Robust adaptive passivity-based PIλD control

In this article, we develop proportional, fractional-integral, and derivative () controllers for the regulation and tracking problems of nonlinear systems. The analytic results are obtained by extending the passivity-based approach to include fractional operators. Robustness under parametric uncertainty is dealt with by a combination with an adaptive scheme. It is also shown their robustness under additive noise and their robustness under uncertainty in the derivation order. The advantages in the controlled system performance and in the control energy consumption in comparison to classic PI and proportional integral derivative controllers are illustrated for the quadratic boost converter and a benchmark system in the literature.     

Adaptive control of unactuated dynamical systems through interconnections: Stability and performance guarantees

This article studies control and performance enforcement for a class of uncertain dynamical systems that consist of actuated and unactuated portions physically interconnected to each other. The proposed approach stabilizes the overall interconnected system in the presence of unknown physical interconnections as well as system uncertainties. Performance guarantees are enforced, individually, on the actuated as well as unactuated portions of the interconnected system via this approach. For enforcing these performance guarantees, a set-theoretic model reference adaptive control approach is used, in conjunction with linear matrix inequalities, to restrict the respective system error trajectories of the actuated and unactuated dynamics inside a priori, user-defined compact sets. The efficacy of the proposed approach is demonstrated using simulations.     

Optimization of self-regulated hydrogen production from photovoltaic energy

The use of photovoltaic energy (PV) for the production of hydrogen by using autonomous modular self-regulated systems is studied. Results are compared with those obtained for controlled systems. It was proved that for small and low-cost applications, it is possible to eliminate any control system with yields as high as 91.2% in the PV-electrolyzer interface for a sunny day. Self-regulated systems are thus an excellent, safe, cheap and environmentally friendly alternative for applications in isolated sites, especially in emerging countries.     

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

An interval observer for uncertain continuous‐time linear systems

To design robust interval observers for uncertain continuous‐time linear systems, a new set‐integration approach is proposed to compute trajectory tubes for the estimation error. Because this approach, the order‐preserving condition on the dynamics of the estimation error is no longer required. Therefore, synthesis methods can be used to compute observer gains that reduce the impact of the system uncertainties on the accuracy of the estimated state enclosures. The performance of the proposed approach is showcased through illustrative numerical examples.     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

Utilizing the autocatalysis of Co to prepare low-cost WC-Co powder for high-performance atmospheric plasma spraying

A novel method of incorporating fluidized bed chemical vapor deposition (FBCVD) with electroless plating was developed to effectively prepare the core-shell structured WC-Co composite powders. The Co nanoparticles decorated on the surface of WC particles by FBCVD acted as active catalysts for the subsequent electroless plating process. The particle size and quantity of the decorated Co particles determined the electroless plating rate but the particle size played more important role. For the conditions tested, the maximum electroless plating rate of 2.34 mg/g/min was obtained by using an optimal FBCVD pretreatment at 750°C for 3 minutes. WC-12Co composite powders with a commercial composition widely used for atmospheric plasma spraying (APS) were efficiently prepared. The composite powders exhibited excellent suitability for APS by forming a homogenous Co-W-C ternary liquid stream. The APS coating is not only well-bonded with the substrate but also consisted of hard nonequilibrium Co₃W₃C and W₂C phases with a uniform distribution. Both remarkably improved the hardness and tribological properties of the APS coating.     

Werkstoffliche Aufbereitung von Wärmedämmverbundsystemen

Due to increasing energy requirements for buildings, thermal insulation composite systems (TICS) have been used to insulate building facades since the 1970s. In view of the longevity of these composite materials, there has been an increased amount of TICS waste in recent years. Since there is no current recycling concept for these systems, an enormous resource efficiency potential remains unused. Due to the complexity of composite materials this study focuses on the pretreatment and discusses different processing steps in detail.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

An iterative approach for the discrete‐time dynamic control of Markov jump linear systems with partial information

The , and mixed dynamic output feedback control of Markov jump linear systems in a partial observation context is studied through an iterative approach. By partial information, we mean that neither the state variable x(k) nor the Markov chain θ(k) are available to the controller. Instead, we assume that the controller relies only on an output y(k) and a measured variable coming from a detector that provides the only information of the Markov chain θ(k). To solve the problem, we resort to an iterative method that starts with a state‐feedback controller and solves at each iteration a linear matrix inequality optimization problem. It is shown that this iterative algorithm yields to a nonincreasing sequence of upper bound costs so that it converges to a minimum value. The effectiveness of the iterative procedure is illustrated by means of two examples in which the conservatism between the upper bounds and actual costs is significantly reduced.