Search

Background and Purpose: l-DOPA-induced dyskinesia (LID) is considered a major complication in the treatment of Parkinson's disease (PD). Buspirone (5-HT1A partial agonist) have shown promising results in the treatment of PD and LID, however no 5-HT-based treatment has been approved in PD. The present study was aimed to investigate how the substantia nigra pars reticulata (SNr) is affected by buspirone and whether it is a good target to study 5-HT antidyskinetic treatments.Experimental Approach: Buspirone was studied using in vivo single-unit, electrocorticogram, local field potential recordings along with microdialysis and immunohistochemistry in naïve/sham, 6-hydroxydopamine (6-OHDA)-lesioned or 6-OHDA-lesioned and l-DOPA-treated (6-OHDA/l-DOPA) rats.Key Results: Local buspirone inhibited SNr neuron activity in all groups. However, systemic buspirone reduced burst activity in 6-OHDA-lesioned rats (with or without l-DOPA treatment), whereas 8-OH-DPAT, a full 5-HT1A agonist induced larger inhibitory effects in sham animals. Neither buspirone nor 8-OH-DPAT markedly modified the low-frequency oscillatory activity in the SNr or synchronization within the SNr with the cortex. In addition, local perfusion of buspirone increased GABA and glutamate release in the SNr of naïve and 6-OHDA-lesioned rats but no effect in 6-OHDA/l-DOPA rats. In the 6-OHDA/l-DOPA group, increased 5-HT transporter and decreased 5-HT1A receptor expression was found.Conclusions and Implications: The effects of buspirone in SNr are influenced by dopamine loss and l-DOPA treatment. The present results suggest that the regulation of burst activity of the SNr induced by DA loss may be a good target to test new drugs for the treatment of PD and LID. [ABSTRACT FROM AUTHOR]

Real-time implementation of optimisation-based control and trajectory planning can be very challenging for nonlinear systems. As a result, if an implementation based on a fixed linearisation is not suitable, the nonlinear problems are typically locally approximated online, in order to leverage the speed and robustness of embedded solvers for convex quadratic programs (QP) developed during the last decade. The purpose of this paper is to demonstrate that, using simple standard building blocks from nonlinear programming, combined with a structure-exploiting linear system solver, it is possible to achieve computation times in the range typical of solvers for QPs, while retaining nonlinearities and solving the nonlinear programs (NLP) to local optimality. The implemented algorithm is an interior-point method with approximate Hessians and adaptive barrier rules, and is provided as an extension to the C code generator FORCES. Three detailed examples are provided that illustrate a significant improvement in control performance when solving NLPs, with computation times that are comparable with those achieved by fast approximate schemes and up to an order of magnitude faster than the state-of-the-art interior-point solver IPOPT. [ABSTRACT FROM AUTHOR]

Presents Part II of a description of a computer-oriented course on process control which has been offered at the University of Wisconsin for undergraduate students. Lecture topics and laboratory experiments are also included. (HM)

Described is a graduate course in the process control curriculum at the University of Wisconsin that integrates real-time computing into process control teaching. Lecture topics and recent graduate laboratory projects are presented. (BT)

In this paper, a novel hierarchical multirate control scheme for nonlinear discrete-time systems is presented, consisting of a robust nonlinear model predictive controller (NMPC) and a multirate sliding mode disturbance compensator (MSMDC). The proposed MSMDC acts at a faster rate than the NMPC in order to keep the system as close as possible to the nominal trajectory predicted by NMPC despite model uncertainties and external disturbances. The a priori disturbance compensation turns out to be very useful in order to improve the robustness of the NMPC controller. A dynamic input allocation between MSMDC and NMPC allows to maximize the benefits of the proposed scheme that unites the advantages of sliding mode control (strong reduction of matched disturbances, low computational burden) to those of NMPC (optimality, constraints handling). Sufficient conditions required to guarantee input-to-state stability and constraints satisfaction by the overall scheme are also provided. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper presents a review of active set (AS) algorithms that have been deployed for implementation of fast model predictive control (MPC). The main purpose of the survey is to identify the dominant features of the algorithms that contribute to fast execution of online MPC and to study their influence on the speed. The simulation study is conducted on two benchmark examples where the algorithms are analyzed in the number of iterations and in the workload per iteration. The obtained results suggest directions for potential improvement in the speed of existing AS algorithms. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm-wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite-horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm-wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]