## 2 Special Issue Topics  
This Special Issue has received several submissions, out of which 13 contributions passed the review process and have been published. Papers [15, 16] deal with two topics that are strictly related: reinforcement learning (RL) and the Markov decision process (MDP). In [15], RL is used to study the autonomous learning mechanism to solve complicated human tasks. The tuning task of cavity filters is considered, which is a common task in the communication industry. In [16], a time-optimal path planning method for autonomous underwater vehicles (AUVs) is proposed based on an MDP algorithm. Its performance is examined under different oceanic conditions, revealing its advantages compared to the A* algorithm, a traditional path planning method. The simulations demonstrate the robustness of the approach even in the case of complex ocean currents. Additionally, paper [17] deals with AUV path planning in the presence of current disturbances. In this case, the specific application is docking. In this work, a nonlinear model of the vehicle is modelled in a linear parameter varying (LPV) form. Then, the LPV model is used for a model predictive control (MPC) design for computing the set of forces and moments driving the vehicle. The LPV-MPC control action is mapped into the reference signals for the actuators by using a thrust allocation (TA) algorithm. The simulation results show that AUVs can be effectively controlled in a wide range of scenarios. Moving from water to air, paper [18] discusses the application of distributed control of a fixed-wing unmanned aerial vehicle (UAV) for coordinated tracking under model uncertainty and disturbances. Based on the gaussian process regression, a data-driven model is established. Then, a learning-based consensus protocol for multi-UAVs is designed. The stability of the system is proven via a Lyapunov function, and the effectiveness of the proposed method is shown by numerical simulations. Additionally, the control of ground vehicles is discussed in this Special Issue in [19; 20]. In [19], a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware. By exploiting this pipeline, a fuzzy controller has been implemented to align the robot autonomously with the staircase. This algorithm can be applied to different robots running the ROS middleware, thus increasing autonomy and allowing the operator to focus on the primary task. Paper [20] discusses the design and simulation of a dynamically stable, self-balancing electric monowheel. A reference model-based adaptive control structure is proposed. A linear quadratic regulator (LQR) and a linear quadratic gaussian (LQG) controller are implemented on the state-space model of the system and compared. Finally, a reference model-assisted adaptive control structure is established to compensate for sudden system parameter changes such as rider mass. Papers [21; 22; 23; 24] deal with motion control for industrial applications, both for single axes [21; 22] and for multi-DOF manipulators [23; 24]. Paper [21] proposes an analytic solution for state-constrained optimal tracking control problems for continuous-time linear time-invariant (CT-LTI) systems based on model-based prediction, quadratic penalty function, and a variational approach. The simulation results for a DC motor servo system are compared with the results of the direct multi-shooting method and of the implicit model predictive control. In [22], the PII\({}^{1/2}\)DD\({}^{1/2}\) control scheme is discussed, in which the half-integral and the half-derivative components are added to the classical PID. The frequency domain responses of the PID, PI\({}^{\lambda}\)D\({}^{u}\), and PII\({}^{1/2}\)DD\({}^{1/2}\) controllers are compared, then the stability features of the PII\({}^{1/2}\)DD\({}^{1/2}\) controller are outlined.