Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles （UCAVs） is a complicated global optimum problem. Particle Swarm Optimization （PSO） is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Diseretization （CPTD） method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.     

An Online Observer for Minimization of Pulsating Torque in SMPM Motors

A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method.     

Supervised Neural Q_learning based Motion Control for Bionic Underwater Robots

Bionic underwater robots have been a hot research area in recent years. The motion control methods for a kind of bionic underwater robot with two undulating fins are discussed in this paper. The equations of motion for the bionic underwater robot are described. To apply the reinforcement learning to the actual robot control, a Supervised Neural Q_learning (SNQL) algorithm is put forward. This algorithm is based on conventional Q_learning algorithm, but has three remarkable distinctions: (1) using a feedforward neural network to approximate the Q_function table; (2) adopting a learning sample database to speed up learning and improve the stability of learning system; (3) introducing a supervised control in the earlier stage of learning for safety and to speed up learning again. Experiments of swimming straightforward are carried out with SNQL algorithm. Results indicate that the SNQL algorithm is more effective than pure neural Q_learning or supervised control. It is a feasible approach to figure out the motion control for bionic underwater robots.     

Control of substrate concentration in a continuous bioprocess

This paper is the first one presenting an application of the control law based on the ratio of measured and optimal values for a real fermentation pilot plant operated in continuous mode. A controller of this type takes into account the process variables and may be designed by using classical linear methods. A simple algorithm is applied for a real CSTR to control the substrate concentration. The results obtained confirm that this approach offers the possibility to combine simplicity and effectiveness in bioprocess control.     

Control of a bioreactor using a neural network

This paper describes an experimental investigation concerning the use of neural networks to achieve the non-linear control of a continuous stirred tank fermenter. The influent dilution rate and the substrate concentration have been selected as control variables. The backpropagation learning algorithm has been used for both off-line and on-line identification of the inverse model which provides the control action. Experimental results show the performance and the implementation simplicity of this control approach.     

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.     

A Supervisory Control Approach for Execution Control of an FMC

This paper presents a generic methodology based on formal language theory for the modeling and control of flexible manufacturing cell (FMC) systems. The motivating idea behind the overall approach stems from the supervisory control theory under the framework of Ramadge and Wonham. Essentially, we characterize the asynchronous and dynamic behavior of an FMC as a regular language and formulate the control logic generation problem as a sublanguage calculation problem, which requires the resulting language to satisfy at least two properties: maximal permissiveness and controllability. Then an algorithm for resolving the problem is presented. Based on the solution of the problem called supervisor, we propose a controller architecture that guarantees coordinated operation of an FMC through the regulation of occurrences of events. An adaptive control policy that regenerates supervisors on changes in task configurations is presented and a dynamic equation that describes the evolution of the control logic along time is derived. Then, we show that the proposed maximally permissive adaptive control policy has a number of preferred properties, including computational efficiency and consistency between the successive supervisors. Finally, a controller for an example FMC is implemented, using the object-oriented software modules. Our procedure has the merit of mathematical soundness, modular design, and systematic implementation.     

Control of Boolean networks: hardness results and algorithms for tree structured networks

Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.     

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.     

Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test

Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.     

Development of a Synthetic Positive Control Which Also Detects Plasmid Contamination in Diagnostic Polymerase Chain Reaction

This paper describes a technique for developing a positive control for use in a nested PCR to show that the PCR has functioned correctly with both outer and inner primers designed for the diagnostic amplification of 618 and 317 bp products, respectively. This positive control produces a larger product than the diagnostic sample that can be discriminated on an agarose gel. This technique is advantageous over traditional cloning of the diagnostic PCR product itself by: (1) making it visually easy to detect plasmid contamination and thus prevent false positives from the plasmid; (2) develop a positive control when the target organism is at a very low prevalence, so initial detection is not relied on for cloning positive controls (this will ensure the PCR is working correctly prior to diagnostic sampling, reducing false negatives); or (3) for developing a PCR and determining the sensitivity prior to the use of diagnostic samples. The methods used to produce this nested positive control demonstrate how to use large oligonucleotide primers in PCR without nonspecific binding occurring.     

A Novel Utility Function for Energy-Efficient Power Control Game in Cognitive Radio Networks

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.     

Learning Control for Biomimetic Undulating Fins: An Experimental Study

Learning control should focus on imitating natural fish's adaptability to complex and dynamic environment to some extent, rather than mimicking streamlined shapes or specific actuators to develop more mechanical prototypes. In this paper, an experimental study on a proposed learning control of the robotic undulating fin, RoboGnilos, is suggested and explored. This study takes inspirations from biological world to practical control algorithms. In detail, an iterative learning scheme based control is studied with the cooperation of a filter to reduce the measurement noise, and a curve fitting component to keep the necessary phase difference between neighboring fin rays. Moreover, the iterative learning control algorithm is designed and implemented for practical applications. The experimental results validate that the proposed learning control can effectively improve the propulsion of RoboGnilos. For instance, the steady propulsion velocity may be enhanced by over 40% with some specified parameters.     

