Dynamics Of Control System Research Articles

Self-structuring fuzzy-neural backstepping control with a B-spline-based compensator

To relieve the burdens of network controller design and approximation error bound determination, a self-structuring fuzzy-neural backstepping control system (SSFNBS) with a B-spline-based compensator is proposed. In this paper, a network-identification-based control is represented where the self-structuring fuzzy neural network-based (SSFNN) is used as the observer to approximate the controlled system dynamics. To balance the tradeoff between the structure efficiency and the identification accuracy, a structure learning mechanism of the node-adding process and the node-pruning process is introduced. On the other hand, the B-spline-based compensator is introduced to dispel the effect of approximation error. With the adoption of B-spline functions, the compensation controller can be given in a recurrent way based on the introduction of knot vector and the drawbacks of the conventional compensation controllers can be freed. With the introduction of the B-spline function, the proposed SSFNBS features the following advantages: (1) the capability of network-based controller is improved, (2) the design of the compensation controller can be easily established based on the characteristics of the B-spline function, (3) the stability of closed-loop control system is guaranteed by the means of Lyapunov function with the tuning law of the B-spline-based compensator. To investigate the capabilities of the proposed approach, the SSFNBS is applied to the nonlinear system, chaotic system, and wing rock motion control problems. Through the simulation results the advantages of the proposed SSFNBS can be observed.

On relative degree, chattering and fractal nature of parasitic dynamics in sliding mode control

With respect to relative degree and chattering in sliding mode (SM) control systems, the notion of fractal dynamics is introduced, and a conjecture is formulated that the character of parasitic dynamics of real control systems is fractal. A model of fractal dynamics is proposed. The characteristics of fractal dynamics are studied in the frequency and time domains. It is shown that with fractal parasitic dynamics SM control systems will always feature chattering and non-ideal closed-loop performance. An example of analysis is provided.

Identification of the contribution of the ankle and hip joints to multi-segmental balance control

BackgroundHuman stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects.MethodsThe method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique.ResultsIn the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque.ConclusionThe method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate legs.

Bilateral Control of a Velocity Control System using Electric and Hydraulic Actuators

SUMMARY Working in extreme environments (e.g., at disaster sites) is dangerous for humans. Hence, it is important to use teleoperated robots in such extreme environments. Because heavy lifting work is involved, hydraulic actuators with high output are more commonly used than electric actuators. Therefore, it is necessary to operate remotely controlled robots driven by hydraulic actuators. Bilateral control is remote control with a sense of force. Because most bilateral control systems have been studied mainly using electric actuators, bilateral control using hydraulic actuators is still a challenge. In this study, we propose placing a bilateral controller between an electric actuator on the master side and a hydraulic actuator on the slave side using position and velocity. The controller exhibits the dynamics of bilateral control systems using position and velocity. In addition, the control performance of conventional methods and that of the proposed method are compared and experimentally verified.

Regulation of Cell Proliferation and Migration in Glioblastoma: New Therapeutic Approach

Glioblastoma is the most aggressive brain cancer with the poor survival rate. A microRNA, miR-451, and its downstream molecules, CAB39/LKB1/STRAD/AMPK, are known to play a critical role in regulating a biochemical balance between rapid proliferation and invasion in the presence of metabolic stress in microenvironment. We develop a novel multi-scale mathematical model where cell migration and proliferation are controlled through a core intracellular control system (miR-451-AMPK complex) in response to glucose availability and physical constraints in the microenvironment. Tumor cells are modeled individually and proliferation and migration of those cells are regulated by the intracellular dynamics and reaction-diffusion equations of concentrations of glucose, chemoattractant, extracellular matrix, and MMPs. The model predicts that invasion patterns and rapid growth of tumor cells after conventional surgery depend on biophysical properties of cells, dynamics of the core control system, and microenvironment as well as glucose injection methods. We developed a new type of therapeutic approach: effective injection of chemoattractant to bring invasive cells back to the surgical site after initial surgery, followed by glucose injection at the same location. The model suggests that a good combination of chemoattractant and glucose injection at appropriate time frames may lead to an effective therapeutic strategy of eradicating tumor cells.

