Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach

Piezoelectric inkjet printing has important applications in high-precision manufacturing, but precise control of droplet quality faces challenges such as the nonlinear coupling between drive waveform parameters and droplet characteristics, as well as environmental disturbances. To address these issues, this paper proposes a closed-loop droplet control method based on a curriculum deep deterministic policy gradient (DDPG) algorithm, with the standard deviation of droplet volume and velocity as the main optimization objectives. To tackle the complex mapping relationships and boost training efficiency, we first built a high-fidelity simulation environment using a four-layer deep residual network trained on real-world industrial ink droplet data. Following that, we designed a dynamic weight-adaptive composite reward mechanism and integrated a curriculum learning strategy with the Deep Deterministic Policy Gradient (DDPG) algorithm. Relying on our self-developed droplet observation platform, we achieve closed-loop control of the standard deviation of droplet volume and velocity. Experimental results show that this method can precisely control the droplet volume within ±0.1 pl of the target value, while keeping the velocity standard deviation below 40 mm/s. The adjustment process is stable without severe oscillations, and the method demonstrates strong robustness against environmental disturbances such as ink supply pressure. This provides an efficient and practical solution for the automated control of droplet quality in piezoelectric inkjet printing.

Various linear and nonlinear controllers have been developed to improve the dynamic performance of DC-DC converters. Most controllers can only be designed on the basis of understanding the mathematical model of DC-DC converter, but the inherent nonlinear and time-varying characteristics of DC-DC switching converter make it difficult to complete the precise modeling, so the model-based control design is complex and the control performance is limited. In order to overcome the problem, this paper proposes a reinforcement learning (RL) controller based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. This controller does not need the model of the switching converter. The converter will be regarded as a black box model, the policy approximation function (policy neural network) can be trained and learned by constructing a Markov decision process interacting with the black box model in the control system, and the optimal control action can be output. The RL controller is developed based on actor critic architecture, and a TD3 algorithm with higher learning efficiency is proposed to improve the control performance of the RL controller. The proposed RL controller based on TD3 algorithm is compared with the traditional PI controller. The simulation results show that the RL controller has better dynamic performance when the converter starts and the load step changes.

To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed.

The research on autonomous driving based on deep reinforcement learning algorithms is a research hotspot. Traditional autonomous driving requires human involvement, and the autonomous driving algorithms based on supervised learning must be trained in advance using human experience. To deal with autonomous driving problems, this paper proposes an improved end-to-end deep deterministic policy gradient (DDPG) algorithm based on the convolutional block attention mechanism, and it is called multi-input attention prioritized deep deterministic policy gradient algorithm (MAPDDPG). Both the actor network and the critic network of the model have the same structure with symmetry. Meanwhile, the attention mechanism is introduced to help the vehicles focus on useful environmental information. The experiments are conducted in the open racing car simulator (TORCS)and the results of five experiment runs on the test tracks are averaged to obtain the final result. Compared with the state-of-the-art algorithm, the maximum reward increases from 62,207 to 116,347, and the average speed increases from 135 km/h to 193 km/h, while the number of success episodes to complete a circle increases from 96 to 147. Also, the variance of the distance from the vehicle to the center of the road is compared, and the result indicates that the variance of the DDPG is 0.6 m while that of the MAPDDPG is only 0.2 m. The above results indicate that the proposed MAPDDPG achieves excellent performance.

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.

This paper presents a parameter selection method of notch filters for suppressing mechanical resonances in industrial servo systems using the deep deterministic policy gradient (DDPG) algorithm. Several methods for tuning the notch filter parameters were studied such as the fast-Fourier-transform-based methods, extended-Kalman-filter-based methods and adaptive notch filter methods. However, these methods do not find the Q parameters of notch filters which play an important role in determining the system stability, and do not consider the cases in which multiple notch filters are required. Deep-Q-network-based method was developed to solve these problems, but the notch filter parameter tuning is limited to discrete action spaces. This paper develops a new parameter selection method of notch filters, using the DDPG algorithm. DDPG algorithm, which is a model-free and actor-critic algorithm using deep neural networks, is utilized for its capability to operate over continuous action spaces. Experiments are performed using an actual industrial servo system to demonstrate that the developed parameter selection method successfully finds the notch filter parameters to suppress the resonances of the system.

The UAV pursuit-evasion strategy based on Deep Deterministic Policy Gradient (DDPG) algorithm is a current research hotspot. However, this algorithm has the defect of low efficiency in sample exploration. To solve this problem, this paper uses the imitation learning (IL) to improve the DDPG exploration strategy. A kind of quasiproportional guidance control law is designed to generate effective learning samples, which are used as the data of the initial experience pool of DDPG algorithm. The UAV pursuit-evasion strategy based on DDPG and imitation learning (IL-DDPG) is proposed, and the algorithm obtains the data from the experience pool for experience playback learning, which improves the exploration efficiency of the algorithm in the initial stage of training and avoids the problem of too many useless exploration in the training process. The simulation results show that the trained pursuit-UAV can flexibly adjust the flight speed and flight attitude to pursuit the evasion-UAV quickly. It also verifies that the improved DDPG algorithm is more effective than the basic DDPG algorithm to improve the training efficiency.

