Switching Logic Research Articles

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Distributed adaptive switching consensus control of heterogeneous multi-agent systems with switched leader dynamics

In this paper, the leader-following distributed consensus control problem is addressed for general linear multi-agent systems with heterogeneous uncertain agent dynamics and switched leader dynamics. Different from most existing results with a single linear time-invariant (LTI) leader dynamics, the leader dynamics under consideration is composed by a family of LTI models and a switching logic governing the switches among them, which is capable of generating more diverse and sophisticated reference signals to accommodate more complicated consensus control design tasks. A novel distributed adaptive switching consensus protocol is developed by incorporating the model reference adaptive control mechanism and arbitrary switching control technique, which can be synthesized by following a two-layer hierarchical design scheme. A numerical example has been used to demonstrate the effectiveness of the proposed approach.

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach.

Tumorigenesis is due to uncontrolled cell division arising from mutations and alterations in the proliferative controls of the cell population. The fight against tumor growth and development has often relied on combination therapy that has been acclaimed as one of the main standards of care in cancer therapeutics and prevention of drug-related resistances. The toxicity of the combinatorial drugs raises a significant concern whenever patients take two or more drugs concurrently at the maximum tolerated dose. A promising solution in tumor treatment involves the administration of the drugs in an alternating or sequential fashion rather than a simultaneous manner. In this paper, we investigate how feasible such an approach is from a mathematical perspective and propose a switched hybrid control systems framework. We explore the response of tumor cells dynamics to sequential drugs administration with the aid of a time-dependent switching strategy. A transit compartmentalized model is employed to describe the tumor cells progression to death. The design of the time-based drug switching logic ensures the proliferating tumor cells are repressed. Simulation results are provided using the tumor growth dynamics with sequential drugs intake to demonstrate the effectiveness of the proposed method in reducing the tumor size. This paper is the first attempt to provide a switched hybrid control systems framework on sequential drug administration to biomedical researchers and clinicians.

High Performance DC Servo System Design Based on Scheduled Disturbance Observer

This paper studies the smooth and precise speed regulation of DC servo system subject to both unknown external disturbances and internal uncertainties. we first propose a disturbance observer based switching control scheme, consists of two DOBs and a switching logic. Thereafter, a novel switching mechanism is meticulously designed to switch the different DOBs according to the variation of output error and settling time, and the stability of the scheduled system is analyzed through Lyapunov stability theory. Experiments on speed regulation show that both smooth dynamic responses at transient state and high accuracy performance at steady state are achieved and the effectiveness of the proposed control scheme is verified.

Implementation of Echo-State Network-Based Control for Power Quality Improvement

This paper proposes a new control algorithm for mitigation of power quality problems at the distribution level. The control algorithm is designed on the basis of echo-state network, which is a type of recurrent neural network. This technique is used to extract fundamental weight components from nonsinusoidal load currents. These weight components are used to evaluate reference grid currents and consequently generate switching logic for the voltage source converter used in the active filter. The shunt-connected active filter with the proposed control algorithm is used to overcome several current-related power quality problems and provides the load compensation. Problems such as the presence of lower order harmonics, insufficient reactive power, and load unbalancing are corrected using a shunt filter in both the power factor correction and voltage regulation modes. The proposed control algorithm is validated on a real time system which is a prototype of a shunt filter. The algorithm is tested in MATLAB using Sim Power Systems and SIMULINK and implemented on a digital signal processor.

Fuzzy Logic Application for Intelligent Control of An Asynchronous Machine

<p>The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method. </p>

Global Finite-Time Adaptive Stabilization of Nonlinearly Parametrized Systems With Multiple Unknown Control Directions

In this paper, the problem of the global finite-time adaptive stabilization for nonlinearly parametrized systems with multiple unknown control directions is addressed. Different from the previous results, the control directions of the considered systems are completely unknown. Adopting the adding a power integrator design technique, we develop an adaptive switching controller with a tuning parameter. Due to control directions unknown, a novel logic switching regulation is established based on Lyapunov function method to overcome this main obstacle. According to this switching rule, the design parameter is tuned online in a switching way. With the help of the obtained adaptive switching controller, the global finite-time stability of the closed-loop systems is shown. To verify the effectiveness of the control algorithm, a simulation example is presented.