Canonical ensemble simulation of biopolymers using a coarse-grained articulated generalized divide-and-conquer scheme

In this paper, a scheme for the canonical ensemble simulation of the coarse-grained articulated polymers is discussed. In this coarse-graining strategy, different subdomains of the system are considered as rigid and/or flexible bodies connected to each other via kinematic joints instead of stiff, but elastic bonds. Herein, the temperature of the simulation is controlled by a Nosé–Hoover thermostat. The dynamics of this feedback control system in the context of multibody dynamics may be represented and solved using traditional methods with computational complexity of O(n3) where n denotes the number of degrees of freedom of the system. In this paper, we extend the divide-and-conquer algorithm (DCA), and apply it to constant temperature molecular simulations. The DCA in its original form uses spatial forces to formulate the equations of motion. The Generalized-DCA applied here properly accommodates the thermostat generalized forces (from the thermostat), which control the temperature of the simulation, in the equations of motion. This algorithm can be implemented in serial and parallel with computational complexity of O(n) and O(logn), respectively.

NCSWT: An integrated modeling and simulation tool for networked control systems

Networked Control Systems (NCS) are becoming increasingly ubiquitous in a growing number of applications, such as groups of unmanned aerial vehicles and industrial control systems. The evaluation of NCS properties such as stability and performance is very important given that these systems are typically deployed in critical settings. This paper presents the Networked Control Systems Wind Tunnel (NCSWT), an integrated modeling and simulation tool for the evaluation of Networked Control Systems (NCS). NCSWT integrates Matlab/Simulink and ns-2 for modeling and simulation of NCS using the High Level Architecture (HLA) standard. The tool is composed of two parts, the design-time models and the run-time components. The design-time models use Model Integrated Computing (MIC) to define HLA-based model constructs such as federates representing the simulators and interactions representing the communication between the simulators. MIC techniques facilitate the modeling and design of complex systems by using abstractions defined in domain-specific modeling languages (DSMLs) to describe the systems. The design-time models represent the control system dynamics and networking system behaviors in order to facilitate the run-time simulation of a NCS. The run-time components represent the main software components and interfaces for the actual realization of a NCS simulation using the HLA framework. Our implementation of the NCSWT based on HLA guarantees accurate time synchronization and data communication. Two case studies are presented to demonstrate the capabilities of the tool as well as evaluate the impact of network effects on NCS.

Evolution and the microbial control of insects

Insect pathogens can be utilized in a variety of pest management approaches, from inundative release to augmentation and classical biological control, and microevolution and the consideration of evolutionary principles can potentially influence the success of all these strategies. Considerable diversity exists in natural entomopathogen populations and this diversity can be either beneficial or detrimental for pest suppression, depending on the pathogen and its mode of competition, and this should be considered in the selection of isolates for biological control. Target hosts can exhibit considerable variation in their susceptibility to entomopathogens, and cases of field-evolved resistance have been documented for Bacillus thuringiensis and baculoviruses. Strong selection, limited pathogen diversity, reduced gene flow, and host plant chemistry are linked to cases of resistance and should be considered when developing resistance management strategies. Pre- and post-release monitoring of microbial control programs have received little attention; however, to date there have been no reports of host-range evolution or long-term negative effects on nontarget hosts. Comparative analyses of pathogen population structure, virulence, and host resistance over time are required to elucidate the evolutionary dynamics of microbial control systems.

Improved State-Feedback Control for Current Source Converter of SMES

Current source converter (CSC) can control power transfer between superconducting coil and the AC power system, which is the key component of superconducting magnetic energy storage (SMES). This paper presents an improved state-feedback control for CSC of SMES. Adopting the discretization method, the design of decoupled state-feedback control system is simple and straight. To overcome the drawbacks of the discretization method and improve the control system dynamics, the effect of the control time delay is considered. Furthermore, by selecting the current error vector and the output voltage error vector as the additional state variables, a linear state feedback loop is added to the control system to achieve the feature of deadbeat. The steady state performance and dynamic response are investigated. MATLAB simulation is performed to evaluate the performance of the proposed control method.

