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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.

Precise measurement of tailor-welded blanks is crucial for quality control of laser welding. The difficulty in measuring the seam profile of similar gage tailor–welded blanks lies in lacking of solutions to locate their feature points such as those representing the laser stripe. A laser sensor–based method is proposed to measure tailor-welded blanks based on its laser stripe and texture features, from which the proposed algorithm is employed to extract the feature points for seam profile assessment. The algorithm is evaluated on a laser-based weld seam measurement system, the experiment results show that the proposed method measures the tailor-welded blanks with high accuracy and is suitable for online inspection.

This paper studies the sliding mode control for T-S fuzzy systems in the framework of finite-time boundedness. It is assumed that the control signals are transmitted via vulnerable channels, where injection attacks might happen. A fuzzy sliding mode controller is firstly synthesized to guarantee the finite-time reachability of the prescribed sliding surface and attenuate the effect of the injection attacks. By introducing a partitioning strategy, the finite-time boundedness over both the reaching phase and the sliding motion phase are analyzed. Furthermore, sufficient criteria are derived such that the closed-loop system is finite-time bounded over the whole specified finite-time interval despite of the injection attacks. An optimal algorithm is further provided for searching ideal control gains with fewer energy demands. Finally, a simulation example verifies the proposed sliding mode control approach.

Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs, a Recurrent Denosing Autoencoder (RDAE) is built to extract meaningful features and reduce input dimension in the spatial domain, and Cumulative Percent Variance (CPV) is introduced to decide the number of extracted features. Considering correction and prior knowledge effects of real history outputs, a Weighted Auto Regressive Long Short Term Memory (WAR-LSTM) structure is proposed as the basic cell, then multiple WAR-LSTMs are stacked as deep WAR-LSTM to extract high level representations from multi variables. Hence, spatial and temporal information are sufficiently used both in extracting features and building model. The benchmarked Tennessee Eastman process data and real process data from a Fluid Catalytic Cracking (FCC) unit verify the effectiveness of our work.

Traditional control system design to complex networks is generally implemented by integrated structural analysis aiming at a global network. However, such a global method may be inefficient, in particular, when a massive network with a huge number of nodes and associations is considered. In this paper, motivated by the idea of “dividing and dealing”, we propose a block-based approach to the issue of minimum input design for structural controllability (MIDSC) of complex networks that potentially incurs in higher efficiency. Specifically, we consider a large-scale networked system that consists of several local blocks. The main challenge for control configuration design of this class of systems is how to find the minimum inputs of global network according to the local block information while maintaining system’s structural controllability. To this end, two block-based graphical algorithms are developed to meet the conditions required for achieving structural controllability, and meanwhile determine an optimal solution for addressing the MIDSC problem. The complexity of the proposed method is analyzed, which is also compared with existing algorithms designed mainly based on monolithic model. In particular, we show that, under some mild conditions on blocking structure, the complexity of the proposed algorithm is strictly lower than that of existing algorithms to the MIDSC problem.

This paper is concerned with the security control problem for a class of Markov jump systems subject to false data injection attack and incomplete transition rates. An on-line estimation strategy is provided for the time-variant and unknown cyber-attack modes. And then, an adaptive sliding mode controller is synthesized with different robust terms for different modes to guarantee the reachability of the specified sliding surface. Moreover, the sufficient conditions for the stability of the closed-loop systems are derived. Finally, it is shown from simulation results that the effect of both false data injection attack and incomplete TRs can be effectively attenuated by the present adaptive SMC method.

This paper investigates the event-triggered distributed predictive control (DPC) problem for multi-agent systems subject to bounded disturbances. A novel event-triggering mechanism which involves the neighbours’ information is derived for each agent to achieve a trade-off between resource usage and control performance. In such a framework, the DPC optimization problem is solved and information is exchanged only at triggering instants, thus achieving asynchronous coordination. To lower computation and communication consumption more significantly, a dynamic variable considering effects of neighbours is introduced to design a dynamic event-triggering condition and we show that larger inter-execution time can be obtained using the dynamic triggering mechanism. The theoretical conditions on ensuring feasibility and closed-loop stability are developed for these two triggering mechanisms, respectively. Finally, numerical simulations are given to illustrate the effectiveness of the proposed control strategy.

