Large Language Models (LLMs) show remarkable potential for few-shot information extraction (IE), yet their performance is highly sensitive to the choice of in-context examples. Conventional selection strategies often fail to provide informative guidance, as they overlook a key source of model fallibility: confusion stemming not just from semantic content, but also from the generation of well-structured formats required by IE tasks. To address this, we introduce Active Prompting for Information Extraction (APIE), a novel active prompting framework guided by a principle we term introspective confusion. Our method empowers an LLM to assess its own confusion through a dual-component uncertainty metric that uniquely quantifies both Format Uncertainty (difficulty in generating correct syntax) and Content Uncertainty (inconsistency in extracted semantics). By ranking unlabeled data with this comprehensive score, our framework actively selects the most challenging and informative samples to serve as few-shot exemplars. Extensive experiments on four benchmarks show that our approach consistently outperforms strong baselines, yielding significant improvements in both extraction accuracy and robustness. Our work highlights the critical importance of a fine-grained, dual-level view of model uncertainty when it comes to building effective and reliable structured generation systems.

In this paper, we consider multi-robot path planning problems for high-level tasks with a finite horizon. In many situations, there is a need tocount how many timesa sub-task is satisfied in order to achieve the overall task. However, existing temporal logic languages, such as linear temporal logic, is not efficient in describing such requirements. To address this issue, we propose a new temporal logic language calledCounting Time Temporal Logic(CTTL) that extends linear temporal logic by explicitly counting the number of times that some tasks are satisfied. To solve the CTTL path planning problem, we propose an efficient integer linear programming-based method to encode task satisfaction. We show that our approach is both sound and complete, while achieving higher efficiency than direct encodings of such requirements. Moreover, we study several variants of the problem. To validate our results, we present several numerical experiments to show the scalability of the proposed approach and a simulation case study of a team of autonomous robots to illustrate the feasibility of the synthesis procedure. Finally, to evaluate the real-world feasibility of our method, we conduct a hardware experiment with two Turtlebot3-Burger mobile robots.

This article studies the output-feedback event-triggered consensus problem of linear multi-agent systems. The core of our concern is to reduce the cost of communication resources of agents through designing new event-triggered sampling mechanisms. We propose an observer-based adaptive event-triggered protocol that can be implemented in a fully distributed and asynchronous way and construct three different edge-based event-triggering mechanisms. All the three mechanisms feature the design ofedge-based nonmonotonicclocks, which, in contrast to existing results, can not only provide theoretical guarantees on strictly positive minimum inter-event times, but also involve more potentials to reduce the number of events. In addition, the third mechanism ingeniously integrates the previous two designs, resulting in a novel composite design that is able to further reduce the number of events. Theoretical benefits are finally validated through numerical simulations.

We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called RRT*former, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.

In this paper, we investigate a multi-robot task allocation problem where a team of heterogeneous robots operates in a discrete workspace to achieve a set of tasks expressed by linear temporal logic formulas. In contrast to existing works, we further consider inter-task-time-order constraints, which are imposed on the start or end times of each task. Solving such problems generally requires combinatorial search, which is not scalable. Inspired by the efficiency of max-plus algebra in handling time constraints, we propose a novel approach called MaxAuc, which integrates Auction-based task allocation with Max-plus algebra in a novel manner. Specifically, max-plus computations are performed to approximate task priorities in the auction without explicitly solving the constraint optimization problem. Our numerical results demonstrate that MaxAuc is highly scalable with respect to both the number of robots and the number of tasks, while maintaining a tolerable performance trade-off compared to the baseline’s optimal yet exhaustive solution.

Control Barrier Functions (CBFs) have emerged as an effective and non-invasive safety filter for ensuring the safety of autonomous systems in dynamic environments with formal guarantees. However, most existing works on CBF synthesis focus on fully known settings. Synthesizing CBFs online based on perception data in unknown environments poses particular challenges. Specifically, this requires the construction of CBFs from high-dimensional data efficiently in real time. This paper proposes a new approach for online synthesis of CBFs directly from local Occupancy Grid Maps (OGMs). Inspired by steady-state thermal fields, we show that the smoothness requirement of CBFs corresponds to the solution of the steady-state heat conduction equation with suitably chosen boundary conditions. By leveraging the sparsity of the coefficient matrix in Laplace’s equation, our approach allows for efficient computation of safety values for each grid cell in the map. Simulation and real-world experiments demonstrate the effectiveness of our approach. Specifically, the results show that our CBFs can be synthesized in an average of milliseconds on a 200×200 grid map, highlighting its real-time applicability.

