Is Limited Information Enough? An Approximate Multi-agent Coverage Control in Non-Convex Discrete Environments

In this paper, we study approximate and exact controllability of the linear difference equation x ( t ) = ∑ j = 1 N A j x ( t - Λ j ) + B u ( t ) in L 2 , with x ( t ) ∈ ℂ d and u ( t ) ∈ ℂ m , using as a basic tool a representation formula for its solution in terms of the initial condition, the control u , and some suitable matrix coefficients. When Λ 1 , ⋯ , Λ N are commensurable, approximate and exact controllability are equivalent and can be characterized by a Kalman criterion. This paper focuses on providing characterizations of approximate and exact controllability without the commensurability assumption. In the case of two-dimensional systems with two delays, we obtain an explicit characterization of approximate and exact controllability in terms of the parameters of the problem. In the general setting, we prove that approximate controllability from zero to constant states is equivalent to approximate controllability in L 2 . The corresponding result for exact controllability is true at least for two-dimensional systems with two delays.

In multi-agent coverage control, the Voronoi diagram optimization strategy is widely used. However, the limitation of sensing radius makes Voronoi diagram optimization strategy not suitable to implement. Moreover, when the agent’s dimension and obstacles are considered, the Voronoi diagram cannot deal further due to the collision that may occur. Considering these limitations, power diagram optimization strategy is used. To deal with obstacles, artificial potential field strategy is combined with the power diagram. Despite its simplicity, the numerical study shows that the proposed scheme of coverage control works well under sensing constraints even with the presence of obstacles. The proposed strategy is convergent to the objective without collision with obstacles as well as among the agents.

This letter presents an adaptive coverage control strategy for multi-agent systems with obstacle avoidance in the presence of actuator faults and time-varying uncertainties. The strategy is based on a leader-follower approach. Assuming that the motion of the leader is given, one distributes the followers within the leader's obstacle-free sensing range so that collisions with obstacles can be avoided. An optimized distribution is achieved through the Centroidal Voronoi Tessellation (CVT) and a function approximation technique based immersion and invariance (FATII) coverage controller is constructed to realize the CVT. The stability of the FATII coverage controller is established and its validity is tested by simulations.

Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a new one, which has the same Pareto optimal solutions and the number of objectives with the original one. Each meta-objective in the new problem consists of two components which measure the convergence and diversity performances of a solution, respectively. Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, including those non-Pareto-based ones. Particularly, it can boost the Pareto-based algorithms' ability to solve many-objective optimization problems. Due to separately evaluating the convergence and diversity performances of a solution, the traditional density-based selection criteria, for example, crowding distance, will no longer mistake a solution with poor convergence performance for a solution with low density value. By penalizing a solution in term of its convergence performance in the meta-objective space, the Pareto dominance becomes much more effective for a many-objective optimization problem. Comparative study validates the competitive performance of the proposed meta-objective approach in solving many-objective optimization problems.

In this paper, we study a multi-vehicle coverage control problem in constant flow environments while taking into account both energy consumption and traveling time. More specifically, the metric (called the mixed energy-time metric) is a weighted sum of the energy consumption and the traveling time for a vehicle to travel from one point to another in constant flows when using the minimum energy control, and the objective is to find vehicle locations that can minimize the expected mixed energy-time required for the set of vehicles to cover a region. We propose a refined gradient based control law of which the convergence is proved via Hybrid Systems Theory. Simulations show that the refined gradient based control can achieve similar performance as the exact gradient based control.

In lossless audio compression, it is essential for predictive residuals to remain sparse when applying entropy codings. Hence, developing an accurate predictive method is crucial. The sign algorithm (SA) is a conventional method for minimizing the magnitude of residuals; however, it exhibits poor convergence performance compared with the least mean square (LMS) algorithm. To overcome the convergence performance degradation, we proposed novel adaptive algorithms based on a natural gradient: the natural gradient sign algorithm (NGSA) and normalized NGSA (NNGSA). We also propose an efficient update method for the natural gradient based on the AR(p) model. It requires <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(p)$</tex> multiply-add operations at every adaptation step. Through experiments conducted using toy data and real music data, we showed that the proposed algorithms achieve better convergence performance than the SA does. The NNGSA suggested having good compression ability in lossless audio coding.

We discuss the functional control systems governed by differential equations with Riemann-Liouville fractional derivative in general Banach spaces in the present paper. First we derive the uniqueness and existence of mild solutions for functional differential equations by the approach of fixed point and fractional resolvent under more general settings. Then we present new sufficient conditions for approximate controllability of functional control system by means of the iterative and approximate method. Our results unify and generalize some previous works on this topic.

