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Randomness is common in many real industrial processes, its modeling problem has always been an important research content in the field of industrial control. Probability density function(PDF) is an effective means to solve the problem of stochastic distributed system modeling This paper based on Bspline basis function sets up the model of output and input data. Due to the fact that there are often many constraints in the actual industrial process and high requirements for real-time and accuracy, this paper proposes to combine the PDF model with predictive control to achieve effective tracking of system output setpoints. Predictive control optimization is more complicated and computationally intensive, so the predictive functional control (PFC) strategy is adopted to reduce the degree of freedom of the optimization problem. Applying PFC based on PDF modeling to the molecular weight distribution (MWD) of the polymerization process, and compared with generalized predictive control (GPC) algorithm, the validity of the proposed method is verified.

Large scale power grid is the most important foundation of energy internet. Due to the scale of energy internet increasingly expands with high complexity, using conventional power flow model cannot achieve reliable convergence in all cases. In order to avoid energy internet power flow insolvability, a new improved DC power flow model with virtual transmission channel is proposed in this paper. This model takes into account the active power loss of power grid to reduce the calculation error of standard DC power flow algorithm. The active power loss of each branch is simulated with equivalent loads located at both ends of the branch. An iterative algorithm is adopted to calculate the equivalent loads. In order to overcome power transmission congestion which is one of the main reasons leading to power flow insolvability, a virtual transmission channel model is presented. Virtual transmission channel is parallel to the related transmission line or transformer where the active power flow exceeds rated value. Some virtual transmission channels may be added to the original network during iteration process. Plenty of actual calculation examples show that the proposed improved DC power flow model is practical and effective. Using virtual transmission channel can significantly enhance the power transmission capacity, and helps to restore energy internet power flow solvability.

Nowadays, we get more and more remote sensing (RS) images which cannot be well processed or used by manual analysis or existing automatic methods. In the past few years, the object detection technology has greatly developed, especially after the usage of CNN in object detectors. However, Object detection in large scale RS images is still a challenging tasks which needs further study. Compared to natural images, RS images include much more objects in different sizes with a larger scope. Therefore, it is extraordinarily time-consuming to detect small objects in a large-scale RS image, since this work needs more scale and location traverses. Algorithms for common images cannot tackle the problem of some special object detection, like aircraft detection, in RS images. In this paper, we introduce an extra Region Proposal strategy named Region Locating Network (RLN) to improve the Faster RCNN framework. The proposed RLN locates spectacular areas where aircrafts are usually found, like parts of the runway and the parking apron. Based on the locating result, we can use Faster RCNN to detect airplanes in several smaller image regions. Extensive experiments show that the proposed method has oblivious improvement in recall rate, accuracy and computing efficiency.

In this paper, the pumped storage power station and other conventional power sources will be considered to participate in the peaking regulation of power system, and the adjustment order to obey has been studied. Based on the key concepts including continuous ramping events and unit capacity adjustment cost, an comprehensive cost optimization model for the adjustment order of different peaking-regulation measures is proposed, in which the characteristics of the system load ramping, pumped storage power station characteristics, all kinds of peak adjustment means cost have been taken into consideration. The example has shown that the method can effectively realize the sorting of many kinds of peaking regulation measures in the power system. It also has shown that the calling order of the pumped storage power station not always takes the first place as the conventional consideration, which is necessary to combine with the specific conditions of the system.

Software Defined Network (SDN) is deemed to be one of the promising solutions for the next-generation wireless communication system. Software Defined Protocol (SDP) is the data plane equivalent of SDN. The design objective of SDP is to make data processing protocol stacks programmable based on platform virtualization and functionality modularization to redefine the wireless network architecture. In this paper, a protocol module multiplexing algorithm for SDP is proposed, where all the function blocks of protocol processing are abstracted to protocol modules and resources are optimally allocated to the protocol modules, targeting to minimize the total processing time of data flows. Numerical simulations show that the proposed module multiplexing algorithm significantly outperforms the baseline algorithm in terms of throughput. The baseline is set as the traditional algorithm in which resources are allocated to all modules uniformly. Throughput gains about 156% and 128% over the baseline are observed, on uplink and downlink respectively. It is also observed that the throughput of the module multiplexing algorithm increases as the total module number increases.