This work presents an unsupervised learning-based approach to approximate the controller of the Lyapunov-based Model Predictive Control (MPC) for a class of continuous-time nonlinear systems. The proposed design aims to produce an optimal control input by aligning the objectives and constraints of the learning problem with those of the MPC. The MPC is approximated by a deep feedforward neural network (DFNN) whose output can strictly satisfy the system constraints. A sufficient condition is provided under which the stability of the closed-loop system with the approximation DFNN control implemented in a sampling-and-hold fashion can be guaranteed. Additionally, we define a polyhedral Lyapunov function to shape the domain of attraction, allowing it to approach the shape of the state boundary defined by polyhedral state constraints, thereby providing potential for enlarging the domain of attraction. The implementation of the proposed method on a permanent magnet synchronous generator wind turbine and a continuous stirred tank reactor illustrates the effectiveness and performance of the designed approximation controller.

This article presents a less conservative self-triggered distributed model predictive control (ST-DMPC) algorithm for dynamically coupled systems with disturbances. In general, the triggering frequency is highly dependent on the prediction error between the actual and predicted states. However, this error tends to be overestimated due to the conservative handling of dynamic coupling and disturbances, leading to redundant triggering in resource-limited environments. To handle this problem, this work derives a tighter prediction error by predicting the future disturbances through their variations and incorporating them into state prediction models. Thus, compared to the traditional self-triggered criteria, the designed self-triggered conditions exhibit reduced conservatism and greater capability in reducing triggering frequency correspondingly. In addition, the designed ST-DMPC algorithm can achieve a tradeoff between computational usage and system performance. Sufficient conditions ensuring both recursive feasibility and closed-loop stability are derived, which are crucial for the algorithm’s implementation. Finally, two illustrative examples are provided to verify the merits of the algorithm from both qualitative and quantitative perspectives, respectively.

Industrial multimode operation commonly exists in chemical process control systems. The fundamental mechanism is classifying operational modes and identifying the local models w.r.t. typical operating conditions, such that the global dynamic characteristics can be aligned with the switching control strategies for flexible operation demands. This article proposes a novel integrated economic predictive control (EMPC) method, to account for the mutual coupling economic optimization objectives and overlapping feasible regions in mode decision-making and dynamic control. In the proposed EMPC formulation, the prediction and optimization are carried out on all of the switched-admissible modes, which have been predetermined together with the stabilization conditions at the preliminary stage. The switching signal and input sequences are combined-optimized for achieving the best accumulated economic cost defined on the optimal predicted trajectories. The switching feasibility, and local/global stability under specified DT constraints are guaranteed by the stabilization constraints designed in EMPC. Through theoretical analysis and simulation comparisons, the proposed approach is demonstrated to tackle with the infeasible problem that inevitably arises in the classic hierarchical decision-making architectures; moreover, it exhibits better closed-loop accumulated economic cost in the illustrated cases.

In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved. Sampling-based methods have emerged as a promising avenue for addressing this challenge by incrementally constructing random trees, thereby sidestepping the need to explicitly explore the entire state-space. However, the performance of this sampling-based approach hinges crucially on the chosen sampling strategy, and a well-informed heuristic can notably enhance sample efficiency. In this work, we propose a novel neural-network guided (NN-guided) sampling strategy tailored for LTL planning. Specifically, we employ a multi-modal neural network capable of extracting features concurrently from both the workspace and the Büchi automaton. This neural network generates predictions that serve as guidance for random tree construction, directing the sampling process toward more optimal directions. Through numerical experiments, we compare our approach with existing methods and demonstrate its superior efficiency, requiring less than 15% of the time of the existing methods to find a feasible solution.

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach.

In a Multi-Agent System (MAS), where each agent is assigned with local Signal Temporal Logic (STL) tasks, coupled tasks frequently appear, causing STL violations. To address this issue, a Distributed Model Predictive Control (DMPC) algorithm is proposed to loosen the temporal constraints of non-nested STL specifications with multiple sub-formulae. We use temporal relaxation and task postponement of some sub-formulae to handle task conflicts. First, the online reference trajectories of each subtask are generated that the corresponding STL satisfaction constraints are incorporated into the DMPC problem. Then, we use compatibility constraints to implement synchronous cooperative collision avoidance. Based on the online reference trajectories, the temporal relaxation metric of each sub-formula is given, and is optimized in the DMPC optimization problem. As a result, the optimal controller enforcing STL satisfaction and minimal temporal relaxation is found. Finally, simulations demonstrate the effectiveness of the suggested algorithm.

