No abstract

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

No abstract

No abstract

No abstract

Pain alleviation associated with castration of piglets is an important welfare issue. The present study compares the effect of different approaches and products suitable for farmer use, with the aim to alleviate pain due to castration in piglets. A randomized within-litter design, with 28 replicate litters, compared 7 treatments: handling () restraint of the piglet and manipulation of the scrotum, castration without pain relief (), 2 treatments (, ) with different concentrations of tetracaine (2 and 6%) applied topically 10 min before and immediately post-surgery, and 3 treatments with i.m. injection of different nonsteroidal anti-inflammatory drugs () 10 min prior to surgery (-meloxicam, -ketoprofen, -tolfenamic acid). Efficacy of pain relief was assessed during a 300 min period after castration by serum cortisol, behavior (walking, lying, suckling, in the nest, isolated and pain related: tremors, rubbing the rear, hunching, wagging of the tail), facial expression and scrotal skin pressure sensitivity. C pigs had greater serum cortisol concentration than all other groups at 60 min post-surgery ( < 0.001), while H pigs had lower concentrations than pigs given topical anesthesia ( < 0.001) though not injected analgesia. No treatment differences were significant at 180 min, but at 300 min cortisol concentration was greater in T2 and T6 piglets than those given NSAIDs ( = 0.03). These treatment differences were mirrored by the pressure sensitivity of the scrotum; in comparison with C piglets, those given NSAIDs showed a reduced sensitivity ( 0.003) but those given local anesthesia did not ( = 0.15). C pigs showed increased frequency of pain-related behavior in the first 30 min in comparison with all other treatments, more time isolated than H or NSAID treatments, and more time standing inactive than H or K treatments. No behavioral differences were apparent after 60 min. No differences in facial expressions were observed among treatments. In conclusion, on-farm methods for pain relief can provide some, though not complete, pain alleviation in the hours after castration. The use of topical anesthesia gave only minor benefit in comparison to NSAID agents injected prior to castration. Since the main differences in indicators of pain between positive and negative controls were observed within the first h after castration, it is important to select drugs that act quickly after administration to facilitate practical processing schedules on farm.

This paper investigates stochastic stabilization procedures based on quadratic and piecewise linear Lyapunov functions for discrete-time linear systems affected by multiplicative disturbances and subject to linear constraints on inputs and states. A stochastic model predictive control (SMPC) design approach is proposed to optimize closed-loop performance while enforcing constraints. Conditions for stochastic convergence and robust constraints fulfillment of the closed-loop system are enforced by solving linear matrix inequality problems off line. Performance is optimized on line using multistage stochastic optimization based on enumeration of scenarios, that amounts to solving a quadratic program subject to either quadratic or linear constraints. In the latter case, an explicit form is computable to ease the implementation of the proposed SMPC law. The approach can deal with a very general class of stochastic disturbance processes with discrete probability distribution. The effectiveness of the proposed SMPC formulation is shown on a numerical example and compared to traditional MPC schemes. Index Terms-Constrained linear systems, model predictive control (MPC), stochastic control. I. INTRODUCTION M ODEL predictive control (MPC) is a popular strategy which has been widely adopted in industry as an effective mean of dealing with multivariable constrained control problems [1]. The idea behind MPC is to obtain the control signal by solving at each sampling time an open-loop finitehorizon optimal control problem based on a given prediction model of the process, by taking the current state of the process as the initial state. The control inputs are implemented in accordance with a receding horizon scheme. Classical MPC formulations do not provide a systematic way to deal with model uncertainties and disturbances. Many predictive control schemes have been proposed to guarantee stability and constraint fulfillment in the presence of disturbances. Most works are based on the min-max approach, where the performance index to be minimized is computed over the worst possible disturbance realization [2]-[6]. However, min-max policies are often computationally demanding, and the resulting Manuscript

No abstract