Search

This article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tunnel boring machines (TBMs) are usually the first choice for tunneling construction with its advantages on high safety, time saving, and less operators. Cutterhead system is an important component for TBMs since it is used to excavate rocks and soil. It is difficult to guarantee both the boring efficiency and energy saving under the excavating rock disturbances and the constraints on the driving motors in TBMs by manual operation. To deal with this problem, it is necessary to develop advanced control algorithms for the cutterhead system. Thus, we investigate an economic model predictive control (EMPC) structure for cutterhead system in TBMs. A generalized nonlinear dynamic model of TBM cutterhead system is built based on the first principle method. An economic cost is constructed with the boring efficiency and energy cost to evaluate the tunnel construction quality. EMPC algorithms are designed to optimize the constructed economic cost for a cutterhead system to guarantee good economic performance. It is shown that EMPC can improve the economic performance of the cutterhead system. [ABSTRACT FROM AUTHOR]

As digital communication and storage continue to expand, the protection of image privacy information becomes increasingly critical. To safeguard sensitive visual information from unauthorized access, this paper proposes a novel image encryption scheme that integrates multiobjective Artificial Bee Colony (ABC) optimization algorithm and DNA coding. Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics. [ABSTRACT FROM AUTHOR]

• A systematic subsystem decomposition procedure for complex process systems for state estimation. • Three different distributed state estimation algorithms with detailed examples. • Extensive simulation results based on three commonly used distributed state estimation algorithms. In this work, a systematic subsystem decomposition procedure for complex process systems is proposed for distributed state estimation. In the proposed procedure, both the connectivity within a system and the strength of connections are taken into account. A nonlinear process is first presented by a directed graph in which a node denotes either a state or a measured output of the nonlinear process. In the directed graph, nodes are connected via weighted edges and the weights reflect the strength of connection between two nodes. Based on the weighted directed graph, community structure detection is used to divide all the variables into smaller groups, such that the intra-connection within each group is made much stronger than the interaction among different groups. Subsystem models that are appropriate for distributed state estimation are configured based on the variables assigned to the groups. A comparative study between the proposed procedure with a procedure based on unweighted directed graph is conducted through extensive simulations. In the simulations, a numerical example and a chemical process example are considered. Three commonly used distributed state estimation algorithms designed based on the two decomposition procedures are applied to the two examples. A few guidelines on when to use weighted graphs for decomposition for distributed state estimation are concluded. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed model predictive control (MPC) of polytopic uncertain systems with quantised communication and packet dropouts. The model of the whole plant is divided into a certain number of incomplete subsystems. Due to the nature of the distributed control structure, there is generally a lack of information about the state of the overall system. Each subsystem shares its information with neighbour subsystems via reliable connection. Distributed MPC controllers are designed for each subsystem by solving the linear matrix inequalities optimisation problem. The distributed state feedback laws are quantised and transmitted via communication network. An iterative algorithm is presented to make coordination among distributed state feedback laws. The communication is assumed to be affected by random packet dropouts in a representation of Bernoulli distributed white sequences with known conditional probabilities. A case study is carried out to demonstrate the effectiveness of the proposed distributed MPC technique. [ABSTRACT FROM PUBLISHER]

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

Although distributed model predictive control (MPC) has received significant attention in the literature, the robustness of distributed MPC with respect to model uncertainties and state delays has not been explicitly addressed. In this paper, a novel approach to design robust distributed MPC is proposed for polytopic uncertain systems with state delays. The algorithm requires decomposing the entire system into M subsystems and solving M linear matrix inequality optimization problems to minimize an upper bound on a robust performance objective for each subsystem. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed controllers. The algorithm is a flexible structure of robust control, which allows the independent computation of the state feedback laws for the subsystems. Convergence and robust stability of the proposed distributed MPC are analysed. Two numerical examples are carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on‐line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article investigates the reduced‐order interval observer (R‐IO) design technique for continuous‐time linear systems with unknown external disturbances and measurement noises. First, we propose a coupled R‐IO structure with more design degrees of freedom, and it can be directly applied not only to solve the difficulty of the error system cooperativity construction but to relax the constraint on the sensor measurement noises. Second, the R‐IO existence condition is derived as a set of matrix equations (MEs), and a complete solution, explicitly showing the available design parameters, to such an R‐IO is further obtained by solving the MEs. Third, using the solution, an integrated optimization indicator of the R‐IO performance is built as the valid selection mechanism of these parameters. Finally, the efficiency of the obtained results is illustrated through a numerical example and a practical example. [ABSTRACT FROM AUTHOR]