The adaptive sliding mode control (SMC) problem is addressed for singular semi-Markovian jump systems (S-MJSs) against actuator attacks, in which the transition rates rely on the random sojourn time and are not constant, and the system states are unavailable. Moreover, the vulnerability of control signals transmitted via communication network means that the actuators may receive the attacked control signals. For the sake of reducing the effect of actuator attacks, the neural network technique is used to approximate the false information injected by adversaries. Meanwhile, a sliding mode observer is introduced to estimate the unmeasured states. An adaptive SMC law is proposed to guarantee that the estimation states and errors can reach to the sliding surfaces, and the stochastic admissibility of the singular S-MJSs can be ensured. In the end, an example is applied to illustrate the method in this paper.

This paper addresses the problem of sliding mode control for discrete-time interval type-2 fuzzy system subject to fading channels. The multiplicative fading model is established for reflecting the fluctuation of the transmitted signals. Meanwhile, the membership function-dependent sliding function involves the fading coefficient. By using the membership functions depending on the fading signals, the mismatched interval type-2 fuzzy sliding mode controller is designed such that the state trajectories can be driven into the neighborhood of the specified sliding surface. Moreover, the closed-loop fuzzy system is ensured to be mean-square asymptotically stable. Finally, the proposed control scheme is verified by a numerical example.

This paper proposes an observer-based event-triggered adaptive dynamic programming (ADP) approach for the nonlinear system with unknown disturbances to efficiently reduce computational cost. Under the event-triggered mechanism, an observer is constructed to identify unknown disturbances and ensure the estimation error to be ultimately uniformly bounded(UUB). Next, an adaptive triggering condition related to the estimation error of disturbances is derived with the help of a modified performance index function. Then, the event-based control algorithm is implemented based on a single critic neural network(NN) structure to approximate the optimal control law. Moreover, the stability analysis for the closed-loop system is presented with the event-driven control law and the weight estimated error of the critic NN is proved to be UUB. Finally, simulation results illustrate the effectiveness of the proposed control scheme. Compared with the traditional ADP approach, the proposed event-triggered strategy can reduce controller updates with guaranteed performance of the system.

In this work, the problem of asynchronous sliding mode control (SMC) is investigated for a class of uncertain Markovian jump systems (MJSs) with time-varying delays and stochastic perturbation. It is assumed that the system modes cannot be obtained synchronously by the controller, but instead there is a detector that provides estimated values of the system modes. This asynchronous phenomenon between the system modes and controller modes will be described in this work via a hidden Markov model with partly accessible mode detection probabilities . Based on a common sliding surface, an asynchronous SMC law depending on the detector mode is synthesized to ensure the mean square stability of the sliding mode dynamics and the reachability of the specified sliding surface simultaneously. Moreover, a design algorithm for obtaining the asynchronous SMC law is established. Finally, an application of the automotive electronic throttle system is provided to illustrate the effectiveness and advantages of the proposed asynchronous sliding mode control approach.

Summary This paper studies the coordination control problem of stabilizing large-scale dynamically coupled systems via a novel event-triggered distributed model predictive control (DMPC) approach. In order to achieve global performance, certain constraints relevant to the triggering instant are imposed on the DMPC optimization problem, and triggering mechanisms are designed by taking into account coupling influences. Specifically, the triggering conditions derived from the feasibility and stability analysis are based on the local subsystem state and the information received from its neighbors. Based on these triggering mechanisms, the event-triggered DMPC algorithm is built, and a dual-mode strategy is adopted. As a result, the controllers solve the optimization problem and coordinate with each other asynchronously, which reduces the amount of communication and lowers the frequency of controller updates while achieving global performance. The recursive feasibility of the proposed event-triggered DMPC algorithm is proved, and sufficient parameter conditions about the prediction horizon and the triggering threshold are established. It shows that the system state can be gradually driven into the terminal set under the proposed strategy. Finally, an academic example and a realistic simulation problem to the water level of a 4-tank system are provided to verify the effectiveness of the proposed algorithm.

This work addresses the asynchronous output feedback control for time-varying Markovian jump systems. The parameter uncertainties enter the system in random ways according to a determined mode-dependent Bernoulli distributed white sequence. The non-synchronization phenomenon between the system modes and controller modes is modeled as a hidden Markov model. By means of intensive stochastic analysis and recursive matrix inequality techniques, sufficient conditions are attained to ensure the finite-time stochastic boundedness of the closed-loop system with a satisfied H ∞ disturbance rejection level. Moreover, an iterative algorithm is established for designing asynchronous output feedback controller. Finally, simulation results are given to illustrate the proposed design scheme.

