disturbances, allow us to measure control quality and detect wrong MPC tuning or configuration. In addition to providing a rich list of references accompanying the article, the author also points out several important issues collected during many years of industrial practice concerned with advanced process control system design. The article _Comparison and Interpretation Methods for Predictive Control of Mechanics_[12] was written by [PERSON]. This contribution reminds us that the [PERSON]'s theorem allows us to invoke [PERSON] and [PERSON]'s equations to fully describe the six degrees of freedom of mechanical motion. For a double-integrator benchmark process, the effectiveness of the following different control strategies are thoroughly compared: the open-loop optimum controller, continuous-time and discrete-time MPC optimum controllers, the Proportional and Derivative (PD) controller derived from an optimisation problem, the feedforward-feedback PD controller and the two-Degrees-of-Freedom (2 DOF) controller which consists of the optimal control strategy augmented with feedback errors calculated with optimal states. The controllers are assessed in terms of many indicators, including tracking error and robustness, evaluated using Monte Carlo analysis. Noise and modeling errors are taken into account. The optimal controller achieves the lowest cost (with very poor robustness), while the classical controllers achieve low mean errors and deviations (at the highest cost). Both continuous-time and discrete-time MPC schemes perform well, while the 2 DOF controller performs better. The article _Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control_[13] was written by [PERSON] et al. This contribution describes an MPC-based approach based on dynamic multi-objective optimisation for reducing freeway congestion and relieving environment impact. For MPC optimisation, a new dynamic multi-objective optimisation algorithm based on clustering and prediction is introduced. The minimised cost-function measures the time spent, the travel distance, the emissions and the resulting fuel consumption. The optimal solution is used from the Pareto front computed by the optimisation algorithm. To verify the algorithm's performance, simulations of the freeway network in Shanghai are considered. The obtained results indicate that it can effectively alleviate traffic congestion and reduce emissions and fuel consumption compared to the fixed speed limit strategy and the classical MPC approach based on a single optimisation method. This research received no external funding. Not applicable. The author declares no conflict of interest.