An Improved Constraint-Tightening Approach for Stochastic MPC

The problem of achieving a good trade-off in Stochastic Model Predictive Control between the competing goals of improving the average performance and reducing conservativeness, while still guaranteeing recursive feasibility and low computational complexity, is addressed. We propose a novel, less restrictive scheme which is based on considering stability and recursive feasibility separately. Through an explicit first step constraint we guarantee recursive feasibility. In particular we guarantee the existence of a feasible input trajectory at each time instant, but we only require that the input sequence computed at time $k$ remains feasible at time $k+1$ for most disturbances but not necessarily for all, which suffices for stability. To overcome the computational complexity of probabilistic constraints, we propose an offline constraint-tightening procedure, which can be efficiently solved via a sampling approach to the desired accuracy. The online computational complexity of the resulting Model Predictive Control (MPC) algorithm is similar to that of a nominal MPC with terminal region. A numerical example, which provides a comparison with classical, recursively feasible Stochastic MPC and Robust MPC, shows the efficacy of the proposed approach.Comment: Paper has been submitted to ACC 201

Computationally efficient stochastic MPC: A probabilistic scaling approach

In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-performing systems. To reduce the computational burden, in this paper we extend the probabilistic scaling approach to obtain low-complexity inner approximation of chance-constrained sets. This approach provides probabilistic guarantees at a lower computational cost than other schemes for which the sample complexity depends on the design space dimension. To design candidate simple approximating sets, which approximate the shape of the probabilistic set, we introduce two possibilities: i) fixed-complexity polytopes, and ii) `p-norm based sets. Once the candidate approximating set is obtained, it is scaled around its center so to enforce the expected probabilistic guarantees. The resulting scaled set is then exploited to enforce constraints in the classical SMPC framework. The computational gain obtained with the proposed approach with respect to the scenario one is demonstrated via simulations, where the objective is the control of a fixed-wing UAV performing a monitoring mission over a sloped vineyard

Chance-constrained sets approximation: A probabilistic scaling approach

In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then scales these sets to obtain the desired probabilistic guarantees. The proposed approach is shown to be applicable in several problems in systems and control, such as the design of Stochastic Model Predictive Control schemes or the solution of probabilistic set membership estimation problems

Perioperative non-invasive versus semi-invasive cardiac index monitoring in patients with bariatric surgery - a prospective observational study

Results Sixty patients (mean BMI 49.2 kg/m2) were enrolled into the study and data from 54 patients could be entered in the final analysis. Baseline CI was 3.2 ± 0.9 and 3.3 ± 0.8 l/min/m2, respectively. Pooled absolute CI values showed a positive correlation (rs = 0.76, P  92%). Conclusion Non-invasive as compared to semi-invasive CI measurements did not reach criteria of interchangeability for monitoring absolute and trending values of CI in morbidly obese patients undergoing bariatric surgery. Trial registration The study was registered retrospectively on June 12, 2017 with the registration number NCT03184272

A distributed solution to the adjustable robust economic dispatch problem

The problem of maintaining balance between consumption and production of electric energy in the presence of a high share of intermittent power sources in a transmission grid is addressed. A distributed, asynchronous optimization algorithm, based on the ideas of cutting-plane approximations and adjustable robust counterparts, is presented to compute economically optimal adjustable dispatch strategies. These strategies guarantee satisfaction of the power balancing constraint as well as of the operational constraints for all possible realizations of the uncertain power generation or demand. The communication and computational effort of the proposed distributed algorithm increases for each computational unit only slowly with the number of participants, making it well suited for large scale networks. A distributed implementation of the algorithm and a numerical study are presented, which show the performance in asynchronous networks and its robustness against packet loss

An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers

In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of real-time implementability of guidance and control strategies for automated rendezvous and proximity operations between spacecraft. The paper presents considers the validation of the proposed control algorithm on an experimental testbed, showing how it may indeed be implemented in a realistic framework. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided, and under mild assumptions, asymptotic stability in probability of the origin can be established. The offline sampling approach in the control design phase is shown to reduce the computational cost, which usually constitutes the main limit for the adoption of Stochastic Model Predictive Control schemes, especially for low-cost on-board hardware. These characteristics are demonstrated both through simulations and by means of experimental results

Tetracationic bis-triarylborane 1, 3-butadiyne as a combined fluorimetric and Raman probe for simultaneous and selective sensing of various DNA, RNA and proteins

A new bis-triarylborane tetracation (4-Ar2B-3, 5-Me2C6H2)-C≡C- C≡C-(3, 5-Me2C6H2-4-BAr2 [Ar = (2, 6-Me2-4-NMe3-C6H2)+] (24+) shows distinctly different behaviour in its fluorimetric response than that of our recently published bis-triarylborane 5- (4-Ar2B-3, 5-Me2C6H2)-2, 2’-(C4H2S)2-5’-(3, 5-Me2C6H2-4-BAr2) (34+). Single-crystal X-ray diffraction data on the neutral bis- triarylborane precursor 2N confirm its rod-like dumbbell structure, which is shown to be important for DNA/RNA targeting and also for BSA protein binding. Fluorimetric titrations with DNA/RNA/BSA revealed the very strong affinity of 24+ and indicated the importance of the properties of the linker connecting the two triarylboranes. Using the butadiyne- rather than a bithiophene linker resulted in an opposite emission effect (quenching vs enhancement), and 24+ bound to BSA 100 times stronger than 34+. Moreover, 24+ interacted strongly with ss-RNA, and circular dichroism (CD) results suggest ss- RNA chain-wrapping around the rod-like bis-triarylborane dumbbell structure like a thread around a spindle, a very unusual mode of binding of ss-RNA with small molecules. Furthermore, 24+ yielded strong Raman/SERS signals, allowing DNA or protein detection at ca. 10 nM concentrations. The above observations, combined with low cytotoxicity, efficient human cell uptake and organelle-selective accumulation make such compounds intriguing novel lead structures for bio-oriented, dual fluorescence/Raman-based applications

Towards a methodical framework for comprehensively assessing forest multifunctionality

Funded by Deutsche Forschungsgemeinschaft. Grant Number: DFG FOR 891/1-3 National Natural Science Foundation of China. Grant Numbers: 30710103907, 30930005, 31170457, 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in Beijing. Grant Number: GZ 986Peer reviewedPublisher PD