This paper presents the preliminary results of a Linear Parameter Varying-Autoregressive eXogenous model, identified through Least Squares Support Vector Machines, able to optimally drive a robotic arm system by emulating the performance of a Nonlinear Model Predictive Control (NMPC) policy. The support vector machine framework is employed to replicate the control performance of a computationally demanding NMPC. Due to the nonlinear characteristics of the robotic arm, the NMPC is suitable to guarantee expected control performance. However, its application in real-time systems with fast dynamics is limited by high memory and computational demands required at each sampling instant. In this work, the linear parameter varying model is trained using a data-driven approach to imitate the control actions of the NMPC across different scenarios. The proposed controller and the original NMPC are evaluated in simulation, considering multiple operating conditions of the robotic arm. The control performance of both approaches is then compared to assess the effectiveness of the proposed method.

A geofence is a virtual perimeter representing the limits of a real-world operating area. The development of a control policy allowing to guarantee the safety of the Unmanned Aircraft Vehicles' (UAVs) users and stakeholders represents an important industrial world problem, yet studied in depth by the international scientific research community. In this paper, a geofencing system for UAVs based on the Model Predictive Control (MPC) paradigm is proposed. MPC permits to optimally drive dynamical systems explicitly imposing constraints on input and output by the prediction of the future evolution of the controlled plant. In this paper, an MPC policy is proposed to impose the geofencing area of a pre-compensated multi-rotor UAV. The proposed approach considers recomputing iteratively the controlled UAV speed constraints with respect to a prescribed maximum vehicle deceleration, in order to correctly impose the limits on vehicle speed and to stop the UAV on the borders of the prescribed geofence operating area. The proposed algorithm has been verified in simulation tests controlling a pre-compensated multi-rotor vehicle in a considered control scenario.

This paper presents a preliminary study considering the design of an adaptive torque allocation policy for electric vehicles combining optimal control with data-driven techniques. The vehicle is equipped with four independent actuated wheels driven by electric motors. The policy aims to control the vehicle powertrain by allocating available power among motors to satisfy the driver control torque request and adjust torque allocated to different motors according to estimated wheels slip ratio change due to terrain varying conditions. A constrained optimal torque allocation algorithm is designed to distribute available power among wheels. In order to adjust the power allocation result, a data-driven adaptive policy is designed to adjust the control allocation parameters and the torque distribution reflecting wheel's operating conditions. The combination of torque allocation and data-driven adaptation policies permits the adjustment of the allocated power according to the wheel/road contact conditions. The algorithm has been tested and validated in simulation, showing the improvement given by the proposed approach compared with respect to the control system neglecting the data-driven adaptation of the torque allocation policy.

Nowadays many machines and robots are programmed to perform the same task repeatedly. The Iterative Learning Control (ILC) paradigm is based on the idea that the performance of a system that executes the same trial multiple times can be improved by learning from the previous iterations. The objective of ILC is to improve the batch process performance by incorporating past trials error information into the control reference signal for the subsequent iteration. The ILC algorithms are categorized with respect to the number of past iterations considered to compute the next control signal and the first order ILC includes those algorithms considering only information about the last trial. In this paper different first order ILC update laws have been considered and compared controlling a Single-Input Single-Output (SISO) micro-positioning piezostage system. The proposed comparison allows to evaluate the performance of different first order ILC algorithms tested on the considered real world case study.

This paper presents a Model Predictive Control (MPC) strategy for a triaxial piezoelectric actuators (PAs) system. PAs systems require appropriate controllers to guarantee fast and high-precision positioning performances avoiding effects of non-linearities. Typically, commercial systems provide integrated Proportional-Integral (PI) controllers guarantying to maintain system stability in the presence of uncertainty and disturbance. MPC owes its success to the ability of optimally regulate multivariable systems through the minimization of a Quadratic Programming (QP) problem subjected to prescribed constraints. Alternatively, unconstrained MPC eliminates constrains from the problem, reducing the number of operations elapsed to compute the solution. The aim of this work is to design an unconstrained MPC for a 3-DOF PA replacing PI controllers to improve control performances by a smaller increase of required computational effort. The system is described by a Multi-Input Multi-Output (MIMO) Linear Time-Invariant (LTI) model, experimentally identified by the open-loop real plant response. Effectiveness of the proposed method is validated by simulation tests and experiments on the real system, comparing MPC with PI controllers tuned to guarantee common PA stability requirements.

Piezoelectric drivers are widely used in nanoscale image acquisition systems. A source of performance degradation of these drivers is hysteresis, which introduces low rate noise and causes a slow continuous drift, decreasing the quality of scanned images. In this paper a sliding mode control policy, based on an estimator of the perturbation due to hysteresis, is applied to the control of a piezostage in a three dimensional images acquisition system, in order to improve control performances and final scanning results. The presented solution has been experimentally tested and compared with the Proportional-Integrative-Derivative (PID) controllers provided with the piezoelectric acquisition system, showing a noticeable improvement both in the piezostage behavior and in the images quality.

In this paper we present a computational intelligence approach to solve the optimal sizing problem of grid connected microgrid (MG) components. A simulation model has been built for the MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of the long term economic benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using population based optimization techniques. We tested four different algorithms in their basic form, i.e. artificial bee colonies, particle swarm optimization, genetic algorithm and gravitational search algorithm, comparing their performances. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.

The control of DC-DC converters for high performance applications usually relies on Current Mode Control (CMC). Compared to voltage mode control, it guarantees automatic over-current protection, and a better closed loop stability, together with improved transient response. This paper presents a Model Predictive Control (MPC) algorithm for the regulation of the voltage reference signal in CMC. The control of pre-compensated systems via MPC is common in several fields, such as automotive and aerospace, and power electronics is a perfect candidate to exploit this hierarchical structure as well. Indeed, controllers for power converters are often coded in the integrated circuits, and cannot be changed. Furthermore the possible multirate structure allows to exploit the performance of MPC less affecting the computational cost. The paper describes the design of an MPC regulator for a synchronous buck converter, when a primal CMC is already coded. Performance improvements of the proposed controller are reported.