Highlights: [•] A robust distributed MPC algorithm for polytopic uncertain systems was developed. [•] Less conservative results were obtained for system subject to actuator saturation. [•] Computational complexity and time cost were reduced using distributed control. [•] Trade-off between computational time and performance was studied. [Copyright &y& Elsevier]

In this paper, a stochastic distributed model predictive control (DMPC) architecture is investigated for cyber–physical systems subject to probabilistic input saturations. We present a systematic approach to design the stochastic DMPC by extending the deterministic DMPC. The input saturation constraints are relaxed and characterized by a Bernoulli-distributed white sequence, which can provide a trade-off between the satisfaction of input saturations and control performance. The probabilistic input constraints are transformed into a convex hull of linear feedback laws, therefore, can make the controller design less conservative. Then, the optimization problem of the stochastic DMPC is designed and cast into solving an online optimization problem. The probabilistic input saturations are dealt with a distributed fashion by developing an iterative algorithm of the DMPC. The recursive feasibility and closed-loop stability subject to probabilistic input saturations are given. Finally, a numerical example and an industrial electric heater system are used to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed model predictive control (DMPC) for positive systems with interval and polytopic uncertainties, respectively. Different from the traditional quadratic DMPC, a new linear DMPC framework is established for positive systems. First, a linear cooperative cost function (performance index function) is introduced for the DMPC of positive systems. Accordingly, some linear constraint conditions are imposed on the systems. Then, a linear co-positive Lyapunov function is constructed. Using the linear Lyapunov function, the DMPC controllers are designed for interval positive systems and polytopic positive systems, respectively. The controller of each subsystem contains not only the state information of the subsystem but also the state information of other correlated subsystems. Linear programming based optimization algorithm is addressed to compute the controller parameters. To guarantee the feasibility and stability of the systems, a cone invariant set is proposed. Finally, two case studies are provided to illustrate the effectiveness of the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper presents a novel deoxyribonucleic acid (DNA) network encryption method combined with a hyperchaotic system for image encryption. The DNA network encryption algorithm contains 12 different DNA operations, which overcomes the limitations of existing DNA encryption schemes that rely on limited fixed DNA operation types. Firstly, secure hash algorithm 256-bit (SHA-256) is employed to generate secret keys associated with the plaintext. Secondly, the hyperchaotic system is employed to derive key streams with high plaintext sensitivity. Thirdly, the DNA encoding, DNA network encryption and decoding are performed sequentially to encrypt the plaintext image using the keystreams. Finally, an additional row–column disruption is executed to strengthen the resistance of the encryption scheme to attacks. Security analysis reveals that the information entropy of our cipher image reaches 7.9975 and the average pixel correlation coefficients are below 0.01, implying that our encryption algorithm has excellent security. [ABSTRACT FROM AUTHOR]

