Reduce Control Energy Research Articles

Detection of minimal extended driver nodes in energetic costs reduction.

Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.

Tilt-fractional order proportional integral derivative control for DC motor using particle swarm optimization

Introduction. Recently, the most desired goal in DC motor control is to achieve a good robustness and tracking dynamic of the set-point reference speed of the feedback control system. Problem. The used model should be as general as possible and consistently represent systems heterogeneous (which may contain electrical, mechanical, thermal, magnetic and so on). Goal. In this paper, the robust tilt-fractional order proportional integral derivative control is proposed. The objective is to optimize the controller parameters from solving the criterion integral time absolute error by particle swarm optimization. The control strategy is applied on DC motor to validate the efficiency of the proposed idea. Methods. The proposed control technique is applied on DC motor where its dynamic behavior is modeled by external disturbances and measurement noises. Novelty. The proposed control strategy, the synthesized robust tilt-fractional order proportional integral derivative speed controller is applied on the DC motor. Their performance and robustness are compared to those provided by a proportional integral derivative and fractional order proportional integral derivative controllers. Results. This comparison reveals superiority of the proposed robust tilt-fractional order proportional integral derivative speed controller over the remaining controllers in terms of robustness and tracking dynamic of the set-point reference speed with reduced control energy.

Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape

Psychedelics including lysergic acid diethylamide (LSD) and psilocybin temporarily alter subjective experience through their neurochemical effects. Serotonin 2a (5-HT2a) receptor agonism by these compounds is associated with more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brain’s control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states. Using brain states derived from existing functional magnetic resonance imaging (fMRI) datasets, we show that LSD and psilocybin reduce control energy required for brain state transitions compared to placebo. Furthermore, across individuals, reduction in control energy correlates with more frequent state transitions and increased entropy of brain state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors (obtained from publicly available positron emission tomography (PET) data under non-drug conditions), we demonstrate an association between the 5-HT2a receptor and reduced control energy. Our findings provide evidence that 5-HT2a receptor agonist compounds allow for more facile state transitions and more temporally diverse brain activity. More broadly, we demonstrate that receptor-informed network control theory can model the impact of neuropharmacological manipulation on brain activity dynamics.

Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.

Model‐free incremental adaptive dynamic programming based approximate robust optimal regulation

AbstractThis article presents a new formulation for model‐free robust optimal regulation of continuous‐time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), utilizes measured input‐state data to allow the design of the approximate optimal incremental control strategy, stabilizing the controlled system incrementally under model uncertainties, environmental disturbances, and input saturation. By leveraging the time delay estimation (TDE) technique, we first use sensor data to reduce the requirement of a complete dynamics, where input‐state data is adopted to construct an incremental dynamics which reflects the system evolution in an incremental form. Then, the resulting incremental dynamics serves to design the approximate optimal incremental control strategy based on adaptive dynamic programming, which is implemented as a simplified single critic structure to get the approximate solution to the value function of the Hamilton–Jacobi–Bellman equation. Furthermore, for the critic neural network, experience data are used to design an off‐policy weight update law with guaranteed weight convergence. Rather importantly, we incorporate a TDE error bound related term into the cost function, whereby the unintentionally introduced TDE error is attenuated during the optimization process. The proofs of system stability and weight convergence are provided. Numerical simulations are conducted to validate the effectiveness and superiority of our proposed IADP, especially regarding the reduced control energy expenditure and the enhanced robustness.