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. Note to Practitioners—Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

This paper proposes a distributed model predictive control (DMPC) for a class of discrete-time stochastic multi-agent systems subject to partially coupled temporal logic tasks. For each agent, the given tasks are formulated as local and coupled probabilistic signal temporal logic (PrSTL) constraints in DMPC, and the control objective is to satisfy the PrSTL constraints against stochastic uncertainties and the coupled spatio-temporal relationship between agents. Considering that control under STL is historically dependent, a shrinking horizon DMPC framework is adopted and a probabilistic-tightening method is proposed to transform the complex form of PrSTL into deterministic constraints. Then, combining with the asynchronous update strategy, the satisfaction verification of coupled PrSTL tasks is achieved. Since large uncertainties may result in optimization infeasibility and affect the completion of the temporal logic tasks, a distributed PrSTL task softening method is further proposed, which can guarantee the softened tasks converge to the original ones and reduce the conservatism of the controller design. The recursive feasibility of the proposed PrSTL-DMPC strategy is proved and the efficiency of the algorithm is demonstrated by simulations.Note to Practitioners—With the increasing application of autonomous systems, more complex temporal logic tasks are imposed on system behaviors. This paper focuses on the distributed control problem for stochastic multi-agent systems under temporal logic tasks formulated as PrSTL specifications. A asynchronous DMPC controller is presented to improve task satisfaction against uncertainties and coupled spatial-temporal relationships between agents. The control performance including the recursive feasibility and soundness is formally guaranteed. For applications to autonomous systems, the practitioners can employ PrSTL for specifying spatial and temporal requirements on agents and employ the proposed PrSTL-DMPC algorithm for the task implementation.

This paper aims to design a stabilized Adaptive Model Predictive Control (MPC) for nonlinear continuous systems with unknown dynamics at the start time. The proposed method is based on the Lyapunov MPC (LMPC) where the derivative of the Lyapunov function of the closed-loop system is limited to be less than that under an auxiliary Lyapunov controller. Different from the existing LMPC methods, this paper provides an online constraint updating strategy, which gradually reduces the conservation of the constraint. Specifically, the piece-wise linear (PWL) model updated by the Set-Membership (SM) identification method is adopted in the constraint and the auxiliary controller design, which can result in a non-increasing boundary of the model error. Then, a sufficient condition that guarantees the stabilization of the closed-loop system is deduced, which takes the effect of sample-and-hold implementation and the error boundary of the PWL model into account. By this condition, an optimization problem to update the Lyapunov controller for relaxing the constraints of MPC is proposed. We prove that, by the proposed method, the states will eventually converge to a small region around the equilibrium, both this small region and the conservatism of MPC decrease with the increasing of the accuracy of the PWL model. The application of the proposed method to a chemical process demonstrates the effectiveness of the proposed method. Note to Practitioners—The control problem of nonlinear continuous systems with unknown dynamics has attracted the attention of researchers. This article develops an Adaptive MPC for this kind of system, which updates the boundary of uncertainties according to the measurement, and updates the constraints of MPC according to the updated uncertainty boundary. It provides a guaranteed stabilization and continuously relaxed constraint design for MPC where machine learning-based models are employed to predict the future trajectory. In the process of implementation, the method solves two optimization problems: one is to get the parameters of constraints which is based on the PWL model, and the other is used to optimize the future trajectory with the updated constraints. To proceed, the initial boundary of the uncertainty should be given at the initial instant, the initialization of PWL models and constraints can be configured based on it. The proposed method can be used for tracking control of chemical processes, power systems, robots, etc., where the accurate dynamics are not provided in advance and an improved performance is required.

This paper investigates the consensus with connectivity maintenance problem based on the distributed model predictive control (DMPC). To solve such connectivity-maintaining consensus problem, we propose a novel DMPC-based strategy, this novel strategy can handle the explicit practical input constraints and has a fast convergence speed. In the construction of this novel DMPC-based strategy, according to the requirements of both consensus and connectivity maintenance, we design a novel optimization problem and meanwhile design the terminal ingredients. Based on the designed optimization problem and the designed terminal ingredients, we construct a novel DMPC-based algorithm. It is proved in this paper that this novel algorithm is recursively feasible, and by implementing this algorithm, the connectivity can always be maintained, and the system can finally reach consensus. The simulation results demonstrate the effectiveness of the DMPC-based algorithm proposed in this paper.

