Terminal constrained robust hybrid iterative learning model predictive control for complex time-delayed batch processes

This work mainly addresses terminal constrained robust hybrid iterative learning model predictive control against time delay and uncertainties in a class of complex batch processes with input and output constraints. In this work, an equivalently novel extended two-dimensional switched system is first constructed to represent the process model by introducing state difference, output error and new relaxation variable information. Then, a hybrid predictive updating controller is proposed and an optimal performance index function including terminal constraints is designed. Under the condition that the switching signal meets certain conditions, the solvable problem of model predictive control is realized by Lyapunov stability theory. Meanwhile, the design scheme of controller parameters is also given. In addition, the robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbances, such that the system state converges to the constraint set and meets its expected value. Finally, the effectiveness of the proposed algorithm is verified by controlling the speed and pressure parameters of the injection molding process.     

Resource limited event-triggered model predictive control for continuous-time nonlinear systems based on first-order hold

This paper investigates a resources-limited situation in the event-triggered model predictive control (ETMPC) for continuous-time nonlinear system with first-order hold fashion. In consideration of limited bandwidth in data transmission through wireless network under actual operation, our strategy divides the prediction horizon, and applies linear interpolation instead of zero-order hold fashion to obtain a better system performance, so that the reduction of resources and the optimization of strategy can be guaranteed. Furthermore, in actual industry processes, quadratic cost function cannot be implemented in all operations, then general cost function is adopted in this paper. Based on the first-order hold method and general cost function, the feasibility of the ETMPC algorithm and the stability of dynamical systems are analyzed. At last, a practical example is given to show the advantages of our method.     

Characterization and computation of control invariant sets for linear impulsive control systems

Impulsive control systems are suitable to describe and control a venue of real-life problems, going from disease treatment to aerospace guidance. The main characteristic of such systems is that they evolve freely in-between impulsive actions, which makes it difficult to guarantee its permanence in a given state-space region. In this work, we develop a method for characterizing and computing approximations to the maximal control invariant sets for linear impulsive control systems, which can be explicitly used to formulate a set-based model predictive controller. We approach this task using a tractable and non-conservative characterization of the admissible state sets, namely the states whose free response remains within given constraints, emerging from a spectrahedron representation of such sets for systems with rational eigenvalues. The so-obtained impulsive control invariant set is then explicitly used as a terminal set of a predictive controller, which guarantees the feasibly asymptotic convergence to a target set containing the invariant set. Necessary conditions under which an arbitrary target set contains an impulsive control invariant set (and moreover, an impulsive control equilibrium set) are also provided, while the controller performance are tested by means of two simulation examples.     

A switching-based Moving Target Defense against sensor attacks in control systems

Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem.     

Event-triggered and self-triggered impulsive control for two-time-scale systems

This paper studies the stability problem of two-time-scale system via event-triggered impulsive control and self-triggered impulsive control. The overall system is modeled with the hybrid formalism. Two Chang transformations are introduced to completely decouple the hybrid system states into flow set and jump set. A composite impulsive controller based on slow and fast system states is proposed, under which the slow and fast subsystems are simultaneously triggered by event-triggered and self-triggered mechanism, respectively. As a result, the stability conditions are derived for the system under event-triggered and self-triggered impulsive control, respectively. Furthermore, the theoretical result of self-triggered impulsive control is applied to the consensus of the interconnected two-time-scale systems. Finally, simulation examples and comparison study show the effectiveness of the proposed control strategies.     

Stability analysis of cyclic switched nonlinear systems via event-triggered method

In this paper, the exponential stability problem is addressed for a class of cyclic switched nonlinear systems with unstable modes. A novel event-triggered cyclic switching scheme (ETCSS) is proposed to generate a cyclic switching signal that exponentially stabilizes the considered system for any given initial configuration. Some easily verifiable stability criteria for switched nonlinear and linear systems are established, and the effects of event parameters on the convergence rate are also analyzed. Different from the existing studies, here the developed switching scheme is not only cyclic but also event-triggered and thereby the switching frequency is relatively low. Finally, a numerical example is given to verify the efficiency of the method.     

Stochastic stabilization of hybrid neural networks by periodically intermittent control based on discrete-time state observations

This paper is concerned with stabilization of hybrid neural networks by intermittent control based on continuous or discrete-time state observations. By means of exponential martingale inequality and the ergodic property of the Markov chain, we establish a sufficient stability criterion on hybrid neural networks by intermittent control based on continuous-time state observations. Meantime, by M-matrix theory and comparison method, we show that hybrid neural networks can be stabilized by intermittent control based on discrete-time state observations. Finally, two examples are presented to illustrate our theory.     

Data-driven optimal tracking control of switched linear systems

This paper addresses the optimal tracking control for switched linear systems with unknown dynamics. We convert the problem into an optimal control problem of the augmented switched systems. In view of the augmented systems, we propose a data-driven switched linear quadratic regular algorithm for obtaining the optimal switching signal under unknown system dynamics. It is proved that the optimal switching signal will not cause Zeno behavior and can make the system stable. Besides, with the proposed algorithm, we just need to identify an autonomous system instead of the original systems, which has fewer parameters to be determined. A numerical example is given to illustrate the validity of the main results.     

Manipulating Nonlinear Photocurrent from Interlayer Coupling in Bilayer Metamaterials for Polarized Terahertz Generation

Polarized terahertz (THz) wave generation is of great significance for chiral and anisotropic sensing applications. However, how to manipulate amplitude, polarization, and ellipticity of the THz generation is still a fundamental challenge. Herein, polarized THz wave generation is achieved from a bilayer metamaterial consisting of T-shaped structure (TSS) and split resonator rings (SRRs) by combining Maxwell and hydrodynamic equations. The elliptically polarized THz wave can be synthetized directly from horizontally and vertically polarized THz components due to the orthogonal nonlinear photocurrents along the arm-directions of TSS and SRRs, respectively. Besides, the ellipticity and the orientation angle of the THz polarization ellipse can be modulated by the twist angle between the SRRs and TSS layers. The maximum ellipticity can reach 0.34 while the orientation angle is tunable from −0.45 to 0.48π by tuning the twist angle. This work proposes an interlayer coupling method for the polarized THz sources based on metamaterials in potential circular dichroism and chiral sensing applications.     

Controlling the Trajectory of Swallowtail Gaussian Beams in Dynamic Parabolic Potentials

The propagation dynamics of finite-energy Swallowtail beams in a dynamic parabolic potential, including uniformly moving, accelerating, and oscillating potentials, are investigated. The strong influence of dynamic potentials on the propagation trajectory of Swallowtail beams is demonstrated and various effective manipulations of the beams, including trajectory control, are validated. The intensity and the focal position can also be affected. In addition, the extension to 2D scenarios is also presented. The results theoretically provide more diverse manipulation possibilities for Swallowtail beams, and thus may broaden their potential applications in trajectory and particle manipulation.