Building an accurate and reasonable time-series model of wind power is of great significance for the power system operation and planning. This paper proposes a Markov chain Monte Carlo (MCMC) method to simulate the time series of the wind power. Taking into account the nonstationarity and stochastics of the wind power, this model constructs Markov chain for time series of the wind power to keep the stochastics and utilize the Gibbs sampling to realize the probability transition matrix. A numerical case study with one-year real measurement of the wind power from one large wind farm in Gansu province of China is applied to illustrate the validity the proposed model: the simulated wind-power time series of arbitrary length that accurately reproduce the statistical behavior of the original series.

With the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be obtained and widely used in military and civilian fields. Detecting typical targets in satellite images is a challenging task due to the complicated background. Traditional manually engineered features (i.e. HOG, Gabor feature and Hough transform, etc.) do not work well for massive high-resolution remote sensing image data. Thus, we are expected to find an efficient way to automatically learn the presentations from the massive image data and increase the computational efficiency of target detection. Comparing to the general objects in nature images, the edge information of targets in satellite images shows more distinctive and concise characteristics. This paper proposes a new target detection framework based on Edge Boxes and Convolutional Neural Networks (CNN). CNN can learn rich features automatically and is invariant to small rotation and shifts, has achieved state of-the-art performance in many image classification databases. Edge Boxes can generate a smaller set of object proposals based on the edges of objects. The proposed method can reduce the computational time of the detector. Extensive experiments demonstrate that the proposed framework is effective in typical target detection systems.

The land use and land cover classification is an important and hot research topic in remote sensing image processing. How to use information effectively in remote sensing data to categorize different land use and land cover scenes needs urgent attention. In this paper, we analysis the Bag-of-Word model based feature extracting method systematically, and propose the Saliency Map Cooperated (SMC) coding strategy according characters of remote sensing images. The proposed SMC takes into account both the primary objects and partial of large scale objects in remote sensing images, with little effecting on texture dominated images. Extensive experimental results show the efficiency of the proposed SMC coding strategy.

Road information can be used for several applications, such as essential modes of transportation and updating geographic information systems (GIS) from VHR remote sensing images. In recent years, with the development of multi-sensor and multi-resolution remote sensing techniques, we have acquired a large quantity of high-resolution satellite images. Accurate road extraction from VHR sensing image has drawn considerable attention and a great deal of effort has been devoted to this topic.

Dynamic modeling and controller design for three dimensional (3D) output probability density function (PDF) are studied in this paper. The construction of 3D square-root model and the conditions it should meet under the nature constraint are analyzed firstly. Then based on 3D B-spline basis function, the square-root dynamic model of 3D PDF is set up with output and input data of the system through recursive least square (RLS) algorithm, and a tracking performance index is chosen to design controller based on the 3D square-root dynamic model, at last a dynamic system is constructed for simulation study and the results verify the effectiveness of the presented controller.

In this paper, the algorithm of multi-model predictive control based on neural network is proposed and applied in 160MW Bell-Astrom model. Firstly, the algorithm is described. Each sub-controller is designed based on state space model predictive control, and the global controller is gained from neural network weights. Then, sub-models of Bell-Astrom are given. Lastly, multi-model predictive control and constrained multi-model predictive control are applied in Bell-Astrom model. The algorithm is effective in controlling the unit and has good performance.

Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes.

In this paper, we propose to use Bag-of-Words (BoW) model based methods for the remotely sensed images classification, and also attempt to do research about the image quality and algorithm evaluation. We introduce the Locality-constrained Linear Coding (LLC) for the remotely sensed image classification, and the hard vector quantization and soft vector quantization are adopted for experiments. Regards to the algorithm evaluation, we propose to use a hierarchical model for the image sample quality evaluation as a starting point. The model includes global, class and image level quality, which considers both the image quality and the class separability. Experiments are conducted on the image database selected from google earth and three synthesized databases. Experimental results show that the BoW model based methods are suitable for the remote sensing image classification. However, the advantage of LLC is not as significant as in nature scene image classification. Results of sample evaluation show that the number of middle quality images in a database is the largest, and the quality distribution of a database is consistent with the classification results. The hierarchical model provides an effective quality measurement of remote sensing image samples.

In this paper, the nonlinear dynamic model of the wind energy conversion system (WECS) is established. Due to the non-Gaussian randomness in the WECS introduced by the wind speed, stochastic distribution controller is designed for both maximum power point tracking (MPPT) and attenuation of disturbance. Compared with conventional PI controller, the effectiveness of the proposed approach is verified by some experimental results.

