Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement.Based on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction horizon.Moreover,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system.The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs.The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth Information

Dear Editor,This letter investigates the cooperative localization problem for multiple autonomous underwater vehicles(AUVs)in underwater anchor-free environments,where AUV localization errors grow without bound due to the accumulated errors in inertial measurements(termed accumulated errors hereafter)and the lack of anchors(with known positions).

Simulation Analysis of Deformation Control for Magnetic Soft Medical Robots

Dear Editor,This letter presents a biocompatible cross-shaped magnetic soft robot and investigates its deformation mode control strategy through COMSOL modeling and simulation.Magnetic soft robots offer novel avenues for precise treatment within intricate regions of the human body.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

Most formation approaches of autonomous underwater vehicles(AUVs)focus on the control techniques,ignoring the influence of underwater channel.This paper is concerned with a communication-aware formation issue for AUVs,subject to model uncertainty and fading channel.An integral reinforcement learning(IRL)based estimator is designed to calculate the probabilistic channel parameters,wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design(M-PCM-OFFD)is employed to evaluate the uncertain channel measurements.With the estimated signal-to-noise ratio(SNR),we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs,dealing with uncertain dynamics and current parameters.For the proposed formation approach,an integrated optimization solution is presented to make a balance between formation stability and communication efficiency.Main innovations lie in three aspects:1)Construct an integrated communication and control optimization framework;2)Design an IRL-based channel prediction estimator;3)Develop an IRL-based formation controller with M-PCM-OFFD.Finally,simulation results show that the formation approach can avoid local optimum estimation,improve the channel efficiency,and relax the dependence of AUV model parameters.

Finite-Time Sideslip Differentiator-Based LOS Guidance for Robust Path Following of Snake Robots

This paper presents a finite-time sideslip differentiator-based line-of-sight(LOS)guidance method for robust path following of snake robots.Firstly,finite-time stable sideslip differentiator and adaptive LOS guidance method are proposed to counteract sideslip drift caused by cross-track velocity.The proposed differentiator can accurately observe the cross-track error and sideslip angle for snake robots to avoid errors caused by calculating sideslip angle approximately.In our method,the designed piecewise auxiliary function guarantees the finite-time stability of position errors.Secondly,for the case of external disturbances and state constraints,a Barrier Lyapunov functionbased backstepping adaptive path following controller is presented to improve the robot’s robustness.The uniform ultimate boundedness of the closed-loop system is proved by analyzing stability.Additionally,a gait frequency adjustment-based virtual velocity control input is derived to achieve the exponential convergence of the tangential velocity.At last,the availability and superiority of this work are shown through simulation and experiment results.

Fast least-squares prestack time migration via accelerating the explicit calculation of Hessian matrix with dip-angle Fresnel zone

Amplitude versus offset analysis is a fundamental tool for determining the physical properties of reservoirs but generally hampered by the blurred common image gathers(CIGs).The blurring can be optimally corrected using the blockwise least-squares prestack time migration(BLS-PSTM),where common-offset migrated sections are divided into a series of blocks related to the explicit offsetdependent Hessian matrix and the following inverse filtering is iteratively applied to invert the corresponding reflectivity.However,calculating the Hessian matrix is slow.We present a fast BLS-PSTM via accelerating Hessian calculation with dip-angle Fresnel zone(DFZ).DFZ is closely related to optimal migration aperture,which significantly attenuates migration swings and reduces the computational cost of PSTM.Specifically,our fast BLS-PSTM is implemented as a two-stage process.First,we limit the aperture for any imaging point with an approximated the projected Fresnel zone before calculating the Hessian matrix.Then,we determine whether a seismic trace contributes to the imaging point via DFZ during calculating the Hessian matrix.Numerical tests on synthetic and field data validate the distinct speedup with higher-quality CIGs compared to BLS-PSTM.

Model-Free Formation Control of Autonomous Underwater Vehicles:A Broad Learning-Based Solution

Dear Editor,We develop a broad learning-based algorithm to enforce the formation control of AUVs.Compared with the deep learning(DL)based formation solutions,our solution employs the broad learning system(BLS)to remodel the learning framework without a retraining process.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Communication-Aware Mobile Relaying via an AUV for Minimal Wait Time:A Broad Learning-Based Solution

Dear Editor,This letter studies the communication-aware mobile relaying via an autonomous underwater vehicle(AUV)for minimal wait time.Compared with the analysis-based channel prediction solution,the proposed discrete Kirchhoff approximation solution has a higher estimation accuracy.