Analysis of the parameters of the double clutch drive with reduced control energy consumption

The global trend of improving vehicle and traction transmissions is associated with the use of robotic preselector gearboxes, an important component of which is the friction clutch. The review of existing designs and analysis of clutch development concepts allows determining the directions of their improvement, in particular, the improvement of dry double clutches, which is the subject of research in this paper. A significant disadvantage of existing dual-clutch designs is the use of additional special (mostly hydraulic or combined) systems for on-off friction pairs, so the structural cost and energy costs for control are increased, and the design, maintenance, and repair are complicated. The proposed design of the drive to control the original dry dual-clutch involves a lever mechanism with the use of rotary stops, which are made in the form of mobile carriages with rollers, this provides switching clutches in a short time and actually without interruption of power flow, so the acceleration dynamics of vehicles will be improved, the clutch design will be simplified and energy consumption for its control during the start and gear shifting will be reduced. The mathematical model of the proposed clutch drive on the basis of which the control mechanism operation is simulated and influence of its design parameters on operational indicators of the clutch drive, in particular, on kinematic characteristics of the drive, ranges of kinematic and power gear ratios, the power interaction of the mechanism links, the power of the actuator when each clutch is turning on. It is established that the switching of clutches requires less energy, the movement of the mechanism elements is different, and the compression force of the friction pairs when starting the first and second clutches is different. The simulation results confirm that the proposed original lever design of the clutch control mechanism is effective. The obtained results allow us to reasonably determine the parameters of a clutch, but the choice of optimal-rational parameters of its control mechanism requires further research, for which appropriate methods, search algorithms, and their software implementation have been developed.

A new robust tilt-PID controller based upon an automatic selection of adjustable fractional weights for permanent magnet synchronous motor drive control

This paper focuses on achieving a good trade-off between performance and robustness for a class of uncertainty models including unstructured multiplicative uncertainties. In robust control, the simultaneous improvement of the two secure margins for nominal performances and robust stability using a standard controller structure represents two contradictory objectives and guaranteeing simultaneously of these goals represents therefore a major challenge for most researchers. In this context, a robust tilt-proportional integral derivative (T-PID) controller synthesized with an automatic selection of adjustable fractional weights (AFWs) is discussed in our work. Their parameters are optimized through solving a weighted-mixed sensitivity problem using an optimization tool which is based on the genetic algorithm. This problem is formulated from performance and robustness requirements where a fitness function is accordingly determined. Furthermore, thus its search space is built according to some guidelines for ensuring an automatic selection of adequate AFWs. The proposed constrained optimization problem is initialized by using arbitrary T-PID speed controller as well as through initial fixed integer weights (FIWs) which were chosen previously by the designer. To highlight the proposed control strategy, the synthesized robust T-PID speed controller is applied on the permanent magnet synchronous motor. Their performance and robustness are compared to those provided by an integer-order PID (IO-PID) and two conventional fractional-order PID (FO-PID) controllers. This comparison reveals superiority of the proposed robust T-PID controller over the remaining controllers in terms of robustness with reduced control energy.

Bioinspired Nonlinear Dynamics-Based Adaptive Neural Network Control for Vehicle Suspension Systems With Uncertain/Unknown Dynamics and Input Delay

A unique adaptive neural network control scheme is proposed for active suspension systems by employing bioinspired nonlinear dynamics, so as to address several critical engineering issues including energy efficiency, input delay, and unknown/uncertain dynamics simultaneously. A novel constructive predictor is firstly designed to solve the effect of input delay. Neural networks are then adopted to approximate the uncertain/unknown dynamics, and importantly, a unique finite-time adaptive control is established which can not only online update the input and output weights of the neural networks but also intentionally introduce beneficial nonlinear dynamics to vibration control. The significant difference from most existing controllers lies in that the designed controller can effectively utilize beneficial nonlinear stiffness and damping characteristics of a novel bioinspired reference model and, thus, purposely achieve superior vibration suppression and obvious energy-saving performance simultaneously. Theoretical analysis and experimental results vindicate that the proposed controller can effectively suppress vibration with much more improved control performance and considerably reduced control energy consumption more than 44%. This should be for the first time to reveal both in theory and experiments that a superior suspension performance is achieved simultaneously with an obvious control energy saving, by employing beneficial bioinspired nonlinear dynamics, compared to most traditional control methods.

