Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as “this is better than that” but also as “this is much better than that”), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human–robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.

To allow wheelchair (electronic or manual) users to practice driving in different safe, repeatable and controlled scenarios, the use of simulator as a training tool is considered here. In this context, the capabilities of providing high fidelity motions for users of the simulator is highlighted as one of the most important aspects for the effectiveness of the tool. For this purpose, the motion cueing algorithm (MCA) is studied in our work to regenerate wheelchair motion cues by transforming motions of the real or simulated wheelchair into the simulator motion. The studied algorithm is developed based on Model Predictive Control (MPC) approach to efficiently optimize the motions of the platform. The overall problem is formulated using mixed-integer quadratic programming (MIQP) which involves not only the vestibular model, strict constraints of the platform but also the perception threshold in the optimization cost function. In the end, the performance assessment of the system using different control techniques is analyzed, showing the effectiveness of the proposed approach in the simulation environment.

This paper addresses the problem of sharing a private Electrical energy Storage System (ESS) for thermoelectrical energy management in a group of buildings. Each building is equipped with an ESS which can be used partly or completely by other buildings in the same group. This sharing strategy creates coupling constraints among the controllers of each individual building. In this context, an increment proximal minimization method has been employed to manage the energy flows among the various ESS's through a distributed approach. The local controller of each building employs a Model Predictive Control (MPC) which focuses on balancing economic optimization and occupant comfort while fulfilling various local technical constraints. The proposed technique allows local controllers to operate with maximum autonomy and privacy since, at each iteration, a little amount of information is exchanged between each local controller to a centralized coordination unit. Indeed, only the information related to the coupling constraints is required to exchange with the centralized unit. The most significant advantage of the method is to allow the individual building to exploit effectively not only its own ESS but also any excess power and energy capacity of the ESSs of other buildings. The simulation results show the accuracy and efficiency of the proposed method.

This paper addresses the problem of thermal energy efficiency in domestic buildings. As opposed to classic optimal approach, a sensitivity analysis and comparison have been performed to evaluate both comfort and consumption of different heating scheduling, corresponding to different user-oriented scenarios, which allows to derive basic operating rules. Also continuous and discontinuous heating strategies are compared. The various scenarios are evaluated for different building thermal properties and different climatic conditions, under a classic mixed PID and hysteresis control scheme. The case study here considered is a house heated through a radiant floor system. A new index of assessment of user's comfort is also proposed. This paper explores the importance of heating scheme in terms of comfort and energy saving and highlights the importance of the decision maker point of view when deciding the most appropriate heating strategy. An optimal control strategy is also implemented (model-based predictive control) for comparison.

This paper presents a model predictive control approach for the economic optimization of a microgrid including smart buildings, wind power production facilities and an energy storage unit. Various optimization scenarios are considered in a comprehensive and unified framework, which can be adapted to pursue different objectives at the same time, such as ensuring the electricity supply to the smart buildings, maximizing the profit from the electricity trading market, or managing the energy storage. The optimization problem can be addressed in a model predictive framework using the receding horizon approach, and ultimately formulated as a quadratic programming problem, which can be solved with reliable and efficient tools. In order to analyze a realistic scenario, the relevant data are taken from real systems (i.e., from a real wind farm and from a real commercial building, located in Italy). Simulation results show the economic advantages that can be gained through the combined usage of renewable energy generation and energy storage.

Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a data-driven controller design method, based on the Virtual Reference Feedback Tuning (VRFT) approach, is introduced. Advantages in terms of robustness with respect to unknown environment stiffness are discussed and demonstrated. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.