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem

In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.

A HMM-based Mandarin Chinese Singing Voice Synthesis System

We propose a mandarin Chinese singing voice synthesis system, in which hidden Markov model(HMM)-based speech synthesis technique is used. A mandarin Chinese singing voice corpus is recorded and musical contextual features are well designed for training. F0 and spectrum of singing voice are simultaneously modeled with context-dependent HMMs. There is a new problem, F0 of singing voice is always sparse because of large amount of context, i.e., tempo and pitch of note, key, time signature and etc. So the features hardly ever appeared in the training data cannot be well obtained. To address this problem,difference between F0 of singing voice and that of musical score(DF0) is modeled by a single Viterbi training. To overcome the over-smoothing of the generated F0 contour, syllable level F0 model based on discrete cosine transforms(DCT) is applied, F0 contour is generated by integrating two-level statistical models.The experimental results demonstrate that the proposed system outperforms the baseline system in both objective and subjective evaluations. The proposed system can generate a more natural F0 contour. Furthermore, the syllable level F0 model can make singing voice more expressive.

Trajectory Tracking of Vertical Take-off and Landing Unmanned Aerial Vehicles Based on Disturbance Rejection Control

We investigate the trajectory tracking problem of vertical take-off and landing(VTOL) unmanned aerial vehicles(UAV), and propose a practical disturbance rejection control strategy. Firstly, the nonlinear error model is established completely by the modified Rodrigues parameters, while considering dynamics of the servo actuators. Then, a hierarchical control scheme is applied to design the translational and rotational controllers based on the time-scale property of each subsystem,respectively. And the linear extended state observer and auxiliary observer are used to deal with the uncertainties and saturation.At last, global stability of the closed-loop system is analyzed based on the singular perturbation theory. Simulation results show the effectiveness of the proposed control strategy.

Anomaly detection of control rod drive mechanism using long short-term memory-based autoencoder and extreme gradient boosting

Anomaly detection for the control rod drive mechanism(CRDM) is key to enhancing the security of nuclear power plant equipment. In CRDM real-time condition-based maintenance, most existing methods cannot deal with long sequences and periodic abnormal events and have poor feature extraction from these data. In this paper,a learning-based anomaly detection method employing a long short-term memory-based autoencoder(LSTM-AE)network and an extreme gradient boosting(XGBoost)algorithm is proposed for the CRDM. The nonlinear and sequential features of the CRDM coil currents can be automatically and efficiently extracted by the LSTM neural units and AE network. The normal behavior LSTM-AE model was established to reconstruct the errors when feeding abnormal coil current signals. The XGBoost algorithm was leveraged to monitor the residuals and identify outliers for the coil currents. The results demonstrate that the proposed anomaly detection method can effectively detect different timing sequence anomalies and provide a more accurate forecasting performance for CRDM coil current signals.

Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers

This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.

Coherent H^(∞)Control for Linear Quantum Systems With Uncertainties in the Interaction Hamiltonian

This work conducts robust H^(∞)analysis for a class of quantum systems subject to perturbations in the interaction Hamiltonian.A necessary and sufficient condition for the robustly strict bounded real property of this type of uncertain quantum system is proposed.This paper focuses on the study of coherent robust H^(∞)controller design for quantum systems with uncertainties in the interaction Hamiltonian.The desired controller is connected with the uncertain quantum system through direct and indirect couplings.A necessary and sufficient condition is provided to build a connection between the robust H^(∞)control problem and the scaled H^(∞)control problem.A numerical procedure is provided to obtain coefficients of a coherent controller.An example is presented to illustrate the controller design method.

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related techniques.According to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and compared.The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated.The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given.Key issues on OFRMPC optimizations for LPV systems are discussed.Furthermore,the future research directions on OFRMPC for LPV systems are suggested.

Early-Awareness Collision Avoidance in Optimal Multi-Agent Path Planning With Temporal Logic Specifications

Dear Editor,This letter investigates a multi-agent path planning problem in a road network with the requirement of avoiding collisions among all agents in the partitioned environment.We first abstract the agents to a set of transition systems,and construct a team transition system from these individual systems.A mechanism is designed for the team transition system to detect all collisions within the synthesized run.