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This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.

This paper addresses the altitude trajectory tracking control problem of reentry vehicle subject to bounded uncertainty. A new continuous adaptive super-twisting sliding mode control (ASTSMC) method is developed based on conventional super-twisting sliding mode control (STSMC) and adaptive gain technique, which can improve tracking accuracy and achieve high control performance. Based on adaptive gain technique, the designed ASTSMC method requires no prior information on uncertainty and avoids the overestimation of control gain, then the unexpected chattering phenomenon is alleviated. By employing fast power rate reaching law and modified fast nonsingular terminal sliding mode (FNTSM) surface, the designed controller achieves faster convergence and stronger robustness than conventional STSMC methods. Furthermore, the finite-time stability of closed-loop system is proved through Lyapunov theory. Simulation results are executed to validate the superiority of the proposed controller.

This paper investigates several cost-sparsity induced optimal input selection problems for structured systems. Given are an autonomous system and a prescribed set of input links, where each input link has a non-negative cost. The problems include selecting the minimum cost of input links, and selecting the input links with the smallest possible cost with a bound on their cardinality, all to ensure system structural controllability. Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the former problem is polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited. We show these problems can be formulated as equivalent integer linear programming (ILP) problems. Subject to a certain condition on the prescribed input configurations that contains the dedicated input one as a special case, we demonstrate that the constraint matrices of these ILPs are totally unimodular. This property allows us to solve those ILPs efficiently simply via their linear programming (LP) relaxations, leading to a unifying algebraic method for these problems with polynomial time complexity. It is further shown those problems could be solved in strongly polynomial time, independent of the size of the costs and cardinality bounds. Finally, an example is provided to illustrate the power of the proposed method., 8 pages, 4 figures. rearranged, add some materials

Data-driven predictive control (DPC) has been studied and used in various scenarios, since it could generate the predicted control sequence only relying on the historical input and output data. Recently, based on cloud computing, data-driven predictive cloud control system (DPCCS) has been proposed with the advantage of sufficient computational resources. However, the existing computation mode of DPCCS is centralized. This computation mode could not utilize fully the computing power of cloud computing, of which the structure is distributed. Thus, the computation delay could not been reduced and still affects the control quality. In this paper, a novel cloud-edge collaborative containerised workflow-based DPC system with disturbance observer (DOB) is proposed, to improve the computation efficiency and guarantee the control accuracy. First, a construction method for the DPC workflow is designed, to match the distributed processing environment of cloud computing. But the non-computation overheads of the workflow tasks are relatively high. Therefore, a cloud-edge collaborative control scheme with DOB is designed. The low-weight data could be truncated to reduce the non-computation overheads. Meanwhile, we design an edge DOB to estimate and compensate the uncertainty in cloud workflow processing, and obtain the composite control variable. The UUB stability of the DOB is also proved. Third, to execute the workflow-based DPC controller and evaluate the proposed cloud-edge collaborative control scheme with DOB in the real cloud environment, we design and implement a practical workflow-based cloud control experimental system based on container technology. Finally, a series of evaluations show that, the computation times are decreased by 45.19% and 74.35% for two real-time control examples, respectively, and by at most 85.10% for a high-dimension control example., Comment: 58 pages and 23 figures

This article develops a dynamic version of event-triggered model predictive control (MPC) without utilizing any terminal constraint. Such a dynamic event-triggering mechanism takes the advantages of both event- and self-triggering approaches by dealing explicitly with conservatism in the triggering rate and measurement frequency. The prediction horizon shrinks as the system states converge; we prove that the proposed strategy is able to stabilize the system even without any stability-related terminal constraint. Recursive feasibility of the optimization control problem (OCP) is also guaranteed. The simulation results illustrate the effectiveness of the scheme.

This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter.

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms.

