Effective path planning is vital for Autonomous Underwater Vehicles (AUVs) for performing diverse roles, including rescue efforts and logistics operations. In recent times, dynamic path planning has emerged as a prominent research area, enabling AUVs to navigate without the need for previous static information. Researchers are leveraging deep reinforcement learning (DRL), as another trending field of study, to tackle the challenges associated with dynamic path planning. In this study a dynamic sensing and collision avoidance approach is proposed to navigate in an entirely unknown environment with unstructured obstacles. Firstly, we employ Deep Q-Networks (DQN), a distinct domain within DRL, to address the challenge of path planning in a dynamic environment. Initially, established methodologies were implemented such as Double Deep Q-Networks (DDQN) and Dueling Double Deep Q-Networks (D3QN) to develop a model capable of navigating an AUV through environments containing both static and dynamic obstacles. Nevertheless, the previously mentioned techniques exhibit restricted generalization abilities in complex, high-dimensional state environments, frequently leading to inadequate performance in dynamic situations, thus making the more adaptable I-DQN method essential. Subsequently, Improved DQN (I-DQN) is introduced, an enhancement of the original DQN, aimed at further refining the DRL model's performance. Test within a real-time setting is carried out to evaluate the effectiveness of the DRL models against randomly generated starting and destination points. The results obtained indicate that the I-DQN approach outperforms DQN, DDQN, and D3QN regarding the path efficiency and the travel duration to reach the destination while avoiding collisions. The stability of the I-DQN algorithm is improved by Prioritized Experience Replay, which carefully selects significant transitions according to their temporal-difference errors, facilitating effective and reliable learning in intricate AUV navigation situations. The proposed strategy can be utilized to improve underwater search and rescue missions in inundated urban regions or areas affected by disasters. The AUVs can maneuver through intricate and unfamiliar underwater settings, skilfully dodging debris and obstructions, to swiftly and efficiently find survivors or evaluate structural damage.
The growing use of optimization models to help decision-making has created a demand for such tools that allow solving more real models of processes related to human activity in which hypotheses are not verified in a way specific for classical optimization. And just interval-valued optimization problems were developed for formulating such real-world problems which are usually not well defined and they contain uncertain data. In this paper, an interval-valued multitime control problem with first-order PDE constraints is considered. Then, the minimax exact penalty function method is used for solving the aforesaid interval-valued extremum problem. The most important property of all exact penalty function methods, that is, exactness of the penalization, is generalized to the case when one of such methods is used for solving an interval-valued multitime control problem. Namely, under appropriate invexity hypotheses, it is examined in the case of the minimax exact penalty function method which is used for solving the aforesaid interval-valued multitime control problem with the first-order PDE constraints.
In this research work, a new hyperchaotic two-scroll system with two unstable equilibrium points has been derived and the qualitative properties of the new hyperchaotic system are detailed. A bifurcation analysis has been carried out for the proposed hyperchaotic two-scroll system by varying the system parameters. Multistability property of the new hyperchaotic two-scroll system has been also illustrated by showing the coexistence of hyperchaotic attractors for the proposed hyperchaotic two-scroll system for the same set of parameter values but different initial conditions. For engineering applications, we provide an electronic circuit simulation of the proposed hyperchaotic two-scroll system using MultiSim 14.0. As a control application, we derive new results for the complete synchronization for a pair of new hyperchaotic two-scroll systems taken as the master and slave systems. We have used integral sliding mode control and Lyapunov stability theory for the derivation of the synchronizing control law for the complete synchronization design for a pair of new hyperchaotic two-scroll systems. We have provided MATLAB simulations to illustrate and highlight the main results of this work.
This study presents and evaluates three steering angle selection methods formulated for path following tasks. The methods determine the steering angle input through numerical integration of the vehicle equations of motion over a finite prediction horizon, divided into small time subintervals. The first approach introduces classical Nelder-Mead optimization algorithm when the steering angle is defined by minimizing a cost functional which is trajectory-tracking error. In the second and third cases the minimization of the cost functional is obtained by applying the following numerical methods: Newton iteration and bisection. The methods are applicable to vehicle dynamics models of varying complexity. Presented methods have been evaluated using planar (3 degrees of freedom — DoF) and spatial (10-DoF) vehicle models. Simulation results demonstrate that proposed approaches achieve trajectory-tracking accuracy comparable to those reported in literature. The bisection-based method provides balance between accuracy and computational efficiency, enabling reliable steering angle computation for trajectories with varying curvature and non-uniform speed profiles. A key advantage of the proposed numerical methods is their ability to incorporate nonlinear vehicle dynamics while significantly reducing the computational effort compared to conventional optimization-based approaches
This paper presents a novel optimization strategy for photovoltaic (PV) power systems based on a synergetic control (SC) theory integrated with state observation and adaptive estimation schemes. The main objective is to enhance system reliability and reduce implementation costs through the elimination of selected physical sensors. A state observer is designed to ensure stable system operation and avoid control-law singularities, while adaptive laws are employed to accurately estimate the load and the photovoltaic output voltage. These estimated states are provided to the synergetic controller, which drives the system to operate at the maximum power point (MPP). The proposed strategy is validated through numerical simulations under dynamic conditions, including variations in solar irradiance, temperature, and load. Furthermore, experimental tests corroborate the simulation results, confirming the robustness and high efficiency under partial shading conditions and demonstrating the strategy's practicality for real-world PV systems.