This paper addresses the problem of quantized [Formula: see text] filtering for multi-output discrete-time systems over independent identically distributed (i.i.d.) fading channels and Markov fading channels, respectively. The measurement outputs are quantized by a logarithmic quantizer, and then transmitted to the filter over fading channels. For the i.i.d. fading channels, the stochastic multiplicative noise form is used to model the unreliable communication environment. For the Markov fading channels, a set of Markov channel state processes is introduced to model time-varying fading channels, which characterizes various configurations of the physical communication environment and/or different channel fading amplitudes. The sufficient condition for stochastic stability with a prescribed [Formula: see text] performance is obtained by using a Lyapunov method and matrix decoupling technique. The corresponding filter design casts into a convex optimization problem. Finally, simulation results are provided to illustrate the effectiveness of our results.

Communicating via mixed wired-wireless connections is the development trend for large-scale distributed control systems. In this communication environment, due to the limited wireless resources, the communication patterns of subsystems have changed fundamentally, and the design of time-triggered and event-triggered distributed controllers should be taken into account simultaneously. The objective of this paper is to investigate mixed time/event-triggered dual-mode distributed predictive control (DPC) for constrained large-scale linear systems subject to bounded disturbances. Considering the effects of two different communication modes and introducing a prediction error between the current actual state and predicted state, the event-triggering condition is derived for each event-triggered subsystem. Based on this, a mixed time/event-triggered dual-mode DPC algorithm is proposed in view of the asynchronous coordination among subsystems. Furthermore, the sufficient conditions to ensure the recursive feasibility and closed-loop stability of mixed triggered DPC are developed. Finally, a multi-vehicle control system is provided to verify the effectiveness of the proposed approach.

This paper studies the mean square quadratic (MSQ) detectability for multi-output networked systems over finite-state digital block-fading channels. The packet-loss rate of each digital fading channel depends on the channel power gain, as well as packet length and power level used for transmission. A finite-state random process is introduced to model time-varying fading channels, which characterizes various configurations of physical communication environment and/or different channel fading amplitudes. Necessary and sufficient conditions for MSQ detectability over finite-state Markov digital block-fading channels are given in the form of algebraic Riccati equations or linear matrix inequalities (LMIs). The estimation gain is given as a function of estimated/observed channel state. In addition, explicit conditions on network for MSQ detectability over finite-state independent identically distributed (i.i.d.) digital block-fading channels are presented in terms of the unstable poles of the multi-output plant. Finally, an application to Gilbert-Elliott channels (GECs) is provided to demonstrate the derived results.

This technical note investigates the sliding mode control (SMC) design based on finite-time boundedness (FTB) for a class of nonlinear systems. A suitable SMC law is constructed to drive the state trajectories onto the specified sliding surface during a specified finite (possibly short ) time interval. Besides, by introducing a partitioning strategy , the corresponding FTB over reaching phase and sliding motion phase are guaranteed, respectively. And then, the sufficient conditions are derived to ensure the FTB of the closed-loop systems over the whole finite-time interval. Finally, a simulation example is given to show the effectiveness of the proposed design method.

This study investigates the problem of event-triggered distributed predictive control (DPC) for multi-agent systems. To guarantee the stability of overall system, a constraint relevant to the triggering instant is imposed in the DPC optimisation problem, and the corresponding event-triggering condition with the information received from neighbouring agents is derived based on the input-to-state stability (ISS). In such a framework, the DPC optimisation problem is solved only when the triggering condition is satisfied, which reduces the communication consumption and lowers the computational load. Moreover, the recursive feasibility of DPC optimisation problem is proved and the sufficient conditions for ensuring the ISS of overall system are developed. Finally, a simulation example is provided to illustrate the effectiveness of the presented method.

For a networked system with structural controllability, components will be added in the process of operation because of function increase or size increase, which may result in structural uncontrollability of the networked system under original control inputs. With network topology and original control inputs preserved, designing more control inputs or coupling relations to guarantee structural controllability is an important problem. We propose two strategies of adding minimum extra inputs or edges to render a structurally controllable system: 1) through adjusting network topology, minimum input addition problem is transformed into a minimum input selection problem of a simplified network topology, and then a set of minimum additional control inputs is chosen to ensure structural controllability; 2) combining maximum matching sets with greedy algorithm, minimum number of directed edges are added to the system topology to obtain structural controllability. The proposed strategies are able to find an optimal solution of minimum input/edge addition in polynomial time with a low time complexity. We illustrate the validity of our strategies via several graph examples.

