An Alternative for PID control: Predictive Functional Control - A Tutorial

PFC (Predictive Function Control) can be considered as a bridge between PI(D) and complex MPC. PI(D) control can have problems handling dead time and constraints. PFC handles these cases and is often better than using a Smith predictor. PFC is a simple realizable MPC which thus uses prediction and preview of key variables. PFC can be implemented via simple program code and thus has cheap license costs. The tutorial introduces the basic idea of PFC and algorithms for typical processes. Simulations illustrate its effectiveness and advantages over PI(D) and Smith predictors

Input shaping predictive functional control for different types of challenging dynamics processes

Predictive functional control (PFC) is a fast and effective controller that is widely used for processes with simple dynamics. This paper proposes some techniques for improving its reliability when applied to systems with more challenging dynamics, such as those with open-loop unstable poles, oscillatory modes, or integrating modes. One historical proposal considered is to eliminate or cancel the undesirable poles via input shaping of the predictions, but this approach is shown to sometimes result in relatively poor performance. Consequently, this paper proposes to shape these poles, rather than cancelling them, to further enhance the tuning, feasibility, and stability properties of PFC. The proposed modification is analysed and evaluated on several numerical examples and also a hardware application

Enhancement in pneumatic positioning system using nonlinear gain constrained model predictive controller: experimental validation

The issues of inaccurate positioning control have made an industrial use of pneumatic actuator remains restricted to certain applications only. Non-compliance with system limits and properly control the operating system may also degrade the performance of pneumatic positioning systems. This study proposed a new approach to enhance pneumatic positioning system while considering the constraints of system. Firstly, a mathematical model that represented the pneumatic system was determined by system identification approach. Secondly, model predictive controller (MPC) was developed as a primary controller to control the pneumatic positioning system, which took into account the constraints of the system. Next, to enhance the performance of the overall system, nonlinear gain function was incorporated within the MPC algorithm. Finally, the performances were compared with other control methods such as constrained MPC (CMPC), proportional-integral (PI), and predictive functional control with observer (PFC-O). The validation based on real-time experimental results for 100 mm positioning control revealed that the incorporation of nonlinear gain within the MPC algorithm improved 21.03% and 2.69% of the speed response given by CMPC and PFC-O, and reduced 100% of the overshoot given by CMPC and PI controller; thus, providing fast and accurate pneumatic positioning control system

Design and Control of Power Converters 2019

In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Web addiction in the brain: Cortical oscillations, autonomic activity, and behavioral measures

Background and aims Internet addiction (IA) was recently defined as a disorder tagging both the impulse control and the reward systems. Specifically, inhibitory deficits and reward bias were considered highly relevant in IA. This research aims to examine the electrophysiological correlates and autonomic activity [skin conductance response (SCR) and heart rate] in two groups of young subjects (N = 25), with high or low IA profile [tested by the Internet Addiction Test (IAT)], with specific reference to gambling behavior. Methods Oscillatory brain activity (delta, theta, alpha, beta, and gamma) and autonomic and behavioral measures [response times (RTs) and error rates (ERs)] were acquired during the performance of a Go/NoGo task in response to high-rewarding (online gambling videos and video games) or neutral stimuli. Results A better performance (reduced ERs and reduced RTs) was revealed for high IAT in the case of NoGo trials representing rewarding cues (inhibitory control condition), probably due to a “gain effect” induced by the rewarding condition. In addition, we also observed for NoGo trials related to gambling and video games stimuli that (a) increased low-frequency band (delta and theta) and SCR and (b) a specific lateralization effect (more left-side activity) delta and theta in high IAT. Discussion Both inhibitory control deficits and reward bias effect were considered to explain IA

The Effects of Dopamine on Frequency Dependent Short Term Synaptic Plasticity: A Comparison of Layer Contributions and Rhythmic Dynamics