Self-adaptive particle swarm optimization: a review and analysis of convergence

Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori parameter tuning makes the implicit assumption that the optimal parameters of the PSO algorithm are not time-dependent. To address these issues, self-adaptive particle swarm optimization (SAPSO) algorithms adapt their control parameters throughout execution. While there is a wide variety of such SAPSO algorithms in the literature, their behaviours are not well understood. Specifically, it is unknown whether these SAPSO algorithms will even exhibit convergent behaviour. This paper addresses this lack of understanding by investigating the convergence behaviours of 18 SAPSO algorithms both analytically and empirically. This paper also empirically examines whether the adapted parameters reach a stable point and whether the final parameter values adhere to a well-known convergence criterion. The results depict a grim state for SAPSO algorithms; over half of the SAPSO algorithms exhibit divergent behaviour while many others prematurely converge.     

Control of functional electrical stimulation with extended physiological proprioception

The use of functional electrical stimulation (FES) of muscle for paraplegic locomotion, or grasp augmentation in tetraplegia, is limited by the variability in muscle response to stimulation as a result of several external and internal factors. Previous approaches to this problem have used position-servo controllers, which have been shown to function satisfactorily in the laboratory. However, such systems will fail should obstacles be encountered or should the stimulation hardware develop a fault. To prevent such potentially dangerous failures some form of sensory feedback is required. This paper describes the first application of a technique known as extended physiological proprioception (EPP) to the control of FES to compensate for muscle response variability and provide proprioceptive feedback via the appropriate sensory pathways. In the experimental system described, a paraplegic subject controlled the extension of his paralysed knee by shoulder protraction. A Bowden cable linked the two joints, and a dynamometer in this cable was used to derive the control signal for a computer-controlled stimulator which delivered surface stimulation to the quadriceps muscle group. Modelling and parameter identification were performed by analysis of the step response, and the controller was designed from consideration of the root locus. The advantages of the system, in terms of improved proprioceptive feedback and reduced limb-positioning error were assessed in a test of joint positioning accuracy with vision occluded. The EPP system showed improvements over both open and closed-loop position-servo controllers.     

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.     

Automated production support for the bioprocess industry

This paper describes the application of Artificial Intelligence and Multivariate Statistical Techniques to two industrial fermentation systems. In the first example, an Expert System is shown to provide tighter control of an important process parameter. This is shown to lead to improved consistency of operation. In the second application, Principal Component Analysis is applied to a final stage fermentation production facility. The results presented indicate that the algorithm can provide concise indicators of process faults that can be presented to the operators to assist them in taking suitable corrective actions.     

Proportional-Integral-Derivative (PID) Control of Secreted Factors for Blood Stem Cell Culture

Clinical use of umbilical cord blood has typically been limited by the need to expand hematopoietic stem and progenitor cells (HSPC) ex vivo. This expansion is challenging due to the accumulation of secreted signaling factors in the culture that have a negative regulatory effect on HSPC output. Strategies for global regulation of these factors through dilution have been developed, but do not accommodate the dynamic nature or inherent variability of hematopoietic cell culture. We have developed a mathematical model to simulate the impact of feedback control on in vitro hematopoiesis, and used it to design a proportional-integral-derivative (PID) control algorithm. This algorithm was implemented with a fed-batch bioreactor to regulate the concentrations of secreted factors. Controlling the concentration of a key target factor, TGF-β1, through dilution limited the negative effect it had on HSPCs, and allowed global control of other similarly-produced inhibitory endogenous factors. The PID control algorithm effectively maintained the target soluble factor at the target concentration. We show that feedback controlled dilution is predicted to be a more cost effective dilution strategy compared to other open-loop strategies, and can enhance HSPC expansion in short term culture. This study demonstrates the utility of secreted factor process control strategies to optimize stem cell culture systems, and motivates the development of multi-analyte protein sensors to automate the manufacturing of cell therapies.     

一种非线性多阶段动力系统的最优控制及数值优化

针对间歇发酵过程的非线性多阶段动力系统,建立了以初始浓度为控制变量、以生产强度为性能指标的最优控制模型.证明了非线性多阶段动力系统的主要性质、最优控制的存在性及达到最优解的必要条件.构造了优化算法并应用于实际数据计算,其数值结果表明了本文模型与算法的有效性.