In this paper, a multi-agent formation control strategy with collision avoidance and obstacle avoidance in complex environments is proposed. Under a virtual leader structure, the multi-agent formation is realized through path planning and formation control. In path planning, a transitional region based on prediction horizon is used to replan reference trajectories and avoid obstacles in advance. The virtual-leader alternation strategy and feasible state searching strategy are adopted to avoid one side obstacles and crowded obstacles, respectively. Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. In formation control, a distributed model predictive control (DMPC) algorithm is developed to form and maintain formation. In DMPC optimization problem, the non-convex obstacle avoidance constraints are replaced with deviation constraints and linear constraints to design terminal invariant set and reduce online computation burden. Under the whole formation control strategy, the tasks of obstacle avoidance and collision avoidance are both achieved and the agents maintain the formation as much as possible. Finally, the example with different obstacle situations is discussed to illustrate the effectiveness of the proposed strategy.

This paper studies the cooperative stabilization problem of large-scale systems composed of several dynamically coupled subsystems. To drive these subsystems towards global performance in an effective communication way, a self-triggered distributed model predictive control algorithm is proposed. In this algorithm, the triggering instants are introduced to the index function to estimate communication cost and are optimized jointly with the control input sequence by minimizing both control performance and communication cost. At its triggering instants, the local subsystem actively makes a request to all the neighbors and receives their state measurements instead of their predictive state trajectories, which is potential to improve the utilization of communication information and to relieve communication burden. Furthermore, the stability of the overall system is analyzed. Conditions for the terminal matrices and the design parameters used to produce the predictive state trajectories of neighbor subsystems are established to guarantee closed-loop system stability.

This paper considers the problem of event-driven robust output feedback model predictive control for constrained linear systems subject to bounded disturbances. The proposed output feedback controller involves designing a Luenberger state estimator and an event-driven robust model predictive controller. The state estimation error is bounded by a robust positively invariant set, and a constraint tightening method is utilized to formulate the robust output feedback MPC optimization problem. Based on input-to-state stability theory, the event-triggering condition with respect to the error between the system output and the observer output is derived. At each sampling instant, the optimization problem is required to be solved only when the triggering condition is violated. Otherwise, a candidate control law is constructed and applied to system. Moreover, both the feasibility of the optimization problem and the convergence of closed-loop system are proved. Finally, a simulation example is given to show that the proposed approach can reduce the solving frequency of optimization problems while guaranteeing that the system state converges to a neighborhood of the equilibrium.

This work deals with the input-output finite-time stabilization problem for a class of nonlinear systems by employing sliding mode control (SMC) approach. A suitable SMC law is designed to ensure that the state trajectories can be driven onto the specified sliding surface during the assigned finite-time interval. Moreover, some parameters-dependent sufficient conditions are derived such that the input-output finite-time stability (IO-FTS) during both reaching phase and sliding motion phase are attained. Simulation results are provided to illustrate the proposed approach.

This paper investigates the event-triggered model predictive control problem for networked control systems with transmission delays. Based on a multirate scheme, an event-triggered delay compensation strategy is proposed. The event-triggered predictive control algorithm with feasibility and stability guarantee is built upon the triggered conditions that determine whether the measurements are transmitted. The results are further extended to a self-triggered mechanism to avoid continuous monitoring of measurements difference. Finally, simulation results are given to illustrate the effectiveness of the proposed methods.

This paper gives a new systematic framework to solve the asynchronous sliding mode control (SMC) problem for the semi-Markovian jump systems with singular perturbations, which can effectively remove the dependence on the unavailable system mode of SMC controller in some existing works. By considering the effect of the singular perturbation, this paper introduces a novel e -dependent common sliding function, and then an asynchronous SMC law is developed by just using the detected mode signal. In order to overcome the difficulties in analyzing the stability of the sliding mode dynamics and the reachability of the specified sliding surface, a novel matrix decoupling technique and a novel semi-Markovian Lyapunov function method are introduced. Besides, genetic algorithm (GA) is employed to solve two meaningful optimization problems arising from designing a desired asynchronous SMC scheme. Finally, an operational amplifier circuit with a switching positive temperature coefficient thermistor is simulated to show the practicability of the proposed asynchronous SMC approach via GA.

