Abstract-The formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper. The proposed algorithm is designed so that the socalled reaching phase, normally present in the evolution of a system controlled via the sliding mode approach, is reduced to a minimum. This fact makes the algorithm more suitable to be applied to a real industrial robot, since it enhances its robustness, by extending it also to time intervals during which the classical sliding mode is not enforced. Moreover, since the algorithm generates second order sliding modes, while the model of the controlled electromechanical system has a relative degree equal to one, the control action actually fed into the plant is continuous, which provides a positive chattering alleviation effect. The assessment of the proposal has been carried out by experimentally testing it on a COMAU SMART3-S2 anthropomorphic industrial robot manipulator. The satisfactory experimental results, also compared with those obtained with a standard PD controller and with the original Suboptimal algorithm, confirm that the new algorithm can be actually used in an industrial context.

Abstract-This technical note introduces the design of sliding mode control algorithms for nonlinear systems in the presence of hard inequality constraints on both control and state variables. Relying on general results on minimum-time higher-order sliding mode for unconstrained systems, a general order control law is formulated to robustly steer the state to the origin, while satisfying all the imposed constraints. Results on minimum-time convergence to the sliding manifold, as well as on the maximization of the domain of attraction, are analytically proved for the first-order and second-order sliding mode cases. A general result is presented regarding the domain of attraction in the general order case, while numerical results on the estimation of the domain of attraction and on minimum-time convergence are discussed for the third-order case, following a procedure applicable to a sliding mode of any order.

The present paper deals with modelling of complex microgrids and the design of advanced control strategies of sliding mode type to control them in a decentralized way. More specifically, the model of a microgrid including several distributed generation units (DGus), connected according to an arbitrary complex and meshed topology, and working in islanded operation mode (IOM), is proposed. Moreover, it takes into account all the connection line parameters and it is affected by unknown load dynamics, nonlinearities and unavoidable modelling uncertainties, which make sliding mode control algorithms suitable to solve the considered control problem. Then, a decentralized second order sliding mode (SOSM) control scheme, based on the Suboptimal algorithm is designed for each DGu. The overall control scheme is theoretically analyzed, proving the asymptotic stability of the whole microgrid system. Simulation results confirm the effectiveness of the proposed control approach.

Abstract-This paper deals with the design of advanced control strategies of sliding mode type for microgrids. Each distributed generation unit (DGu), constituting the considered microgrid, can work in both grid-connected operation mode (GCOM) and islanded operation mode (IOM). The DGu is affected by load variations, nonlinearities and unavoidable modelling uncertainties. This makes sliding mode control particularly suitable as a solution methodology for the considered problem. In particular, a second order sliding mode (SOSM) control algorithm, belonging to the class of Suboptimal SOSM control, is proposed for both GCOM and IOM, while a third-order sliding mode (3-SM) algorithm is designed only for IOM, in order to achieve, also in this case, satisfactory chattering alleviation. The microgrid system controlled via the proposed sliding mode control laws exhibits appreciable stability properties, which are formally analyzed in the paper. Simulation results also confirm that the obtained closed-loop performances comply with the IEEE recommendations for power systems.

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Abstract-This paper deals with the design of a robust hierarchical multi-loop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic MIMO system and the combination of a Model Predictive Control (MPC) module with an Integral Sliding Mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodelled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfilling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the model predictive control optimization problem relying on a set of linearized decoupled SISO systems which are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data.Index Terms-Model predictive control, integral sliding mode, robot manipulators, uncertain systems.

International Journal of Control ASSOSM˙Microgrids˙manuscriptThis paper deals with the design of adaptive suboptimal second order sliding mode (ASSOSM) control laws for grid-connected microgrids. Due to the presence of the inverter, of unpredicted load changes, of switching among different renewable energy sources, and of electrical parameters variations, the microgrid model is usually affected by uncertain terms which are bounded, but with unknown upper bounds. To theoretically frame the control problem, the class of second order systems in Brunovsky canonical form, characterized by the presence of matched uncertain terms with unknown bounds, is first considered. Four adaptive strategies are designed, analyzed and compared to select the most effective ones to be applied to the microgrid case study. In the first two strategies the control amplitude is continuously adjusted, so as to arrive at dominating the effect of the uncertainty on the controlled system. When a suitable control amplitude is attained, the origin of the state-space of the auxiliary system becomes attractive.In the other two strategies a suitable blend between two components, one mainly working during the reaching phase, the other being the predominant one in a vicinity of the sliding manifold, is generated, so as to reduce the control amplitude in steady-state. The microgrid system in grid-connected operation mode, controlled via the selected ASSOSM control strategies, exhibits appreciable stability properties, as proved theoretically and shown in simulation.

This work proposes a hybrid control methodology to achieve full body collision avoidance in anthropomorphic robot manipulators. The proposal improves classical motion planning algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc for performing obstacle avoidance, while achieving a reaching task in the operative space. More specifically, a switching mechanism is enabled whenever a condition of proximity to the obstacles is met, thus conferring to the dualmode architecture a self-configuring capability in order to cope with objects unexpectedly invading the workspace. The proposal has been finally tested relying on a realistic robot manipulator simulated in a V-REP environment.