Event-triggered (ET) consensus of linear multiagent systems with relative output sensing on undirected graphs is studied. Two output-feedback protocols with static and time-varying coupling strengths, respectively, are proposed, which, different from the existing results in relative output sensing, integrate effective ET strategies to reduce the communication burdens between agents. To ensure the closed-loop consensus, design conditions about the gain matrices, coupling strengths, and event-triggering functions are derived. Zeno behaviors are also shown to be excluded from the triggering process. In addition, recursive algorithms are devised for computing the continuous-time relative signals required by the event-triggering functions, so that continuous monitoring of neighbors is circumvented. Numerical examples finally demonstrate the effectiveness of the proposed design method.

This work presents a learning-based approximation scheme to improve the computational burden of general distributed model predictive control (DMPC). Under the framework of dual decomposition, an independent neural network approximator with rectified linear unit is designed for each subsystem. The primal and Lagrangian dual analysis indicates that this error-containing approximation is a suboptimal solution of the global DMPC optimization problem. In addition, the distributed conditions designed to guarantee the feasibility and stability of global system, which inspired by an explicit-implicit procedure to approximate an MPC law, are derived from an decoupling process using dual decomposition. In cases with infeasible approximator output or the distributed conditions are violated, an backup controller will used to promote the implementation of approximation. The proposed learning-based DMPC approximator with feasibility and stability guarantees is finally employed to a reactor-separator process, and simulation results demonstrate the efficiency and superior performance of proposed strategy.

Federated Semi-Supervised Learning alleviates the necessity for fully labeled data in Federated Learning. However, it does not sufficiently prioritize model privacy or the personalized requirements of clients. To address these concerns, our key idea is to communicate no longer individual model parameters but their black-box models, implying to provide other clients with only an input-output interface of individual models. The communication mechanism is model-agnostic, thereby facilitating adaption to heterogeneous models for each client through customization. Consequently, we propose a framework called Personalized Federated Semi-Supervised Learning with Black-Box Models (B2PFSSL) to enhance the privacy of communication. To prevent negative knowledge transfer due to data heterogeneity, we design a two-stage strategy that filters at both the model and data levels, enabling clients to obtain large training datasets by including more high-quality pseudo-labeled data under conditions of scarce labeled data. The experimental results indicate that B2PFSSL achieves competitive performance while reducing the amount of information exposed during communication. Furthermore, it can foster productive collaboration among diverse model architectures in model heterogeneous Federated Learning.

In this paper, we investigate the problem of reinforcement learning under linear temporal logic (LTL) specifications for Markov decision processes (MDPs) with security constraints. We consider an outside passive intruder (observer) that can observe the external output behavior of the system through an output projection. We assume that the secret of the system is a subset of the initial states. The security constraint requires that the observer can never infer for sure that the agent was initiated from a secret state. Our objective is to learn a control policy that achieves the LTL task while ensuring security. To solve the problem of shaping the reward for reinforcement learning, we propose an approach based on the initial-state estimator and the limit deterministic Büchi automata. We illustrate the proposed approach by a case study of mobile robot example.

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: \textit{resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns.} Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to $11.3\%$ in key metrics.

In this paper, the problem of model reduction for Takagi-Sugeno (T–S) fuzzy systems is studied. The virtual inner disturbance is first constructed to map a potential link between the states of two systems before and after dimensionality reduction. Based on Lyapunov theorem, the H∞ approach is then used to analyze the augmented system with the inner disturbance. The results are unrelated to reduced-order system membership functions (MFs), which can be freely selected. Furthermore, both the number of rules and the size of the reduced-order system and the inner disturbance may be flexibly chosen. An example is adapted to demonstrate the efficiency of the proposed method.

This article proposes a distributed model predictive control (DMPC) strategy for a class of large-scale systems composed of several interacting subsystems. When a certain subsystem is required to be removed or inserted, the topology change of the system network can lead to the infeasibility of interacting local controllers due to the existence of the interactions among subsystems. In this article, the interactions among subsystems are presented as state trajectory estimations of interacting subsystems, and the estimations are involved in each local MPC. To deal with the influence resulted from the change of system topology, optimization schemes for removal and plugging-in are designed and employed in the proposed strategy. They optimize related subsystems’ reference trajectories, which are used to approximate the interacting state trajectories here, to reduce the time it takes to drive the system states and reference trajectories to a region. This region ensures that the system topology change can be conducted with all controllers having feasible solutions. The proposed DMPC algorithm has the following characteristics: 1) all the optimization problems in each MPC are solved in a noniterative manner and each controller only communicates with its neighbors and 2) it guarantees the feasibility of all controllers throughout the topology change process and the convergence of the system after the topology change. Simulation results show the effectiveness of the proposed DMPC algorithm.Note to Practitioners—The DMPC algorithm proposed in this article is designed for networked systems where certain subsystems may be removed or inserted. The purpose is to obtain a fast response to this topology change of system. An integrated algorithm is provided for the application of the strategy. First, the parameters of the MPCs are initialized. Then, before the command of a switch in topology is given, local controllers for normal situations provide the real-time inputs. Once the switch of topology is commanded, additional optimization problems are solved in the related subsystems’ controllers to guarantee the feasibility of removal or plugging-in operation. The optimization problems involved in each MPCs can be solved by adopting efficiently quadratic programming solvers supported by MATLAB. Note that although the topology change is commanded, all the subsystems are still operated normally until the topology change is conducted. In general, the strategy introduced in this article can be applied to control a class of large-scale networked systems, where each subsystem-based controller can exchange information with its neighbors.