Bifurcations in phase oscillator networks with a central element

A system of phase oscillators with identical natural frequencies and the star-like architecture of connections is considered. Interaction functions are described by two terms of Fourier expansion. Bifurcation analysis of small systems containing 3 or 4 oscillators has been performed. The results are summarized in bifurcation diagrams that provide a full description of the boundaries between regions with different dynamics and the types of bifurcations that lead to the changes in the topology of phase space. The bifurcations include changes of fixed point stability and formation (destruction) of limit and heteroclinic cycles. For the system with 4 oscillators chaotic behaviour has been investigated. The results can be useful to control system dynamics through an appropriate choice and variation of parameter values. The generalization of the results to the systems with an arbitrary number of oscillators and application of the results in computational neuroscience are discussed.

A self-sensing magnetorheological damper with power generation

Magnetorheological (MR) dampers are promising for semi-active vibration control of various dynamic systems. In the current MR damper systems, a separate power supply and dynamic sensor are required. To enable the MR damper to be self-powered and self-sensing in the future, in this paper we propose and investigate a self-sensing MR damper with power generation, which integrates energy harvesting, dynamic sensing and MR damping technologies into one device. This MR damper has self-contained power generation and velocity sensing capabilities, and is applicable to various dynamic systems. It combines the advantages of energy harvesting—reusing wasted energy, MR damping—controllable damping force, and sensing—providing dynamic information for controlling system dynamics. This multifunctional integration would bring great benefits such as energy saving, size and weight reduction, lower cost, high reliability, and less maintenance for the MR damper systems. In this paper, a prototype of the self-sensing MR damper with power generation was designed, fabricated, and tested. Theoretical analyses and experimental studies on power generation were performed. A velocity-sensing method was proposed and experimentally validated. The magnetic-field interference among three functions was prevented by a combined magnetic-field isolation method. Modeling, analysis, and experimental results on damping forces are also presented.

Adjusting Phenotypes by Noise Control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks.

On analyses of the dynamics of an automatic control system with controlled rectifiers

A generalized analyses of an automatic control system with controlled rectifiers and DC motors taking into consideration the nature of the transient processes in the currents’ loop is carried out.

Robust Line-Voltage Sensorless Control and Synchronization of LCL -Filtered Distributed Generation Inverters for High Power Quality Grid Connection

This paper presents a robust control scheme for high power quality grid connection of inductor–capacitor–inductor (LCL)-filtered distributed generation (DG) inverters. The presence of the LCL filter complicates the dynamics of the inverter control system, particularly when the uncertain nature of the grid background distortion and system parameters is considered. The proposed scheme addresses such practical difficulties by providing: 1) robust and simple active damping control performance under grid and filter parameter variation; 2) suppression of grid-induced distortion without a-priori knowledge of the grid background distortion and unbalance via real-time generation of the frequency modes and disturbances that should be eliminated from the closed-loop current control system; 3) robust deadbeat digital control performance that maximizes the dynamic performance of the converter; and 4) robustness against interaction dynamics between active damping and current tracking controllers. Furthermore, the proposed control scheme facilitates line-voltage sensorless current control and grid-synchronization performance, which enhances the reliability and cost measures of the DG interface. Theoretical analysis and comparative evaluation results are presented to demonstrate the effectiveness of the proposed control scheme.

Qualitative Network Modeling of the Myc-p53 Control System of Cell Proliferation and Differentiation

A kinetic model of a molecular control system for the cellular decision to proliferate or differentiate is formulated and analyzed for the purpose of understanding how the system can break down in cancer cells. The proposed core of this control system is composed of the transcription factors Myc and p53. The network of interactions between these factors involves negative and positive feedback loops that are linked to pathways involved in differentiation, cell cycle, and apoptosis. Understanding the dynamics of the Myc-p53 control system is aided by the postulate that there exists a cancer zone defined as a range of oncogenic Myc activities where the probability of initiating cancer is high. We propose that an essential role of p53 is to prevent the system from entering or staying too long in the cancer zone by downregulating Myc or, when Myc activity somehow becomes too high, by inducing apoptosis, cell cycle arrest, or differentiation. Kinetic modeling illustrates how deletions or aberrations in PTEN, MDM2, and ARF (genes implicated in various cancers, including glioma) affect the Myc-p53 control system. In addition, computer simulations demonstrate how this control system generates different cellular phenotypes characterized by rates of cellular differentiation and proliferation.

