Analytical results for the multi-objective design of model-predictive control

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational resource is the underlying design consideration. This paper analyzes the MPC design problem with control performance and required computational resource as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational resource separately -- often with the latter as a fixed constraint -- which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient solver are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the design approach and importance of the developed conditions for an effective solver of the MOD-MPC problem

Evolutionary-game-based dynamical tuning for multi-objective model predictive control

Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.Peer ReviewedPostprint (author's final draft

Multi-objective modulated Model Predictive Control for a multilevel solid state transformer

Finite Control Set Model Predictive Control (FCS-MPC) offers many advantages over more traditional control techniques, such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response of the control system. This control scheme has been recently applied to several power conversion systems, such as two, three or more level converters, Matrix converters, etc. Unfortunately, because of the lack of presence of a modulation strategy, this approach produces spread spectrum harmonics which are difficult to filter effectively. This may results in a degraded power quality when compared to more traditional control schemes. Furthermore, high switching frequencies may be needed, considering the limited number of switching states in the converter. This paper presents a novel multi-objective Modulated predictive control strategy, which preserves the desired characteristics of FCS-MPC but produces superior waveform quality. The proposed method is validated by experimental tests on a seven level Cascaded H-Bridge Back-To-Back converter and compared to a classic MPC scheme

Multi-objectives model predictive control of multivariable systems

In this thesis, MOO [Multi-Objective Optimization] design for Model Predictive Control (MPC) and Proportional Integral (PI) control are investigated for a multivariable process

Modulated model predictive control for a 7-level cascaded h-bridge back-to-back converter

Multilevel Converters are known to have many advantages for electricity network applications. In particular Cascaded H-Bridge Converters are attractive because of their inherent modularity and scalability. Predictive control for power converters is advantageous as a result of its applicability to discrete system and fast response. In this paper a novel control technique, named Modulated Model Predictive Control, is introduced with the aim to increase the performance of Model Predictive Control. The proposed controller address a modulation scheme as part of the minimization process. The proposed control technique is described in detail, validated through simulation and experimental testing and compared with Dead-Beat and traditional Model Predictive Control. The results show the increased performance of the Modulated Model Predictive Control with respect to the classic Finite Control Set Model Predictive Control, in terms ofcurrent waveform THD. Moreover the proposed controller allows a multi-objective control, with respect to Dead-Beat Control that does not present this capability