Graph-based semi-supervised learning (GSSL) studies the problem where in addition to a set of data points with few available labels, there also exists a graph structure that describes the underlying relationship between data items. In practice, structure uncertainty often occurs in graphs when edges exist between data with different labels, which may further results in prediction uncertainty of labels. Considering that Gaussian process generalizes well with few labels and can naturally model uncertainty, in this paper, we propose an Uncertainty aware Graph Gaussian Process based approach (UaGGP) for GSSL. UaGGP exploits the prediction uncertainty and label smooth regularization to guide each other during learning. To further subdue the effect of irrelevant neighbors, UaGGP also aggregates the clean representation in the original space and the learned representation. Experiments on benchmarks demonstrate the effectiveness of the proposed approach.

This paper studies the design of adaptive event-triggered protocols for consensus of linear multi-agent systems (MASs) with external disturbances. Different from most of the existing results that deal with undirected graphs, this paper addresses directed graphs, specifically graphs that are assumed to be strongly connected. Inspired by a recent development [1], this paper devises novel adaptive event-triggered protocols for linear MASs with all agents subject to external disturbances. Two specific designs of composite triggering conditions are analysed and discussed. Compared with the disturbance-free case, additional constraints need to be introduced to deal with external disturbances and moreover the time-dependent terms are allowed to take any finite functions, rather than a class of L1 functions.

With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply-demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.

This paper focuses on a two-stage framework for economic optimization to maximize the profits of electric-vehicle (EV) microgrid. In the first stage, an economic optimization problem at the day-ahead time scale is solved to determine the power purchased from load serving entities (LSE), and make an optimal price decision (parking fee and charging fee) while considering with the EVs uncertainties. In the second stage, a real-time model predictive control strategy is proposed to meet the EVs requirement and minimize the operation cost. Through two-stage scheduling, EV microgrid can guarantee long-term safe and efficient operation, while ensuring maximum benefits. The simulation results show that the proposed method in this paper can provide reliable power supply to the EVs, increase the EV microgrid revenue and ensure the safe operation of the EV microgrid system.

Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems' MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method.

For a large-scale distributed system, distributed model predictive control (DMPC) is a method of choice because of its ability to explicitly accommodate constraints and to achieve good dynamic performance. In the design of a DMPC, guaranteeing stability with a strong global performance is known to be a challenge. In this paper, we consider a large-scale distributed system whose input is constrained to given sets in their respective spaces and propose a stabilizing DMPC design, where each subsystem-based model predictive control (MPC) optimizes the cost function of the entire system over the region it directly impacts on. Consistency constraints and stability constraints, which bound the estimation errors of the interaction sequences among subsystems, are designed to guarantee that, if an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and that the closed-loop system is asymptotically stable. A key feature of the proposed DMPC is that it coordinates the MPCs of the subsystems by redefining the impact region of a subsystem according to the coordination strategy. Simulation results show that the performance of the proposed DMPC is very close to that of a centralized MPC.

Good performance of a controller in Model Predictive Control (MPC) system keeps the whole industrial process running well. Because of the complexity of the process, data-driven performance assessment approach, instead of model approach, becomes a popular topic. However, performance assessment is inaccurate when operating condition changes, because the performance benchmark should be different. This paper proposes an overall index to classify different operating conditions of real-time dataset. This index is the sum of two similarity factors by adding a weight value. One is the Principal Component Analysis (PCA) similarity factor and another is Bhattacharyya distance similarity factor. This index, considering both characteristic and spatial distance of datasets, identifies the operating condition that the real-time data belongs to. The effectiveness of this index is demonstrated in the case of simulation.

