Design of a fast real-time LPV model predictive control system for semi-active suspension control of a full vehicle

This article is concerned with the control of a Semi-Active suspension system of a 7DOF Full Vehicle model, equipped with four Electro Rheological (ER) dampers, taking into account their incipient dissipativity constraints. Herein, a real-time, fast, advanced control structure is presented within the Model Predictive Control framework for Linear Parameter Varying (LPV) systems. The control algorithm is developed to provide a suitable trade-off between comfort and handling performances of the vehicle in a very limited sampling period ($T_s=5\text{ms}$), in view of a possible realtime implementation on a real vehicle. The control structure is tested and compared to other standard fast control approaches. Full nonlinear realistic simulation results illustrate the overall good operation and behaviour of the proposed control approach.     

Event triggering power sharing control for AC/DC microgrids based on P -F droop curve method

The power sharing of AC/DC micro-grids is researched in this paper. The proposed strategy mainly include two parts: the primary power event triggering control with secondary control and an adaptive quasi sliding mode voltage control in inner-loop. Firstly, a event triggering power sharing control (ETPSC) based on $P?F$ droop curve is developed to regulate the voltage and frequency of AC and voltage of DC with the aim of the proportional power sharing between AC and DC micro-grids. The triggered threshold of ETPSC can be chosen to decide the transmitted power between AC and DC micro-grids. When the difference power between AC and DC micro-grids is less than the triggered threshold of power flow, there is no power sharing between AC and DC micro-grids, which can less the number of switching the power flow direction and the transmitted line power loss. The ETPSC has a great robust for the disturbances of load and improve the stability of the system. An adaptive quasi-sliding-mode control,which is implemented easily and flexibly with small computational burden and only based on input/output (I/O) measurement data but not the model any more, is used to control voltage in inner-loop. The effectiveness of the proposed control schemes is demonstrated by some numerical simulations and experimental results.     

Passive controller realization of a bicubic admittance containing a pole at s=0 with no more than five elements for inerter-based mechanical control

This paper is concerned with a passive network synthesis problem about the damper-spring-inerter realization of a bicubic admittance containing a pole at $s=0$ with at most five elements, where the least number of elements for the possible realizations is four. The admittances of many passive mechanisms (controllers) in inerter-based control systems are in this form. A specific four-element realizability that is a parallel connection of a spring and a three-element subnetwork is first solved. By utilizing the realizability constraints based on graph theory, it is proved that only one configuration can cover all the other four-element cases through the discussions of other possible network graphs. By deriving its realizability condition, a necessary and sufficient condition for the four-element realizations can be combined. More generally, the five-element realizability can be investigated. The specific five-element realizability that is the parallel connection of a spring and a four-element subnetwork is solved. By making use of realizability constraints described by network graphs and eliminating impossible configurations, a set of four configurations is found out to cover all the other five-element cases. By investigating their realizability conditions, a necessary and sufficient condition for the five-element realizations can be combined. The results of this paper can reduce the realizability redundancy compared with classical synthesis approaches like Bott–Duffin procedure, which provide a first critical step towards solving the minimal realization problem of such admittances. The results can be applied to the design and optimization of mechanical control systems based on inerters, and in the long term can also contribute to the development of other areas of circuits and systems.     

Rapid highly sensitive general protein quantification through on-chip chemiluminescence

Protein detection and quantification is a routinely performed procedure in research laboratories, predominantly executed either by spectroscopy-based measurements, such as NanoDrop, or by colorimetric assays. The detection limits of such assays, however, are limited to μM concentrations. To establish an approach that achieves general protein detection at an enhanced sensitivity and without necessitating the requirement for signal amplification steps or a multicomponent detection system, here, we established a chemiluminescence-based protein detection assay. Our assay specifically targeted primary amines in proteins, which permitted characterization of any protein sample and, moreover, its latent nature eliminated the requirement for washing steps providing a simple route to implementation. Additionally, the use of a chemiluminescence-based readout ensured that the assay could be operated in an excitation source-free manner, which did not only permit an enhanced sensitivity due to a reduced background signal but also allowed for the use of a very simple optical setup comprising only an objective and a detection element. Using this assay, we demonstrated quantitative protein detection over a concentration range of five orders of magnitude and down to a high sensitivity of 10pgmL−1, corresponding to pM concentrations. The capability of the platform presented here to achieve a high detection sensitivity without the requirement for a multistep operation or a multicomponent optical system sets the basis for a simple yet universal and sensitive protein detection strategy.     

Mean square consensus of double-integrator multi-agent systems under intermittent control: A stochastic time scale approach

We consider the leader–follower consensus problem for a multi-agent system where information is exchanged only on a non-uniform discrete stochastic time domain. For a second-order multi-agent system subject to intermittent information exchange, we model the tracking error dynamics as a $\mu ?$varying linear system on a discrete stochastic time scale, where μ is the graininess operator. Based on a Lyapunov operator and a positive perturbation operator on the space of symmetric matrices, we derive necessary and sufficient conditions to design a decentralized consensus protocol. This protocol allows us to cast the mean-square exponential consensus problem within the framework of dynamic equations on stochastic time scales. We establish some theoretical results which allow for the computation of the control gain matrix which guarantees the mean-square exponential stability with a given decay rate for the error dynamics. To show the effectiveness of the theoretical results, some simulation and experimental results on multi-robot systems have been performed.     

An adaptive online learning algorithm for distributed convex optimization with coupled constraints over unbalanced directed graphs

This paper investigates a distributed optimization problem over multi-agent networks subject to both local and coupled constraints in a non-stationary environment, where a set of agents aim to cooperatively minimize the sum of locally time-varying cost functions when the communication graphs are time-changing connected and unbalanced. Based on dual decomposition, we propose a distributed online dual push-sum learning algorithm by incorporating the push-sum protocol into dual gradient method. We then show that the regret bound has a sublinear growth of O(T^p) and the constraint violation is also sublinear with order of $O\left(T^{1?p/2}\right),$ where T is the time horizon and 0 < p ≤ 1/2. Finally, simulation experiments on a plug-in electric vehicle charging problem are utilized to verify the performance of the proposed algorithm. The proposed algorithm is adaptive without knowing the total number of iterations T in advance. The convergence results are established on more general unbalanced graphs without the boundedness assumption on dual variables. In addition, more privacy concerns are guaranteed since only dual variables related with coupled constraints are exchanged among agents.