Robust stabilization of a class of nonholonomic systems using logical switching and integral sliding mode control

The problem of stabilization of a class of nonholonomic systems that can be transformed into chained form is considered. Since nonholonomic systems cannot be stabilized using smooth and time invariant control, a switching algorithm comprising three distinct steps is employed to stabilize the system. The proposed algorithm allows the system to be reduced into simpler subsystems and affords employing different controllers in each step. Using this approach, desired performance and robustness properties of the feedback control system can be guaranteed. The effectiveness of the proposed algorithm is established by applying it on a unicycle type system moving on hyperbolic plane. In order to make the controller robust, integral sliding mode control is introduced that guarantees robust stabilization. Simulation results confirm the mathematical developments.

Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Dual threshold voltage and sleep switch dual threshold voltage DOIND approach for leakage reduction in domino logic circuits

Subthreshold leakage current becomes the major component of total power dissipation as scaling down the feature size. In this paper, two new circuit techniques are proposed for reducing the subthreshold leakage power consumption in domino logic circuit. Dual threshold voltage DOIND (Domino logic with clock and input dependent transistors) and NMOS sleep switch dual threshold voltage DOIND circuits for low leakage domino logic circuits are presented. High threshold voltage transistors are utilized to reduce the leakage current and a sleep transistor is added to the dynamic node that strongly turnoff all the high threshold voltage transistor and significantly reduce the subthreshold leakage power. The proposed circuit techniques, dual threshold voltage DOIND logic and sleep switch dual threshold voltage DOIND logic reduces the leakage current by 71.46 and 74.86% respectively as compared to standard domino logic circuit. Simulation results also shows that both the circuits are less affected by supply and temperature variations. The proposed sleep switch dual threshold voltage DOIND exhibits 19.95% less power consumption with 24% die area overhead for the buffer circuit as compared to standard domino logic circuit. The proposed sleep switch dual threshold voltage DOIND logic has improved normalized figure of merit of 1.17 as compared to standard domino logic circuit.

Stop and Go adaptive strategy for synchronization of delayed memristive recurrent neural networks with unknown synaptic weights

Although the drive-response synchronization problem of memristive recurrent neural networks (MRNNs) has been widely investigated, all the existing results are based on the assumption that the parameters of the drive system are known in prior, which are difficult to implement in real-life applications. In the present paper, a Stop and Go adaptive strategy is proposed to investigate the synchronization control of chaotic delayed MRNNs with unknown memristive synaptic weights. Firstly, by defining a series of measurable logical switching signals, a switched response system is constructed. Subsequently, by utilizing the logical switching signals, several suitable parameter update laws are proposed, then some different adaptive controllers are devised to guarantee the synchronization of unknown MRNNs. Since the parameter update laws are weighted by the logical switching signals, they will work or stop automatically with the switch of the unknown weights of drive system. Finally, two numerical examples with their computer simulations are provided to illustrate the effectiveness of the proposed adaptive synchronization schemes.

Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem

Abstract This paper studies a large-scale urban traffic light scheduling problem (LUTLSP). A centralized model is developed to describe the LUTLSP, where each outgoing flow rate is described as a nonlinear mixed logical switching function over the source link’s density, the destination link’s density and capacity, and the driver’s potential psychological response to the past traffic light signals. The objective is to minimize the total network-wise delay time of all vehicles in a time window. Three metaheuristic optimization algorithms, named as Jaya algorithm, harmony search (HS) and water cycle algorithm (WCA) are implemented to solve the LUTLSP. Since we adopt a discrete-time formulation of LUTLSP, we firstly develop a discrete version of Jaya and WCA. Secondly, some improvement strategies are proposed to speed up the convergence of applied optimizers. Thirdly, a feature based search operator is utilized to improve the search performance of reported optimization methods. Finally, experiments are carried out based on the real traffic data in Singapore. The HS, WCA, Jaya, and their variants are evaluated by solving 11 cases of traffic networks. The comparisons and discussions verify that the considered metaheuristic optimization methods can effectively solve the LUTLSP considerably surpassing the existing traffic light control strategy.