Walking on irregular terrain is usually a common task for a quadruped robot. It is however difficult to control the robot in this situation as undesirable impulse force by collision between the foot of robot and obstacles makes the robot unstable. This paper presents a Posture Feedback Compensation Controller (PFCC) for a quadruped robot with high payload walking on irregular terrain. In order to make the robot walk stably and fast on irregular terrain, we choose trotting gait for walking. The foot trajectory is scheduled based on the Bezier curve method in order to improve the stability of quadruped robot. Simulations of walking on irregular terrain have been performed. The results have verified that the proposed methods have better stability and higher speed for walking on the irregular terrain.

To tackle the twin challenges of sustainable energy supply and climate change, numerous efforts have been made to decarbonize the whole energy systems. Control Engineering, which concerns the automated operation of a machine or system to achieve desired target(s) and to avoid unstable or unintended disruptive behavior, has played a key role in modern industry and across the whole energy system. Advanced control technologies, such as optimal control, provide a framework to simultaneously regulate the system performance and limit control energy. However, little has been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper for the first time investigates the correlation between control energy consumption and system performance, and shows that this correlation is nonlinear and the controller design should be a delicate synthesis procedure to achieve better trade-off between system performance and energy saving.

A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.

This paper focuses on the stability analysis of T-S fuzzy control systems. The artificial T-S model method is utilized with piecewise linear interpolations into the normalized fuzzy membership functions. The stability conditions are derived to a series of LMIs. Using piecewise linear interpolation functions, we obtain finite LMIs and only solve them at the interpolation points. Furthermore, because of the dramatically improved approximation accuracy of piecewise linear interpolations, the method presented in this paper can provide a wider stable region for T-S fuzzy control systems, compared with the approach with staircase (zero-order) interpolations. A simulation example is adopted to illustrate the advantage of the proposed method.

In accelerated & controlled cooling (ACC) process, the relationship between plate point's temperature and the manipulated variable, plate velocity, is complicate and nonlinear with a time delay. A novel Model Predictive Control (MPC) is developed for precise control of the plate point's final temperature (FT) with fast computational speed. The control objective of time-temperature curve (plate temperature evolution) is converted into a space distribution of plate temperature along cooling line, which simplifies the ACC model to a linear model then does beneficial to employ MPC directly for optimizing the FT of plate points. The predictive horizon and control horizon of MPC is reduced to one or two sample time since the space distribution of plate temperature contains the future information of plate point's temperature, then dramatically reduce the computational burden. The simulation result shows the efficiency of the proposed method.

An optimal point-wise control method for parabolic distributed parameter systems is proposed to solve the problem of determining both the locations of point-wise controllers and the control which should be exerted on each controller. For a given number of point-wise controllers, the optimal point-wise control form is given by solving a quadratic cost control problem, and the controller locations and the control are determined by minimizing the quadratic control cost performance index. The result indicates the method proposed is effective at solving the optimal point-wise control problem for parabolic distributed parameter systems.

In actual industrial processes, the measurement data always contain noise. Therefore, it will affect the accuracy of modeling. Compare to type-1 fuzzy sets, the membership functions in type-2 fuzzy sets include primary membership function and secondary membership function. It provides additional degrees of freedom that make it possible to model uncertainties brought by the noise. In this paper, a type-2 T-S fuzzy model is presented to minimize the effect of measurement noise. Furthermore, the influence of the initial conditions is considered in the algorithm. The primary membership function is gained through an improved nearest-neighborhood clustering algorithm, and the secondary membership function is determined through GMM based on the sufficient statistics. The orthogonal least-squared algorithm is used to identify the consequent of the fuzzy rules. Finally, the simulation results are compared with those obtained from a type-1 T-S fuzzy modeling results and the superiority of the proposed approach is highlighted.

In this paper, a new fully adaptive feedforward decentralized controller is developed for 6 degree of freedom (6DOF) parallel robot. This method makes the position error and velocity error converge to zero asymptotically. Theoretical analysis and simulation results are presented to illustrate the proposed approach. The controller parameter tuning method is also proposed.

A novel three-dimensional fuzzy logic controller (3D FLC) is presented for controlling the spatio-temporal systems, with the help of three-dimensional (3D) fuzzy sets and inference logic. The analytical model of the 3D FLC is derived to disclose its working principle and guide the control design. The derived model show that the 3D FLC has a global sliding mode structure over the spatial domain, which explains why the 3D FLC is able to process spatial information more effectively with a few more sensors. Based on its sliding mode feature, the 3D fuzzy logic control system can be analyzed and designed in the sense of Lyapunov stability. Finally, a catalytic reactor is presented as an example to validate the effectiveness of 3D FLC.

