The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) compared with genetic algorithm-based PID (GA-PID) and classical PID; a total of five controllers were simulated for controlling and trajectory tracking of the ball dynamics in a linearized ball-and-plate (B&P) system. For the experiments, we trained novel variants of deep RL-PID built from a customized deep deterministic policy gradient (DDPG) agent (by modifying the neural network architecture), resulting in two new RL agents (DDPG-FC-350-R-PID & DDPG-FC-350-E-PID). Each of the agents interacts with the environment through a policy and a learning algorithm to produce a set of actions (optimal PID gains). Additionally, we evaluated the five controllers to assess which method provides the best performance metrics in the context of the minimum index in predictive errors, steady-state-error, peak overshoot, and timeresponses. The results show that our proposed architecture (DDPG-FC-350-E-PID) yielded the best performance and surpasses all other approaches on most of the evaluation metric indices. Furthermore, an appropriate training of an artificial intelligencebased controller can aid to obtain the best path tracking.

Maximum power point tracking (MPPT) entails constraining photovoltaic (PV) modules to operate under a specified power condition. It has previously been shown that some meta-heuristic techniques often suffer from steady-state oscillations around maximum points and experience difficulty in adapting to environmental variations, such as irradiation and/or temperature. To address the aforementioned limitation, this work proposed an adaptable reinforcement learning (RL) technique based on a novel deep deterministic policy gradient (DDPG) agent and a reward function. The actor–network top layer uses a sigmoid activation function and the critic–network contains bottleneck layers with non-uniform nodal distributions as well as exponential linear unit (ELU) activation functions in some of the layers. The RL based on DDPG method was compared with Particle Swarm Optimization (PSO) and Perturb-and-Observe (P&O) in order to determine the optimal duty-cycle command needed for controlling the PV modules MPPT. All the investigated systems were implemented in MATLAB/Simulink. The results show that the proposed RL technique based on DDPG agent yielded superior tracking efficiency than all the other approaches. However, as the step change in irradiation at a constant temperature increases, the RL technique based on DDPG agent shows a decrease in tracking efficiency.

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The choice of a vehicle speed controller for a permanent magnet synchronous driven battery electric vehicle (BEV), integrated with a regenerative braking system (RBS), plays a pertinent role in how far a vehicle can travel. While many studies have implemented varied control strategies, the proportional-integral (PI) based controller is predominately studied and practically used. Generally, the performance of PI controller compared to Modeled Predictive Controller; Reference Tracking Signal Controller Based on Generated Polynomial and Reference Tracking Signal Controller Based on Controlled Polynomial in a permanent magnet synchronous motor (PMSM) driven battery electric vehicle integrated with RBS speed control based on field-oriented control mechanism is lacking. In order to address that shortcoming, this paper aims to investigate the performance of a PI controller compared to generalized predictive controllers based on the difference between a reference speed and a controlled speed of the battery electric vehicle. The performance for each controller considered was evaluated in terms of the state of charge (SOC), R^2, slip ratio, minimum error metrics, and peak speed using real-time driving scenario factoring braking, throttling, wind speed, and cruise effects for 15 s. The results show that the PI controller outperforms the other controllers on most metrics. Except for the peak SOC, slip ratio, and peak speed, the Reference Tracking Signal Controller Based on Generated Polynomial seems to be better, although with a drawback of the highest error metrics. Generally, all the controllers yielded excellent regenerative braking effects in SOC and effective control of the speed trajectory.