Optimizing target nodes selection for the control energy of directed complex networks

The energy needed in controlling a complex network is a problem of practical importance. Recent works have focused on the reduction of control energy either via strategic placement of driver nodes, or by decreasing the cardinality of nodes to be controlled. However, optimizing control energy with respect to target nodes selection has yet been considered. In this work, we propose an iterative method based on Stiefel manifold optimization of selectable target node matrix to reduce control energy. We derive the matrix derivative gradient needed for the search algorithm in a general way, and search for target nodes which result in reduced control energy, assuming that driver nodes placement is fixed. Our findings reveal that the control energy is optimal when the path distances from driver nodes to target nodes are minimized. We corroborate our algorithm with extensive simulations on elementary network topologies, random and scale-free networks, as well as various real networks. The simulation results show that the control energy found using our algorithm outperforms heuristic selection strategies for choosing target nodes by a few orders of magnitude. Our work may be applicable to opinion networks, where one is interested in identifying the optimal group of individuals that the driver nodes can influence.

Integral-Type Terminal Sliding-Mode Control for Grid-Side Converter in Wind Energy Conversion Systems

This paper investigates robust control strategies for wind energy conversion systems with variable-speed permanent magnet synchronous generators, which are integrated into the grid to provide reliable, secure, and efficient electrical power. A three-phase grid-side converter without a grid transformer is connected to the grid using an LCL filter with low resistive losses. In these working conditions, an instantaneous power proportional integral (PI) controller for the outer voltage-loop in the grid-side converter is used to regulate the DC-link voltage and generate the required currents for the inner current-loop in the grid-side converter. Two integral-type terminal sliding-mode controllers are proposed to control the active and reactive powers exchanged between the converter and the grid. The switching signals in the controllers are softened to attenuate chattering. The time-varying gains in the controllers are constructed to reduce control energy wastage and avoid overestimation of the boundaries of system uncertainties. Simulated and experimental results validate the proposed method.

Fuzzy fast terminal sliding mode controller using an equivalent control for chaotic oscillation in power system

Chaotic oscillation in a power system is taken to be the main cause for power blackouts in large-scale interconnected power grid. This paper studies a 2-D power system with chaotic oscillation dynamic behaviors through parameter phase portraits, Lyapunov exponents, time-domain waveform graph and proposes fuzzy fast terminal sliding mode controller based on equivalent control to stabilize the power system to synchronization status. Simulation results show that our control scheme can not only speed up convergence rate, but also have smooth control action, reduce control energy and suppress chattering phenomenon effectively.

Improving Operational Performance of Active Magnetic Bearings Using Kalman Filter and State Feedback Control

In this paper, the application of optimal state estimation and optimal state feedback algorithms for real-time active magnetic bearing control is treated. A linear quadratic Gaussian controller, consisting of an extended Kalman filter and an optimal state feedback regulator, is implemented. It is shown that this controller yields improved rotor positioning accuracy, better system dynamics, higher bearing stiffness, and reduced control energy effort compared to the conventionally used proportional-integral-differential control approaches. In addition, a method for compensating unbalance-caused forces and vibrations of the magnetically levitated rotor is presented which is based on the estimation of unknown disturbance forces. All results achieved in this paper are verified by means of measurements.

Discrete controller design for Gaussian- and waveform-type disturbances

This paper describes the general theory of Noisy Discrete Disturbance-Util izing Control (NDDUC) and its application to a planar t erminal homing missile intercept problem. NDDUC is a newlydeveloped control theory relating to the design of discrete c ontrollers which accommodate gaussian disturbances in addition to disturbances that possess structuretT. Previous work in this area applied Linear-Quadratic-Gaussian (LQG) theory to terminal homing missile problems with gaussian n oise and, more recently, disturbanceutilizing control theory to terminal homing missile problems with fTwaveform type disturbances. T his paper describes a new application which combines features of both approaches in terminal homing problems where both classes of disturbances are present. If both waveform-type and noise-type disturbances are present, then NDDUC outperforms the conventional discrete-time LQG estimator/controller. NDDUC effectively utilizes the waveform disturbances to reduce control energy consumption and to obtain lower terminal miss distances.