An interactive satisfying method based on alternative tolerance is presented for the multiple objective optimization problem with fuzzy parameters. Using the alpha-level sets of the fuzzy numbers, all the objectives are modeled as the fuzzy goals, and the tolerances of the objectives are iteratively changed according to a decision maker for a satisfying solution. Via a specific attainable point programming model, the membership functions can be modified, and then, a lexicographic two-phase programming procedure is constructed correspondingly to find the final solution. In a special case, the objective constraint is added instead of changing the membership functions; therefore, the dissatisfying objectives for the decision maker can be improved step by step. The presented method not only acquires the alpha-Pareto optimal or weak alpha-Pareto optimal solution of the fuzzy multiple objective optimization, but also satisfies the progressive preference of the decision maker. A numerical example shows its power.

A novel 3-D fuzzy logic controller (3-D FLC) was presented to control a class of spatially distributed dynamic systems by Li(IEEE Trans. Fuzzy Syst., vol. 15, no. 3, pp. 470–481, Jun. 2007) by utilizing a 3-D fuzzy set and an inference mechanism with 3-D nature for spatial information processing. In this paper, the analytical mathematical model of the 3-D FLC is derived, and the controller structure is explained with the help of the existing conventional control techniques. The graphic analytical method for the traditional two-term FLC can be used for the analytical model derivation. The derived result shows that the 3-D FLC has a global sliding-mode structure over the spatial domain and explains why the 3-D FLC is able to process spatial information more effectively than its traditional counterpart using a few more sensors. Because of its sliding-mode feature, the Lyapunov stability criterion can be developed easily to analyze and design the 3-D FLC. Finally, a catalytic reactor is presented as an example to demonstrate the effectiveness of the 3-D FLC as compared with other controllers.

An interval-valued fuzzy logic controller (I-V FLC) is presented to control a class of nonlinear distributed parameter systems. The proposed FLC is inspired by human operators' knowledge or expert experience to control a distributed parameter process from the point of view of overall space domain. Based on spatial fuzzy set, the I-V FLC employs a centralized rule base over the space domain. Using spatial membership degree fusion operation, the I-V FLC can compress spatial input information into interval-valued fuzzy sets and then execute an interval-valued rule inference mechanism; thereby the I-V FLC has the capability to process spatial information over the space domain. Compared with traditional FLCs, the I-V FLC can improve its control performance due to its increased ability to express and process spatial information. The I-V FLC is successfully applied to a catalytic packed-bed reactor and compared with the traditional FLCs. The results demonstrate its effectiveness to control the unknown nonlinear distributed parameter process.

This paper presents a two-step interactive satisfactory optimization method for fuzzy multiple objective optimization with preemptive priorities. In contrast to previous works, this proposed approach guarantees the order of satisfactory degrees consistent with priorities. The decision-maker not only acquires satisfactory solution of all the objectives, but also realizes the preemptive priority requirement among them. The originally complex optimization problem is simplified and divided into two subproblems that are solved in sequence. Numerical examples and actual application show the proposed method's effectiveness, flexibility and efficiency in comparison with results from the literature.

The traditional fuzzy set is two-dimensional (2-D) with one dimension for the universe of discourse of the variable and the other for its membership degree. This 2-D fuzzy set is not able to handle the spatial information. The traditional fuzzy logic controller (FLC) developed from this 2-D fuzzy set should not be able to control the distributed parameter system that has the tempo-spatial nature. A three-dimensional (3-D) fuzzy set is defined to be made of a traditional fuzzy set and an extra dimension for spatial information. Based on concept of the 3-D fuzzy set, a new fuzzy control methodology is proposed to control the distributed parameter system. Similar to the traditional FLC, it still consists of fuzzification, rule inference, and defuzzification operations. Different to the traditional FLC, it uses multiple sensors to provide 3-D fuzzy inputs and possesses the inference mechanism with 3-D nature that can fuse these inputs into a so called ldquospatial membership function.rdquo Thus, a simple 2-D rule base can still be used for two obvious advantages. One is that rules will not increase as sensors increase for the spatial measurement; the other is that computation of this 3-D fuzzy inference can be significantly reduced for real world applications. Using only a few more sensors, the proposed FLC is able to process the distributed parameter system with little complexity increased from the traditional FLC. The 3-D FLC is successfully applied to a catalytic packed-bed reactor and compared with the traditional FLC. The results demonstrate its effectiveness to the nonlinear unknown distributed parameter process and its potential to a wide range of engineering applications.