In the operation of urban water distribution networks (WDNs), the supply pressure to users should be kept low while meeting the lower bound to satisfy users’ water demand, reduce leakages and power consumption. In this paper, a multi-model based pressure optimization method for large-scale WDNs is proposed, where the pressure of the whole WDNs is represented by the pressure of key nodes and key pump station nodes based on the calculation of pressure transfer relationship and a developed network simplification method, the multiple linear models are constructed to approximate the pressure transfer relationships, and the models between the selected key nodes and key pump station nodes are used for the pressure optimization of the whole WDNs. The proposed methods are operation data based, which overcomes the difficulty in constructing accurate and complicated hydraulic models for large-scale WDNs. The network simplification reduces data requirements in multiple model construction and ensures real-time control. In the simulation case, which is part of Shanghai’s WDNs, the supply pressure to users is optimized to lower and more homogeneous value without violating the lower bound by applying the proposed approach, which is significant in reducing water leakages and energy consumption.

This paper studies the mean square quadratic (MSQ) detectability for multi-output networked systems over finite-state fading channels. The unreliability from the plant output to the estimator input is described by multiplicative noises. A finite-state random process is introduced to model time-varying fading channels. Necessary and sufficient conditions for MSQ detectability are given in the form of algebraic Riccati equations or linear matrix inequalities. In addition, explicit conditions on network for MSQ detectability over finite-state independent identically distributed (i.i.d.) fading channels are presented in terms of the Mahler measure of the unstable and multi-output plant. Finally, application to Gilbert-Elliott channels (GECs) is provided to demonstrate the derived results.

This paper investigates the problem of event-triggered dual-mode distributed predictive control (DPC) for constrained large-scale linear systems subject to bounded disturbances. Based on input-to-state stability (ISS) theory, the event-triggering condition involving information of the subsystem itself is derived. A dual-mode predictive control scheme is designed to reduce information exchanges with neighboring subsystems. The upper bound of disturbances for ensuring the recursive feasibility and closed-loop stability are developed. Finally, a simulation example is given to show that the presented method is able to save computation resources and communication resources while guaranteeing the desired control performance.

In this work, the problem of dissipative control is investigated for stochastic systems under the unreliable wireless network, in which there may exist stochastic fading channels. Firstly, a modified Rice fading model with disturbance-dependent noise is proposed. And then, a state-feedback controller with fading measurements is designed and the some sufficient conditions are derived such that the closed-loop system is finite-time stochastic bounded with a prescribed exponentially dissipative performance. Finally, numerical simulation results are provided.

This paper studies the input-output finite-time stabilization problem of a class of fuzzy stochastic systems with randomly occurring uncertainties and (x, u)-dependent noises. By considering the randomly occurring gain fluctuations in controller gains, a fuzzy controller is designed to render the closed-loop system input-output finite-time stochastic stability with respect to some specified parameters. Through intensive stochastic analysis, a sufficient condition is established for the controller design via a convex optimization approach. A numerical example is exploited to demonstrate the effectiveness of the proposed design method.

This paper proposes a strategy of event-triggered distributed predictive control (DPC) for large-scale systems with dynamic couplings. The event-triggering condition which only involves local information of the subsystems has been derived based on the input to state stability theory. In the propose scheme, all subsystems optimize with decoupled cost functions and constraints only when the event-triggering conditions are satisfied. The dynamic couplings as well as disturbance can be handled through a robustness constraint in the local optimization. In addition, a dual-mode control scheme is adopted to further save computation resources. Several sufficient conditions are developed to ensure the recursive feasibility and close-loop stability of event-triggered DPC. Finally, the effectiveness of the proposed approach is illustrated via four-tuck systems.

This paper investigates the problem of event-triggered networked predictive control for systems with data loss. An event-triggered networked predictive control system is proposed. Based on predictive control model, a data loss compensation strategy is presented and an extended event-triggered transmission mechanism is developed. The closed-loop event-triggered predictive control system is described as a switched system and sufficient closed-loop stability conditions related to event-triggered mechanism are established. Under the event-triggered networked predictive control scheme, the consumption of the communication resources is reduced. Finally, an example is provided to illustrate the effectiveness of the proposed method.