Hysteresis Analysis and Control of a Metal-Polymer Hybrid Soft Actuator

The number of applications of stimulus-responsive polymers is growing at an impressive rate. The motivation of this contribution is to use a commercially available low-budget silver-coated polyamide (PA6) as a thermo-responsive metal-polymer hybrid soft actuator. Polyamide is a hygroscopic polymer; therefore, its mechanical and physical-chemical properties are affected by exposition to humidity or immersion in water. The effect of water absorption content on the PA6 and silver-coated PA6 monofilament properties, such as mass change and resistance, were evaluated. Moreover, the influence of swelling and shrinking effects on the surface morphology, caused by variations of moisture and water immersion, was investigated. Based on these variations, the dynamics of the resistance of the hybrid material were analyzed in the context of the proposed hysteresis model. An identification procedure of the hysteresis is presented along with an approximation of the upper and lower bound based on a constrained least square approach. A switching logic algorithm for this hybrid dynamic system is introduced, which makes it possible to structure the non-linear function in a switching mode. Finally, a non-linear integral sliding manifold is proposed and tested to control the resulting force of the actuator.hysteresis model. An identification procedure of the hysteresis is presented along with an approximation of the upper and lower bound based on a constrained least square approach. A switching logic algorithm for this hybrid dynamic system is introduced, which makes it possible to structure the non-linear function in a switching mode. Finally, a non-linear integral sliding manifold is proposed and tested to control the resulting force of the actuator.

Effects of Doping Concentration on Device Performance of GaN-based Nano-regime MOSFETs

Gallium nitride (GaN) based metal-oxide semiconductor field-effect transistors (MOSFETs) are promising for switching device applications. The doping of n- and p-layers is varied to evaluate the figure of merits of proposed devices with a gate length of 10 nm. Devices are switched from OFF-state (gate voltage, VGS = 0 V) to ON-state (VGS = 1 V) for a fixed drain voltage, VDS = 0.75 V. The device with channel doping of 1×1016 cm-3 and source/drain (S/D) of 1×1020 cm-3 shows good device performance due to better control of gate over channel. The ON-current (ION), OFF-current (IOFF), subthreshold swing (SS), drain induce barrier lowering (DIBL), and delay time are found to be 6.85 mA/μm, 5.15×10-7 A/μm, 87.8 mV/decade, and 100.5 mV/V, 0.035 ps, respectively. These results indicate that GaN-based MOSFETs are very suitable for the logic switching application in nanoscale regime.

Flexible organic N-channel phototransistor and integrated logic devices

A flexible organic field-effect transistor (OFET) and flexible phototransistor have been fabricated using self-assembled single crystals of a small molecule, viz, N,N’-dioctyl-3,4,9,10-perylene tetracarboxylic diimide (PTCDI-C8). The solution processed single crystals of PTCDI-C8 with strong molecular ordering showed excellent OFET performances having mobility μ∼0.2cm2/Vs. The absence of grain boundaries, traps and intrinsically defect-free nature of the single crystals contributed to the improved device performances in PTCDI-C8 crystalline OFET. The device exhibited high sensitivity to photo-illumination with photoresponsivity value upto Ca. 3A/W and photo to dark current ratio ∼103. Both the transistor and photosensing characteristics of the OFET device were retained even under extreme bending of the flexible substrate, exhibiting robust mechanical flexibility of the OPT. To realize OFET based electro-optical logic circuits, we have suitably integrated the photo signal and electrical signal and demonstrated that binary NOT and OR operations could be performed on the OFET device with light as one of the input signal. The large area deposition of self-assembled organic single crystals with multifunctional activity, such as, photosensing with switching, signal amplification, and electro-optical logic operation opens up immense possibilities for flexible organic logic device and promises great potential for future low-cost plastic optoelectronic applications.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objectiveIn human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. MethodsPetri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. ResultsExperiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). ConclusionThe simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.