This work presented a PI type fuzzy controller considering the error and the change rate of error. Based on this structure, we work out a method to tune the parameters of the PI type fuzzy controller online. In order to improve further the performance of the fuzzy controller, the optimization control supervised the under level controller by minimizing a generalized predictive control criterion. A simple and sufficient bound condition of input and output variable is achieved under the Lyapunov theory through the brief analysis. Simulation results are made to demonstrate the fine performance of these novel fuzzy controller structures.

This paper presents a new control loop configuration criterion for multivariable processes. For an arbitrary loop, four cases corresponding to different combinations of open and closed states with the remaining loops are investigated. A new interaction measure, which is able to provide comprehensive description of interaction among loops, is proposed to evaluate the loop-by-loop interaction. Consequently, a new loop-pairing criterion based on the new interaction measure and the algorithm for determining loop pairings which results minimum loop interactions in terms of interaction energy are proposed. The main contribution of the work is that it systematically analyzed the loop interaction from all aspects and derived a feasible solution for the problem. Usefulness of the proposed criterion is illustrated using case study.

A novel suboptimal control design is proposed for a class of multi-input nonlinear cascade systems. Combining the backstepping recursive design with the SDARE technique, the analytic solution to suboptimal control for each subsystem can be online derived by designing state feedback control law. The resulting SDARE control law can make the original closed-loop system globally asymptotically stable (GAS). Furthermore, the designed control law is the optimal solution to the original system when the terminal time is equal to infinity. Finally, a numeric simulation example of the second-order system with two inputs is illustrated to verify the effectiveness of the backstepping based optimization design.

This paper proposes a generalized varying-domain optimization method for fuzzy goal programming incorporating multiple priorities. According to the three possible styles of the objective function, the varying-domain optimization method and its generalization are corresponding proposed. In contrast to the previous method, the proposed method can make that the higher priority achieving the higher satisfaction degree. In this way, the decision-maker can get the optimal solution as well as guarantee the priorities of the multiple objective optimization problem. We demonstrate the power of this proposed method by three illustrative examples and a practice application.

This paper presents the new development of the boiler-turbine coordinated control system using fuzzy reasoning and auto-tuning techniques. The boiler-turbine system is a very complex process which is a multivariable, nonlinear, slowly time-varying plant with large settling time and a lot of uncertainties. A special subclass of fuzzy inference systems, called the GPE(Gaussian partition with evenly spaced midpoints) systems, is used to self-tune the main steam pressure PID controller's parameters on-line based on the error signal and its first difference, aimed at overcoming the uncertainties due to changing fuel calorific value, machine wear, contamination of the boiler heating surfaces and plant modeling errors. For the large variation of operating condition, a supervisory control level has been developed by auto-tuning technique. Satisfactory industrial application results show that such a control system has enhanced adaptability and robustness to the complex process, and better control performance and high economic benefit has been obtained.

From the process control point of view, it is difficult to find out the optimal parameters for the control system based on the single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. First, M control actions are obtained by an unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints can then be calculated and the global satisfactory degree is obtained by fuzzy inference. Moreover, the weighting coefficient /spl lambda/ in the cost function is tuned using simulation-optimization according to the fuzzy criteria.

This paper investigates the use of fuzzy decision making in predictive control, the use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. By defining the membership degree of the control objective and system constraint, and using the fuzzy interference, the optimal control problem with constraint, multi-objective multi-degree of freedom can be transferred as a convex optimal problem, so as to utilize the efficient optimal algorithm and guarantee the global optimal solution. More importantly, we can increase the freedom degree of control by adjusting the relevant membership degree parameters of control objective and system constraints. The designer's experience of control objective and system constraint can be utilized through the fuzzy inference of language variables, thus can get better understanding of effect for control performance.

A fuzzy satisfactory clustering algorithm is presented in this paper. It starts with two cluster centers and increases a new center if necessary. During the clustering process, the former clustering information is fully used so that the convergence rate can be speed up. A system data set can be quickly divided into several satisfactory fuzzy clusters by this algorithm. A Takagi-Sugeno type fuzzy model can then be identified. For three typical pH processes, satisfactory simulation results are obtained. The effective performance of the modified clustering algorithm is quantitatively evaluated.

This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Both equality and inequality constraints can be handled in a unified form, i.e., fuzzy soft constraints. Thus, the traditional constraints predictive control can be transferred to a standard fuzzy optimization problem. An inexact approach is used in this paper to obtain the fuzzy satisfaction optimal solution, instead of finding an exact unique optimal solution. A family of inexact solution with acceptable membership degree are found. Compared to the standard quadratic objective function, with the fuzzy decision making approach, the designer has more freedom in specifying the desired process behavior. Simulation results show the improvement of this approach when taking into account the constraints on the control or output signals.