Non-coherence in safety of switching control systems and its Markov analysis

This article appropriately describes the safety dynamics of switching control systems under the most realistic and popular failure mode of a switching unit, ‘locking’, by introducing a new idea of situation-dependent basic events in dynamic fault trees with restorable basic events. As a result, possible non-coherence in the safety dynamics is pointed out for the first time. This article also presents a safety analysis framework based on the Markov analysis in order to address the non-coherence correctly.

Interaction of fast and slow dynamics in endocrine control systems with an application to β-cell dynamics

Endocrine dynamics spans a wide range of time scales, from rapid responses to physiological challenges to with slow responses that adapt the system to the demands placed on it. We outline a non-linear averaging procedure to extract the slower dynamics in a way that accounts properly for the non-linear dynamics of the faster time scale and is applicable to a hierarchy of more than two time scales, although we restrict our discussion to two scales for the sake of clarity. The procedure is exact if the slow time scale is infinitely slow (the dimensionless ε-quantity is the period of the fast time scale fluctuation times an upper bound to the slow time scale rate of change). However, even for an imperfect separation of time scales we find that this construction provides an excellent approximation for the slow-time dynamics at considerably reduced computational cost. Besides the computation advantage, the averaged equation provided a qualitative insight into the interaction of the time scales. We demonstrate the procedure and its advantages by applying the theory to the model described by Tolić et al. [I.M. Tolić, E. Mosekilde, J. Sturis, Modeling the insulin–glucose feedback system: the significance of pulsatile insulin secretion, J. Theor. Biol. 207 (2000) 361–375.] for ultradian dynamics of the glucose–insulin homeostasis feedback system, extended to include β-cell dynamics. We find that the dynamics of the β-cell mass are dependent not only on the glycemic load (amount of glucose administered to the system), but also on the way this load is applied (i.e. three meals daily versus constant infusion), effects that are lost in the inappropriate methods used by the earlier authors. Furthermore, we find that the loss of the protection against apoptosis conferred by insulin that occurs at elevated levels of insulin has a functional role in keeping the β-cell mass in check without compromising regulatory function. We also find that replenishment of β-cells from a rapidly proliferating pool of cells, as opposed to the slow turn-over which characterises fully differentiated β-cells, is essential to the prevention of type 1 diabetes.

Research on the Pulse Width Modulated Pneumatic Servo Systems with Long Transmission Lines

This paper presents a nonlinear model of the pulse width modulated(PWM) pneumatic servo systems with long transmission lines which has a huge potential to be used in the automatic production lines and other industrial applications, for the advantages of low cost and clean. An averaging approach is adopted to describe the equivalent continuous-time dynamics of a PWM controlled nonlinear system. With this model, the simulation is taken with a fuzzy logic controller. The simulation results indicate that the PWM pneumatic servo system with long lines have good performance both in the position control and tracking ability.

Sliding mode control of T–S fuzzy descriptor systems with time-delay

This paper is concerned with the problem of sliding mode control (SMC) of a class of Takagi–Sugeno (T–S) fuzzy descriptor system with time-delay. An integral-type sliding surface function is designed by taking the descriptor matrix into account, thus the resulting sliding mode dynamics is a full-order uncertain T–S fuzzy descriptor time-delay system. By the use of the delay partitioning technique, a delay-dependent criterion is established, which ensures the sliding mode dynamics to be regular, impulse-free and stable. Moreover, an SMC law is synthesized to guarantee the stability of the closed-loop system, and the proposed SMC law can drive the dynamics of controlled system into a designated sliding surface in finite time. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theories.

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.