Hierarchical task organization in dual tasks: evidence for higher level task representations.

To examine whether hierarchical higher level task representations comprising the task sets of Task 1 (T1) and Task 2 (T2) are activated within each trial in dual-task situations, we combined the psychological refractory period (PRP) paradigm with the task-pair switching logic (Hirsch et al. 2017). In Experiment 1, in which subjects switched between task-pairs including a varying T1 and a constant T2, we found a PRP effect (i.e., worse performance with short stimulus onset asynchrony [SOA] than with long SOA) and task-pair switch costs in T1 and T2 (impaired performance in task-pair switches compared to task-pair repetitions). However, since in Experiment 1 there were no forward and backward response-response compatibility effects that indicated interference between T1 and T2, we could not exclude that the activation of T1 persisted into the next trial despite the intervening T2, and hence, that task-pair switch costs are due to repetition-priming effects of T1 across task-pairs rather than due to persisting activation of task-pair representations. In Experiment 2, we used a modified task-pair switching logic with a constant T1 and a varying T2, and replicated task-pair switch costs under conditions that not only rule out repetition-priming effects of T1 across task-pairs as the source of task-pair switch costs but also disentangle the effects of switching task-pairs from those of switching T1. These effects were associated in previous studies using the original task-pair switching logic. Thus, the findings of the present study strongly suggest that hierarchical higher level task representations are activated during dual-task processing.

Robust global bimodal rest-to-rest attitude control of rigid body using unit quaternion

This work proposes a hysteresis-based controller with two binary logic variables for the rest-to-rest control of attitude represented by quaternions. The control system has a hybrid nature given by the presence of a switching logic variable that indicates which quaternion representation of the reference attitude should be followed and by a second logic variable which indicates the chattering prone region and has the overall effect of adapting the hysteresis width. It is presented a formal proof that the proposed bimodal control leads to global stability without unwinding and is robust against chattering for any measurement noise with magnitude less than or equal to a given maximum value. Simulation results indicate that the proposed adaptive hysteresis width controller can be advantageous when the initial and final angular velocities have small magnitudes as is the case of the rest-to-rest attitude control problem.

Robust control of nonholonomic wheeled mobile robot with past information: Theory and experiment

In this article, a robust hybrid control method is presented for efficient path tracking control of a nonholonomic wheeled mobile robotic system under parametric and nonparametric variations. The present control law is a paradigm shift to control a wheeled mobile robot over a predefined trajectory by fusing the best features of the switching control logic as well as time-delayed control logic. The proposed hybrid control strategy aims at reducing the effort required for modeling the complex wheeled mobile robotic systems by approximating the unknown dynamics using input and feedback information of past time instances. Furthermore, the proposed methodology significantly reduces the approximation error arising from finite time-delay through the switching logic without any prior knowledge of the uncertainty bound. A new stability analysis for the time-delayed control is proposed which establishes an analytical relation between the controller performance and the approximation error. Performance of the proposed hybrid controller is tested with a real-life wheeled mobile robot and improved tracking performance is observed compared to conventional robust control strategies even with the incorporation of dynamic parametric uncertainties.

Speed recovery strategy for the inertia response control of DFIGs: extended state observer based approach

The inertia response control (IRC) of doubly-fed induction generators is capable of providing the power system with controlled inertia by releasing mechanical power to or absorbing electrical power from the system when the system frequency experiences sudden changes. However, it will result in deviation of the rotor speed from the optimal working point and unexpectedly reduce the captured wind power. To restore the rotor speed as quickly after an IRC action as possible, in this study, the authors derive a novel rotor speed recovery strategy based on the extended state observer (ESO) technique. They first develop two ESOs to accurately estimate the captured wind power by the wind turbine and the system unbalance power, respectively. They then synthesise the rotor speed recovery strategy based on such estimates, including the optimal recovery timing and the control modes as well as the associated switching logics. Detailed simulations carried on PSCAD/EMTDC show that the authors’ controller outperforms the traditional proportional–integral controller in the aspects of dynamical performance, the robustness to varying working conditions and the capability to prevent the system frequency from a second drop.