The cooling system directly impacts the power output of an engine. The cooling water flow and temperature in the heat exchange process outside the engine exhibit dynamic characteristics of nonlinear, time-varying, and solid coupling, precise control algorithms to optimize the coordination mechanism of the pump and valve with electronic control are crucial factors for the cooling system to handle thermal loads. This paper presented a feedforward compensation decoupling control and schedules the fuzzy proportional-integral-differential (FPID) controller optimized through an improved particle swarm algorithm for the engine cooling system. Primarily, construct the mathematical model of the cooling system’s heat exchange by gathering relevant data, and the feedforward compensation decouplers are designed to diminish the coupling extent of cooling water flow and temperature in the external cooling cycle. Next, create the whole control system model in MATLAB/Simulink and perform functional simulation by incorporating particle swarm optimization (PSO) algorithm with the dynamic adjustment strategy of stochastic inertia weight to optimize the FPID controller’s quantization and proportionality factors. Eventually, the approach’s feasibility is validated on the existing physical experimental platform. The findings demonstrate that this proposed improved control and optimization method is expected to enhance the water flow regulation characteristics as well as temperature control precision of the cooling water and strengthen the stability of the heat exchange efficiency of the cooling system.

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects.

Due to the complex internal working process of circulating cooling water systems, most traditional control methods struggle to achieve stable and precise control. Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). The structure is based on the Markov decision process of the recirculating cooling water system. Initially, the TD3 algorithm is employed to construct a deep reinforcement learning agent. Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. The agent updates its network based on different reward values obtained through interactions with the system, thereby gradually aligning the action values with the optimal policy. The TD3-RTM method introduces a reference trajectory model to accelerate the convergence speed of the agent and reduce oscillations and instability in the control system. Subsequently, simulation experiments were conducted in MATLAB/Simulink. The results show that compared to PID, fuzzy PID, DDPG and TD3, the TD3-RTM method improved the transient time in the flow loop by 6.09s, 5.29s, 0.57s, and 0.77s, respectively, and the Integral of Absolute Error(IAE) indexes decreased by 710.54, 335.1, 135.97, and 89.96, respectively, and the transient time in the temperature loop improved by 25.84s, 13.65s, 15.05s, and 0.81s, and the IAE metrics were reduced by 143.9, 59.13, 31.79, and 1.77, respectively. In addition, the overshooting of the TD3-RTM method in the flow loop was reduced by 17.64, 7.79, and 1.29 per cent, respectively, in comparison with the PID, the fuzzy PID, and the TD3.

A novel quinoxaline-based phthalonitrile monomer, namely, 4,4′-(((quinoxaline-2,3-diylbis (oxy))bis (3,1-phenylene))bis (oxy))di-phthalonitrile (QDOP), was successfully synthesized by nucleophilic substitution reaction, which exhibits a low melting point (85°C) and a wide processing window (150°C). The QDOP monomer was cured with 4-aminophenoxy phthalonitrile (APPH) as catalyst by different temperature programs, and the curing process and kinetics were discussed in detail by non-isothermal differential scanning calorimetric (DSC). The QDOP polymer mainly forms triazine, isoindoline and phthalocyanine structure, as revealed by Fourier transform infrared (FTIR) spectroscopy, and its properties improved with the increase of post-curing temperature and curing time. After post-curing at 380°C, the polymer exhibited high storage modulus (3731 MPa), high glass-transition temperature (>400°C), and outstanding thermal stability and thermal oxidation stability.

In response to the critical implications of single tail rotor failures in unmanned helicopters, this study proposes a distributed electric tail rotor yaw channel controller that employs Active Disturbance Rejection Control (ADRC) techniques. A dynamics model was created for an unmanned helicopter with a distributed electric tail rotor. This model was then employed to verify the yaw channel tracking performance of the helicopter through simulation. The verification process involved the use of a cascaded dual closed-loop active disturbance rejection control strategy. The ADRC framework was enhanced by developing an improved fal function to replace the conventional fal function. This addressed the challenges of excessive error gain and overshoot oscillation in the Extended State Observer (ESO). A test setup for the distributed electric tail rotor of an unmanned helicopter was devised and constructed for empirical testing purposes. This setup was used as a reference for the 700-class electric model helicopter. The findings of the simulation demonstrate that the control strategy for the unmanned helicopter’s distributed electric tail rotor, enhanced by the refined cascaded ADRC method, achieves superior response speed and minimized overshoot during yaw channel tracking. Furthermore, it displays a degree of enhanced robustness and improved control proficiency in comparison to the conventional ADRC approach. The outcomes of the test stand validate the efficacy of this control strategy.

The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning and control smoothness near actuator limits. To address these issues, this paper develops a Beta-policy and PID-inspired augmented-state proximal policy optimization framework, termed BPAS-PPO, for the secondary cooling water system. The framework augments the state with proportional, integral, and derivative error features, adopts a Beta-distribution policy for bounded continuous-action generation, and uses a piecewise dense reward for the dual-loop tracking task. Simulation studies on an identified linear two-input two-output (TITO) model within the selected operating region show that the complete PID-augmented state yields the most effective training representation among the tested alternatives. Compared with PID, Fuzzy-PID, and Gauss-PPO, BPAS-PPO shows lower overshoot, shorter settling time, better setpoint tracking and disturbance rejection, and smoother control actions near actuator limits. The proposed framework is effective for the studied system within the selected operating region, while its performance beyond this region requires further validation.