控制理论与技术（英文版）/Journal Control Theory and TechnologyCSCDEI
查看更多>>“Journal of Control Theory and Applications”(《控制理论与应用》(英文版))是由国家教育部主管、华南理工大学主办的全国性学术刊物。2003年创刊，双月刊、 A4开本，国内外公开发行。本刊主要报道系统控制科学中具有新观念、新思想的理论研究成果及其在各个领域中，特别是高科技领域中的应用研究成果。本刊设置的栏目主要有：论文、短文、书刊评介、国内外学术动态等。读者对象为从事控制理论与应用研究的科技人员，高校师生及其它有关人员。
查看更多>>摘要：We examine three simple linear systems from the viewpoint of ergodic theory.We digitize the output and record only the sign of the output at integer times.We show that even with this minimal output we can recover important information about the systems.In particular,for a two-dimensional system viewed as a flow on the circle,we can determine the rate of rota-tion.We then use these results to determine the slope of the trajectories for constant irrational flow on the two-dimensional torus.To achieve this,we randomize the system by partitioning the state space and only recording which partition the state is in at each integer time.We show directly that these systems have entropy zero.Finally,we examine two four-dimensional systems and reduce them to the study of linear flows on the two-dimensional torus.
查看更多>>摘要：While brain computer interfaces(BCIs)offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user's intentions.Here,we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor move-ment errors in a continuous BCI cursor control task.This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.
查看更多>>摘要：Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-off between exploration and exploita-tion.Quantum technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement learning.In this paper,a quantum-enhanced reinforcement learning is pictured for optimal control.In this algorithm,the states and actions of reinforcement learn-ing are quantized by quantum technology.And then,a probability amplification method,which can effectively avoid the trade-off between exploration and exploitation via quantized technology,is presented.Finally,the optimal control policy is learnt during the process of reinforcement learning.The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym.The preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-off between exploration and exploitation.The learning performance of this new algorithm is stable with different learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.
查看更多>>摘要：This research deals with the energy management problem to minimize the cost of non-renewable energy for a small-scale microgrid with electric vehicles(EV)and electric tractors(ET).The EVs and ETs function as batteries in the power system,while they often have to leave it for their mobility and agricultural work.Each State of Charge(SoC),which is the charge rate of the battery from 0 to 1,and the operating time of ETs are optimized under the assumption that the required electrical energy,the arrival and departure time of EVs,and the working time of ETs are given by users,but they include uncertainties.In this paper,we deal with these uncertainties by constraints for robust energy planning and expected optimization based on scenarios,and show that the scheduling of the SoC assuming the worst case and the optimal home-based power consumption planning that considers the cost of each scenario corresponding to each variation can be obtained.Our proposed method is formulated as a mixed-integer linear programming(MILP),and numerical simulations show that the optimal cooperative operation among multiple houses can be obtained and its global optimal or sub-optimal solution can be quickly obtained by using CPLEX.
查看更多>>摘要：Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals,and scheduling optimization is a very important research issue in medical service.In this paper,to cope with the actualities of operation resources scheduling,such as poor planning,lack of standardized scheduling rules,chaotic use of the operating rooms,and many human interference factors,we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources.We first design a framework that includes the composite dispatching rules(CDR),optimization ideology,and feedback mechanism,in which the CDR integrates flexible operating time,hold-up time of medical facilities,available time of medical staff,and multiple constraints.The optimization ideology is carried out through a learning model based on the weighted random forest(WRF)algorithm.The feedback mechanism enables the approach to realize closed-loop optimizations adaptively.Finally,the superiority of the systematic scheduling approach(SSA)is analyzed through numerical experiments on a simulation platform.Results of the simulation experiments show that the proposed scheduling method can improve performances significantly,especially in the waiting time of patients.
查看更多>>摘要：In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtained using these scenarios.Based on the scenario theory,a prior certification is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP.To facilitate the computational task,a distributed solution strategy is proposed by the alternating direc-tion method of multipliers(ADMM)and a finite-time consensus strategy.Moreover,the distributed strategy can solve the scenario problem over a weight-balanced directed graph.Finally,the proposed solution strategy is applied to an EDP for a power system involving wind power plants.
查看更多>>摘要：In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a consensus solution in the inter-section of all the feasible regions.The formulation is transformed into a distributed optimization problem by introducing slack variables and consensus constraints.Then,by the primal-dual methods,a distributed algorithm is proposed with the help of projection operators and derivative feedback.Finally,the convergence of the algorithm is analyzed,followed by illustrative simulations.
查看更多>>摘要：In this review article,we present more than a decade of our work on the development of brain-computer interface(BCI)systems for the restoration of walking following neurological injuries such as spinal cord injury(SCI)or stroke.Most of this work has been in the domain of non-invasive electroencephalogram-based BCIs,including interfacing our system with a virtual reality environment and physical prostheses.Real-time online tests are presented to demonstrate the ability of able-bodied subjects as well as those with SCI to purposefully operate our BCI system.Extensions of this work are also presented and include the development of a portable low-cost BCI suitable for at-home use,our ongoing efforts to develop a fully implantable BCI for the restoration of walking and leg sensation after SCI,and our novel BCI-based therapy for stroke rehabilitation.
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