Lithium-ion batteries are highly sensitive to temperature, with excessive heat leading to accelerated aging, reduced cycle life, and safety risks such as thermal runaway. Traditional thermal management systems often struggle to maintain optimal battery temperature, especially under high ambient temperatures or dynamic operating conditions. This paper proposes a novel reinforcement learning (RL)-based control method for the coupled thermal management system of a power battery and air conditioning system in a Plug-in Hybrid Electric Vehicle (PHEV). The system integrates the battery cooling circuit with the air conditioning system via a heat exchanger, eliminating the need for a separate radiator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to control the water pump speed, optimizing battery temperature in real-time. Experimental results under New European Driving Cycle (NEDC) conditions at 40°C ambient temperature demonstrate that the RL-controlled pump responds faster than the Model Predictive Control (MPC) algorithm, allowing the battery temperature to reach the target temperature of 28°C 1 min earlier with a maximum fluctuation of only 0.8°C. The RL-controlled pump consumes 4 W h more energy than the MPC-controlled pump, while the compressor energy consumption is only 1.68% higher. Additionally, the temperature fluctuations in the passenger cabin under RL control are only 0.1°C higher than under MPC, ensuring passenger comfort. This study highlights the potential of RL-based control for improving the efficiency and stability of integrated thermal management systems in electric vehicles.

Objective As the core issue of ship navigation safety, ship collision avoidance always depends on the captain's personal status and judgment, which has certain potential safety hazards. In order to coordinate all ships (cruise ships, cargo ships, etc.) in key waters and predict their paths, it is necessary to establish an anti-collision early warning mechanism. Methods Using the deep deterministic policy gradient (DDPG) algorithm and the ship domain model, the electronic chart is used to simulate the ship's navigation path, and the improved strategy of DDPG algorithm based on the key learning of failure region and the improved parameters of ship domain model according to the characteristics of cruise ships are proposed, Improve the accuracy of route prediction and anti-collision. Results Using the improved DDPG algorithm and the improved ship domain model, compared with the previous algorithm, the accuracy of ship collision avoidance is improved from 84.9% to 89.7%, and the average error between the simulated route and the real route is reduced from 25.2 m to 21.4 m. Conclusion Through the ship collision avoidance path planning based on the improved DDPG algorithm and the improved ship domain model, the ship route supervision function in water area can be realized, and when the predicted route intersects with other ships, the dispatcher will be alerted to realize the anti-collision early warning mechanism.

An improved Deep Deterministic Policy Gradient (DDPG) algorithm, integrating self-attention mechanisms, adversarial training, and prioritized experience replay, is proposed to address the limitations of state representation capability, sample efficiency, and algorithm robustness in the DDPG algorithm under complex dynamic environments. First, a multihead self-attention module is constructed within the Critic network to enhance the network’s spatial modeling capability for complex environments by parallel computing multidimensional state-action association features. This improves the accuracy of Q-value estimation and the stability of training. Second, an adversarial training mechanism is introduced, where adversarial samples are proportionally mixed into the training data, enhancing the algorithm’s adaptability to state disturbances. Meanwhile, a prioritized experience replay pool is designed based on the SumTree structure, improving the reuse efficiency of high-value samples. Finally, dynamic and static scenarios are built on the Gazebo simulation platform, and real-world experiments are conducted. The results show that, compared with the original DDPG algorithm, the improved algorithm achieves a 55.3% faster convergence speed in dynamic scenarios, an 80.2% reduction in Critic loss, and stronger generalization ability across different scenarios. Real-world tests further validate the superiority of the algorithm in dynamic obstacle avoidance and trajectory smoothness.

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

Anti-rolling devices are widely used in modern shipboard components. In particular, ship anti-rolling control systems are developed to achieve a wide range of ship speeds and efficient anti-rolling capabilities. However, factors that are challenging to solve accurately, such as strong nonlinearities, a complex working environment, and hydrodynamic system parameters, limit the investigation of the rolling motion of ships at sea. Moreover, current anti-rolling control systems still face several challenges, such as poor nonlinear adaptability and manual parameter adjustment. In this regard, this study developed a dynamic model for a ship anti-rolling system. In addition, based on deep reinforcement learning (DRL), an efficient anti-rolling controller was developed using a deep deterministic policy gradient (DDPG) algorithm. Finally, the developed system was applied to a ship anti-rolling device based on the Magnus effect. The advantages of reinforcement learning adaptive control enable controlling an anti-rolling system under various wave angles, ship speeds, and wavelengths. The results revealed that the anti-rolling efficiency of the intelligent ship anti-rolling control method using the DDPG algorithm surpassed 95% and had fast convergence. This study lays the foundation for developing a DRL anti-rolling controller for full-scale ships.

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods.

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

Advanced control techniques for CHP-DH systems: A critical comparison of Model Predictive Control and Reinforcement Learning