Executive functions (e.g. working memory [WM]) are known to be mediated by prefrontal cortical areas of the human brain which share homology with mouse medial prefrontal cortex (mPFC). Furthermore, it is well established that optimal dopaminergic input is required for proper WM function in the PFC. While it is well established that the mPFC receives inputs from several different brain areas, impinging on different compartmental regions of cells, it remains unknown how layer V pyramidal cells, the major output cells of the mPFC, integrate this information. Additionally, it remains unknown how dopamine modulates this integration by way of separate afferents and compartments within the PFC. A subset of studies presented here focus attention on the excitatory synaptic responses of layer V cells in response to compartmentalized stimulation (i.e. within the somatic region [layer V] or within the apical tufts [layer I]). Overall, these data suggest that dopamine, through D1 receptor (R) activation promotes local connectivity (primarily layer V to layer V connections) in the somatic region, while simultaneously inhibiting synaptic plasticity within the apical tufts through the suppression of NMDAR-mediated responses. Additionally, D2R activation had no effect on local layer V connectivity, but may play a role in regulating the signal-to-noise ratio in the apical tufts, by inhibiting low-frequency inputs and promoting inputs firing at high frequencies. Taken together, these results suggest that in the presence of normal dopamine levels local influences (i.e. environmental / bottom-up ) and plasticity will be promoted within layer V, while top-down\u22 or contextual information impinging on layer I is stabilized. Additional studies presented here focus attention on the excitatory synaptic responses, and modulation of dopamine, of layer V pyramids in response to inputs from the contralateral mPFC. These data suggest that D1R modulation enhances the ability of layer V cells to integrate information from the contralateral mPFC. In combination, these experiments provide insight into how normal dopaminergic receptor activation alters signal processing and integration properties of layer V cells within the mPFC and shed light on cellular mechanism disruptions in schizophrenia, a disorder characterized by dopaminergic dysregulation

Linking addiction-related behavior to synaptic efficacy and network activity in the prefrontal-accumbal pathway of behaving rats

Addiction is a chronically relapsing brain disorder, involving compulsive drug seeking and taking. Enduring vulnerability to relapse is a challenging feature to manage in substance use disorder, with devastating effects to those who suffer from it, as well as at familial and public health levels. Incubation of drug craving characterized by gradual increases in cue-induced drug craving following halting of drug use, may contribute to heightened relapse risk, even after prolonged abstinence. Addictive drugs act upon and usurp the mesolimbic circuit, with long-term drug abuse leads to reward processing, cognitive and decision-making deficits. Drug-driven neuronal plasticity within the Prefrontal cortex (PFC) to Nucleus Accumbens (NAc) pathway is a known key substrate and mediator of addictive behavior. Here we performed longitudinal in vivo local field potential (LFP) recordings in freely behaving rats throughout an incubation of drug seeking paradigm. This approach proved suitable to assess both evoked and spontaneous LFP activity at distinct behavioral stages of the addiction cycle, in a within subject manner. Chronic cocaine self-administration induced strengthening of the PFC-NAc pathway, accompanied by enhanced glutamate release, when compared to drug naïve conditions. Compellingly, the degree of synaptic adaptation correlated with the cocaine intake and incubation severity of individual rats. At the network level, accumbal oscillatory profile of rats that underwent CSA was also altered, with suppression of high gamma and enhanced alpha and beta waves. Throughout withdrawal, persistent pre-synaptic release subsisted, while network changes proved to be transient. Yet, rats with history of cocaine intake did showed altered LFP patterns, upon a cocaine challenge, when compared to saline yoked counterparts, suggesting impaired corticostriatal network dynamics that endures after long-term abstinence. During reinstatement, i.e. relapse-like conditions, distinct frequency components were found to be differentially modulated by drug seeking behavior. Drug-driven adaptations to synaptic transmission and concomitant alterations of oscillatory landscape of functionally connected areas, such as the PFC and NAc, represent multiple-leveled dysregulation exerted by addictive drugs. Concerted maladaptive changes may contribute to the development of a de novo homeostatic threshold that is both driven by and drives drug abuse, craving and relapse in a spiraling cycle of addiction