In the process of tracking and obstacle avoidance control of driverless vehicles, the controlled object has nonlinear characteristics and variable control parameters. The linear model and the fixed mathematical model of driverless vehicles are difficult to ensure the safety and stability of the vehicle in complex environments, and the driverless discrete control process increases the difficulty of control. To address such problems, in order to improve the accuracy of real-time control tracking trajectory of driverless vehicles, and at the same time reduce the difficulty of the whole control process, the paper proposed a Monte Carlo-depth deterministic policy gradient-based obstacle avoidance tracking control algorithm for driverless vehicles. The algorithm builds a control system model based on a deep reinforcement learning network, and adopts excellent training samples in the strategy learning sampling process. It optimizes the network training gradient with the Monte Carlo method, and makes a distinction between good and bad training samples for the algorithm. The excellent samples are used to find the optimal network parameters through a gradient algorithm, so as to enhance the learning ability of the network algorithm and realize a better and continuous control of the driverless vehicle. Simulation experiments of the control method were carried out in the computer simulation environment TORCS. The results show that the proposed improved DDPG algorithm can be applied to effectively achieve the obstacle avoidance tracking control of the driverless vehicle, and the tracking accuracy and obstacle avoidance effect of the unmanned car under its control is better than that of the deep Q network algorithm and the DDPG algorithm.

To address the challenges of mapping and navigation in unknown dynamic environments for drone navigation systems, a mapless navigation method based on an improved Twin Delayed Deep Deterministic policy gradient (TD3) is proposed. To solve the perception limitations in a mapless environment, the navigation model is defined as a Partially Observable Markov Decision Process (POMDP). A Gated Recurrent Units (GRU) is introduced to enable the policy network to utilize the temporal information from historical states, allowing it to obtain an optimal policy and avoid falling into local optima. Based on the TD3 algorithm, a softmax operator is employed to the value function, and a dual policy networks is adopted to address issues of policy function instability and value function underestimation in the TD3 algorithm. A non-sparse reward function is designed to resolve the challenge of policy convergence in reinforcement learning under sparse reward conditions. Finally, simulation experiments conducted on the AirSim platform demonstrate that the improved algorithm achieves faster convergence and higher task success rates in drone mapless obstacle avoidance navigation compared to traditional deep reinforcement learning algorithms.

The design of a chaotic image encryption algorithm plays an essential role in enhancing information and communication security. The performance of such algorithms is intricately linked to the complexity of the chaotic sequence and the underlying encryption algorithm. To additionally enhance the complexity of hyperchaotic systems, this study presents a novel construction of a Five-Dimensional (5D) memristive hyperchaotic system through the introduction of the flux-controlled memristor model. The system’s dynamic characteristics are examined through various analytical methods, including phase portraits, bifurcation diagrams, and Lyapunov exponent spectra. Accordingly, the sequences produced by the hyperchaotic system, which passed the National Institute of Standards and Technology (NIST) test, are employed to inform the creation of a novelty image encryption technique that combines hash function, Deoxyribonucleic Acid (DNA) encoding, logistic, and Two-Dimensional Hyperchaotic Map (2D-SFHM). It improves the sensitivity of key and plaintext images to image encryption, expands the algorithm key space, and increases the complexity of the encryption algorithm. Experimental findings and analysis validate the exceptional encryption capabilities of the novel algorithm. The algorithm exhibits a considerable key space 2⁵¹², and the ciphertext image demonstrates an information entropy of 7.9994, with inter-pixel correlation approaching zero, etc., showcasing its resilience against different types of attacks on images.

To address the problem of time-varying formation control of high-order heterogeneous unmanned cluster systems with uncertain system model dynamics and switching communication topology, an optimal distributed hierarchical formation control method is proposed based on integral reinforcement learning. The time-varying formation control problem for heterogeneous cluster systems is transformed into a stabilization problem by using time-varying formation switching vectors to construct an augmented system of multi-quadrotor Unmanned Aircraft System (UAS) with multi-unmanned vehicle systems. The value function with discount factor is introduced to transform the stabilization problem of the heterogeneous clustered system into an optimal control problem. Only the feedback gain parameters are replaced and averaged to obtain the optimal time-varying formation switching control protocol for the whole heterogeneous cluster without destroying the consistent distributed formation control protocol. The feedback gain of the distributed time-varying formation switching controller is updated online in real time using a single-network “actor network-critic network” structure, combined with the integral reinforcement learning algorithm and the distributed control method. The effectiveness and superiority of the proposed control scheme are verified by theoretical proof and simulation experiments.

In view of the trajectory tracking problem of the forward-tilting morphing aerospace vehicle with time-varying disturbance, a non-singular terminal sliding mode control scheme based on immersion and invariance (I&I) theory and fixed time convergence theory was proposed. Firstly, a time-varying disturbance observer based on I&I was designed by combining dynamic scale factors. Secondly, a segmented fixed-time non-singular terminal sliding surface was constructed, which eliminated the singularity of the sliding mode surface and made the system state converge to any small neighborhood of the equilibrium point within a fixed time, and the upper bound of the convergence time had nothing to do with the initial state of the system. Finally, based on the Lyapunov stability theory, the global fixed-time stability of the system was proven, and the upper bound of its convergence time was given. The effectiveness and superiority of the proposed control scheme were verified in two experimental scenarios. Compared with the traditional control method, the control scheme proposed in this paper made the system converge faster and has better anti-disturbance ability.

To address the problem that individual unmanned aerial vehicle (UAV) in under-actuated multi-UAV inclusion control cannot obtain global information and is subject to unknown external perturbations, making it difficult to converge quickly, this paper proposed a distributed fixed-time control strategy for under-actuated UAVs based on a dynamic scale observer. Firstly, a distributed fixed-time observer was designed to enable each UAV to quickly estimate the space state required to complete the contained mission when most UAVs fail to obtain the target state. Secondly, a finite-time dynamic scale observer based on immersion and invariance theory was proposed to quickly and accurately estimate the external perturbations to each UAV. Finally, a non-singular distributed fixed-time controller was designed to achieve the fast stereoscopic inclusion control of an under-actuated multi-UAV system by using local information. With the proposed control strategy, the upper bound on the convergence time of the system was independent of the initial state and depended only on the eigenvalues of the information transfer array and the controller parameters. The superiority of the designed controller was verified by theoretical demonstration and simulation experiments.