In this article, an event-driven output feedback control approach is proposed for discrete-time systems with unknown mismatched disturbances. To estimate the unavailable states and disturbances, a reduced-order extended state functional observer is proposed, and by introducing an event-driven scheduler, the ZOH-based event-driven output feedback disturbance rejection controller is designed, and the stability and disturbance rejection analyses are performed. To further save the network resources, the predictive event-driven output feedback disturbance rejection control approach is proposed, and the stability and disturbance rejection analyses of the systems with predictive control are also conducted. It can be shown that the disturbances are compensated completely in output channels of the systems, and compared with the time-driven control schemes. And event-triggering frequency is greatly reduced with the proposed event-driven control methods. Finally, the effectiveness of the provided control approaches is demonstrated by numerical simulations.

This article addresses the problem of stabilizing a continuous-time switched system affected by a completely unknown disturbance, data quantization, and time-varying delay. In the sense of combined dwell-time and average dwell-time, it is assumed that the switching is slow enough. Suppose that the bound of delay is known but the one of disturbance is unknown. An estimation for the bound of disturbance is used to counteract the unknown disturbance. By extending the approach of the delay-free case, a communication and control strategy is developed by introducing a virtual system. On this basis, the exponential decay and practical stability of the closed-loop system are guaranteed by using a Lyapunov function. Two examples are illustrated to show the usefulness of the proposed framework for stability analysis of some classes of switched systems.

This paper considers a privacy-concerned distributed optimization problem over multi-agent networks, in which malicious agents exist and try to infer the privacy information of the normal ones. We propose a novel dual averaging algorithm which involves the use of a correlated perturbation mechanism to preserve the privacy of the normal agents. It is shown that our algorithm achieves deterministic convergence under arbitrary initial conditions and the privacy preservation is guaranteed. Moreover, a probability density function of the perturbation is given to maximize the degree of privacy measured by the trace of the Fisher information matrix. Finally, a numerical example is provided to illustrate the effectiveness of our algorithm.

This article studies the reachable set of cyber-physical systems subject to stealthy attacks with the Kullback-Leibler divergence adopted to describe the stealthiness. The reachable set is defined as the set in which both the system state and the estimation error of the Kalman filter reside with a certain probability. The necessary and sufficient conditions of the reachable set being unbounded are given for the finite and infinite time cases, respectively. When the reachable set is bounded, an ellipsoidal outer approximation is obtained by solving a convex optimization problem. An application of this approximation to the safety evaluation is also given. A numerical simulation of an unmanned ground vehicle is presented to demonstrate the effectiveness of the proposed approach.

In this paper, the problem of optimal time-varying attitude tracking control for rigid spacecraft with system constraints and unknown additive disturbances is considered. Through the design of a new non-linear tube-based robust model predictive control (TRMPC) algorithm, a dual-loop cascaded tracking control framework is established. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The obtained optimal control action is then combined with a non-linear feedback law such that the actual system trajectories can always be steered within a tube region centred around the nominal solution. To facilitate the recursive feasibility of the optimization process and guarantee the input-to-state stability of the tracking control process, the terminal controller and the corresponding terminal invariant set are also constructed. The effectiveness of using the proposed dual-loop TRMPC control scheme to track reference attitude trajectories is validated by experimental studies. A number of comparative studies were carried out, and the obtained results reveal that the proposed design is able to achieve more promising constraint handling and attitude tracking performance than that of the other newly developed methods investigated in this research.

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN's updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time.

In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

Path planning and formation control are both challenging and critical issues in robotics, which involve computing an optimal path from the initial position to target while keeping the desired formation. This paper studies the path planning and formation control problem for multiple unmanned aerial vehicles (multi-UAVs) in 3-D constrained space. Considering the local minimum of artificial potential function (APF), an effective improved artificial potential function (IAPF) based path planning approach is proposed for the multi-UAV systems. By introducing a rotating potential field, the UAVs can escape from the common local minimum and oscillations efficiently. Afterwards, by using the leader-follower model, a formation controller based on potential function method is developed to ensure that the follower UAVs keep the desired angles and distances with the leader, and a Lyapunov function is designed to analyze the closed-system stability. Finally, simulation studies under different environmental constraints confirm the efficiency of the proposed approaches in addressing the the path planning and formation control issues in 3-D space.