This paper addresses the explicit synchronisation of heterogeneous dynamics networks via three-layer communication framework. The main contribution is to propose an explicit synchronisation algorithm, in which the synchronisation errors of all the agents are decoupled. By constructing a three-layer node model, the proposed algorithm removes the assumptions that the topology is fixed and the synchronisation process is coupled. By introducing appropriate assumptions, the algorithm leads to a class of explicit synchronisation protocols based on the states of agents in different layers. It is proved in the sense of Lyapunov that, if the dwell time is larger than a threshold, the explicit synchronisation can be achieved for closed-loop heterogeneous dynamics networks under switching topologies. The results are further extended to the cases in which the switching topologies are only frequently but not always connected. Simulation results are presented with four single-link manipulators to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper studies the joining consensus of networked multi-agent systems subject to nonlinear couplings and weighted directed graphs via pinning control. A weighted-average consensus protocol is proposed to achieve the collective decision by interacting with the local information of some pinned agents. By proposing a novel joining consensus protocol, average consensus and general consensus strategies are joined to achieve an agreement for the weighting networked system. Furthermore, by calculating a proper consensus gain and using finite control Lyapunov controllers, an efficient joining consensus protocol is presented to improve the consensus speed. Sufficient conditions for achieving the consensuses asymptotically are proved. Finally, theoretical results are validated via simulations. [ABSTRACT FROM PUBLISHER]

This paper considers the robust adaptive consensus tracking for higher-order multi-agent uncertain systems with nonlinear dynamics via distributed intermittent communication protocol. The main contribution of this work is solving the robust consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. The focus is the problem of actuator with occasional failure inputs and communication resources constraints. A novel distributed intermittent communication framework is proposed via adaptive approach. In this framework, the underlying communication topologies switch among several directed graphs with a limited directed spanning tree rooted at a leader agent. Furthermore, by introducing a strategy of actuator fault compensation inputs, a combination of robust consensus tracking protocol is designed by the different adaptive feedback controllers. It is proved that the robust adaptive consensus tracking can be achieved by using local states information of neighboring agents if the communication retention rate condition is satisfied. Two examples are presented to demonstrate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To deal with the problem of conflicting requirements that cannot be satisfied by only a single LTI controller, this paper focuses on the design of a switching controller, which includes several stabilising linear time-invariant (LTI) controllers designed independently with different control performance criteria, for a specific LTI plant with a single I/O delay. The switching controller design procedure is divided into three steps. First, using simple loop shifting arguments, the design problem is reducible to an equivalent delay-free one. Second, traditional LTI controller synthesis methods could be considered independently for the delay-free plant. Third, based on a quadratically stable state space realisation method, a Youla parameter including a switching strategy with these controllers is designed to guarantee different requirements. Finally, a numerical example is provided to demonstrate the validity and efficiency of the approach. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of designing a switching controller, which includes several linear time-invariant (LTI) controllers designed beforehand and independently for a specific LTI plant with corresponding H2 control performance criteria. It is possible to find suitable state space realizations for any given family of controller transfer matrices, which guarantee not only certain H2 performance of the overall closed-loop system under arbitrary switching but also the corresponding H2 performance of local subsystems at each switching point. The effectiveness of the proposed method is demonstrated with a numerical example. [ABSTRACT FROM AUTHOR]

This study addresses the problem of weighted L2 performance controller parameterisation for a plant switching among a finite family of given configurations. It is assumed that switching is a piecewise constant function of time and obeys an average dwell time switching logic. With a multiple Lyapunov functions‐based technique, a dynamical switching output‐feedback controller is solved with Riccati inequalities or linear matrix inequalities formulation. Furthermore, based on the controller construction, there exists a controller parameterisation, which guarantees that the closed‐loop switching linear system with the average dwell time switching scheme achieves certain weighted L2 controlled performance. [ABSTRACT FROM AUTHOR]

This study is concerned with the stability and stabilisability problems of networked control systems (NCSs) with partly quantised information. More precisely, the remote state variables transported from other sub‐systems experience different network‐induced time delays and quantisation errors, whereas the local state variables do not. This consideration is much more natural in NCSs because of the distributive nature of sensors and controllers, even systems. Network‐induced time delays are introduced as a Markov chain and quantisation errors are represented as convex polytopic uncertainties. Based on the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of a quantised robust H∞ output feedback controller for NCSs are presented. Such conditions are obtained in terms of bilinear matrix inequalities (BMIs). Furthermore, a cone complementarity algorithm is utilised to convert these BMIs into a convex optimisation problem. Finally, a simulation example is provided to demonstrate the efficiency of proposed theorems. [ABSTRACT FROM AUTHOR]