The cold chain transportation monitoring system is mainly responsible for data monitoring and control in the cold chain transportation environment, ensuring the stability of the cold chain compartment environment, and playing an important role in the transportation and storage of fresh agricultural products. The traditional Internet of Things cold chain control system uses manual input of control parameters to realize the monitoring of the cold chain compartment environment, which lacks intelligence. Therefore, the BP (Back propagation, BP) neural network is used to build a cold chain transportation monitoring model to realize the dynamic monitoring of the cold chain compartment environment. Considering that the BP network is easy to fall into the problem of “local fall”, which leads to the decline of the prediction accuracy of the model, the improved discrete artificial bee colony algorithm is used to optimize the parameters of the BP neural network, and the fish swarm algorithm is used to decode the discrete population to obtain the final result. Decimal value to construct the DABC-BP cold chain transportation control model. The experimental simulation results show that in the $$f_{1} (x)$$ function optimization test, the improved DABC-BP (Discrete Artificial Bee Colony-Back Propagation Neural Network, DABC-BP) algorithm has the best optimization accuracy, and the minimum logarithm after 1000 iterations is 10−7. Meanwhile, the DABC algorithm has the best convergence performance, reaching convergence after 821 iterations. Compared with related hybrid algorithms such as mayfly algorithm and extreme learning machine, this research method is suitable for harsher environments and more sensitive to temperature monitoring. The research has important reference value for the transportation and storage of modern agricultural products.

Phase retrieval: Stability and recovery guarantees

In this paper the problem of covering an area using a network of multi-agent systems in the presence of inter-agent communication delays induced by communication faults is investigated. A spatial partitioning technique is used based on the delayed information to divide the field among all agents. Then a motion coordination algorithm is proposed to drive agents in such a way that the coverage performance function is minimized over the regions assigned to each agent. Simulations show that the proposed approach offers improved performance compared to conventional coverage method, and that the inter-agent collision avoidance is also guaranteed.

Reinforcement learning (RL) is a powerful machine learning technique to learn optimal actions in a control system setup. An important drawback of RL algorithms is the need for balancing exploitation vs exploration. Exploration corresponds to taking randomized actions with the aim to learn from it and make better decisions in the future. However, these exploratory actions result in poor performance, and current RL algorithms have a slow convergence as one can only learn from a single action outcome per iteration. We propose a novel concept of Inference-based RL that is applicable to a specific class of RL problems, and that allows to eliminate the performance impact caused by traditional exploration strategies, thereby making RL performance more consistent and greatly improving the convergence speed. The specific RL problem class is a problem class in which the observation of the outcome of one action can be used to infer the outcome of other actions, without the need to actually perform them. We apply this novel concept to the use case of dynamic resource allocation, and show that the proposed algorithm outperforms existing RL algorithms, yielding a drastic increase in both convergence speed and performance.

Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

Aiming at the pixel misclassification and poor convergence performance of the suppressed fuzzy C-means clustering algorithm in image segmentation, an adaptive double suppressed fuzzy C-means clustering image segmentation algorithm based on regional information entropy and fuzzy partition is proposed. Firstly, the regional information entropy of each region is calculated by dividing and judging the region to which the pixel belongs, and the number of initial clusters is redetermined according to the actual needs; Secondly, the regional information entropy is used to construct the correction factor, and the membership matrix of different regions is first suppressed by the correction factor; Finally, the fuzzy partition matrix is combined with the distribution characteristics of the data itself to construct the adaptive selection formula of the suppression factor, and realize the second suppression of the membership matrix. The experimental results show that the proposed algorithm, compared with the related algorithms, can improve the misclassification of pixel and the convergence performance.

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultra-reliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field.

When using a differential evolution algorithm to solve the joint flood optimization scheduling problem of cascade reservoirs, a greedy random optimization strategy is prone to premature convergence. Therefore, a new, improved Elite Conservative Differential Evolution Algorithm (ECDE) was proposed in this study. This algorithm divides a population into elite and general populations. The elite population does not undergo differential mutation, whereas the general population uses an adaptive differential mutation strategy based on successful historical information to participate in differential mutation. This elite conservative strategy effectively improves the diversity of the population evolution process and enhances convergence accuracy and stability. In a numerical experiment involving 10 test functions, the proposed ECDE performed the best overall (seven functions had the best stable convergence solution, while the remaining three performed the best), while in the single-objective flood control optimization scheduling problem of cascade reservoirs in the middle reaches of the Gan River, some algorithms could not even stably converge to feasible solutions (taking the 1973 inflow as an example, the peak shaving rate of the ECDE calculation results was 3.4%, 13.72%, and 11.73% higher than those of SHADE, SaDE, and GA, respectively). The proposed ECDE algorithm outperformed the SHADE, SaDE, GA, PSO, and ABC algorithms in terms of both convergence accuracy and stability. Finally, ECDE was used to analyze the multi-objective flood control scheduling problem of cascade reservoirs in the middle reaches of the Gan River, and it was found that the weight setting in multi-objective optimization should follow an upstream priority program or equilibrium programs. Adopting a downstream priority program results in poor upstream flood control performance. The above analysis fully verifies the superiority of the proposed algorithm, which can be used to solve and analyze the joint optimization scheduling problem of cascade reservoirs.