## 1 Introduction
In [13], the uncertainty of the nonlinear dynamic model, the time-varying external disturbances and the friction resistance of the \(n\)-link robotic manipulator are integrated into the uncertainty of the system as well. An adaptive robust term is used to compensate for this uncertainty. A reward function combining a Gaussian function and the Euclidean distance is designed for efficient and stable learning of the reinforcement learning agent. In this method, the parameters of the controller are adjusted and optimized in real time. The results show that the reward value obtained increases gradually with the increase in learning times, so, for slow processes, this method might be useful. By comparison, a controller design based on Multilayer Perceptron Neural Network (MLPNN) is developed in [14]. The proposed strategy can be very effective in control of the distributed multi-agent synchronous generator system. An online backpropagation algorithm is applied on MLPNN to distinguish the uncertainties of the SG model and update and regulate the weights of MLPNN adaptively based on the principles of consensus error. The controller can be applied as a learning scheme for estimation of the multi-agent system states, especially where there is no prior knowledge about the dynamics of leader/follower agent systems. However, the selection of parameters for the Neural Networks can substantially affect the accuracy of the proposed controller. Similarly, Ref. [15] presents a PID controller optimization procedure under constraints of model uncertainties and noise measurement, for a synchronous generator automatic voltage regulator with good results, but a plant model of the exciter is needed. This could be a drawback of the proposal due to the fact that data of the exciter are often unavailable. Some schemes for the stabilization and synchronization of SG are focused to having an efficient use of computational resources as detailed in [16]. There, an adaptive control based on the motion programmed trajectory is used for synchronous generators. In the procedure, it requires the proper choice of the reference model. Moreover, it is mentioned that the strategy shows a promising level of robustness against disturbances. The strategy is recommended for SG from small to medium size. In [17], an H-infinity control for the SG is proposed. The state vector is feedback in the control scheme and the solution of the Riccati equation is carried out. The technique is tested in a three machine power system. The control eliminates the disturbances, making the generators converge to the desirable setpoints, but big jumps are observed when transitions are presented. In contrast, Ref. [18] proposes an adaptive controller to damp oscillations and to enhance the Single Machine Infinite Bus (SMIB) stability. An adaptive linear Neural Network is employed as online model identification to mimic online the SMIB output. The coefficients of the SMIB model are adjusted online and then they are employed for estimating the parameters of the controllers. This method can be used in systems with large perturbation and parameter modifications to reduce oscillation and tuned time. In the same way, Ref. [19] presents an extended linear quadratic regulator designed for linear time-invariant systems in the presence of exogenous inputs with a novel feedback control structure. It can be used in model-based and model-free versions with net cost minimization by using dynamic programming. Disturbances in the system can be considered as exogenous inputs; however, the methodology is still not validated with large scale power systems. The sensitivity analysis with respect to frequency variations, fault in tie-line, power flow and output reaction of dissimilar generators after instant load fluctuation in the planned structure in terms of dispersion of wind is discussed in [20]. Here, several controllers are compared in delay time, rise time, peak overshoot time and settling time. The power system consistent operation requires constant balancing of source and load.