The top-k dominating query is an emerging technology which is used to obtain k services that dominate the highest number of services. The advantage of top-k dominating query is even more obvious in dealing with concurrent requests. In this paper, we improve the dominance-based approach that adopts top-k dominating queries to generate a solution for a given request. Based on the proposed service selection scheme, k outstanding services would be efficiently obtained by using our optimal algorithms. The experiment demonstrates that our optimal approach outperforms the previous dominance-based method and traditional method for service selection.

Rate control and adaptation is an important issue for video communications over cellular networks. In this paper, we propose a quality-aware feedback-free rate control and adaptation scheme for downlink real-time video communications over Long Term Evolution (LTE) networks. In our scheme, the send bit rate is adapted automatically according to the estimated packet loss due to the expiration of packet delay deadline based on queueing analysis by taking into account both packet queueing delay and transmission delay. This differs from existing rate adaptation methods which adapt send bit rate according to either packet loss or delay information explicitly feedback from the user side. The proposed scheme can maximize the system capacity in terms of the number of supportable users while providing satisfying user experience in terms of the received video quality and playback smoothness. Experimental results demonstrate the effectiveness of the proposed scheme.

A theory of multiparty sessions is presented in this paper, where the semantic analysis of global types ensures that the projection onto each participant preserves a strong fairness assumption, i.e., no participant of multiparty sessions will wait for messages that are never sent or will send messages that no other participant will receive. In addition, once a global type is agreed upon by all participants involved, a local protocol from each local viewpoint will be generated as a projection onto each participant using a top-down approach. Furthermore, the introduction of asynchronous communication sub typing allows permutation of actions to raise efficiency, where each local type can be locally refined using our action asynchronous sub typing rules for both dependent and independent messages, which gives a more optimized protocol.

Dynamic services selection is an important and challenging work, especially, when a set of services have similar functionality and are available to the requesters' demands. Therefore, it is necessary to distinguish among these reliable services by computing the QoS value. Crucially, an optimal service can be selected depending on a combined evaluation of qualities of service (QoS). At the same time, these web services that meet the users' requirements should support rapid and dynamic composition of services. In this paper, we presented a QoS-based computation model for web service selection through improving the previous normalization algorithm by which the same QoS value may be gained for different services. In addition, it should have minimal time overhead and more precise service location.

The Bayesian fault detection based on WAMS/PMU measurement system will be clarified in this paper. Multivariate statistical analysis is an effective tool to finish the fault detection for electric power system. In Bayesian discriminant analysis as a sub branch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. A great number of simulation examples have confirmed that the results of Bayesian fault detection in wide area backup protection are accurate and reliable.

In order to obtain complete and correct information for road line extraction from remote sensing image, the complementary of two kinds of line feature is discussed and a feature fusion method is proposed. The method is implemented by cross validating line features of the LSD and the SRM according their spatial relationship. The results of experiments demonstrated the complementary of these two kinds features, and the performance of road extraction with the fused features can be improved obviously than with any single kind of line feature.

Energy is the power and foundation of social and economic development. The largest artificial real physical system is probably the modern power systems. And the operation of power system is near to limiting condition of its capacity. Under these circumstances, a minor fault may cause a series of extensive power failures. In this paper, posterior probability fault localization has been carried out by fully considering the intermittency and fluctuation of the access of new energies to power system. The results have confirmed that the posterior probability fault localization is accurate and reliable.

In this paper, the problem of controller design for a general nonlinear stochastic system is investigated. The randomness of the considered stochastic system comes from external excitations and system parameters. To achieve minimum entropy control, the probability density function (PDF) of the tracking error is given by using recursive method firstly. Then, a new performance index is formulated, which contain the entropy of the tracking error, the mathematical expectation of the squared error and the control energy. A recursive optimization control algorithm is obtained by minimizing the performance index. Stability analysis is established and encouraging simulation results are included to illustrate the efficiency of the proposed method.

After taking the element of adjacent matrix as the function of the state of edges in the network structure, the representation of the element in corresponding adjoint matrix has been given an in-depth analysis. With the use of simplifying method on each element in adjoint matrix, the complete connected matrix could be formed equally with the each element as the set of connected routes. Based on the above analysis and processing, the new connected routes finding algorithm is established, which is a systematic method and suitable for all kinds of network connections. Finally, the successful application to substation configuration has validated the efficiency of the novel algorithm in power system topology analysis, in which the connected routes can be formed in off-line way and be used for topology analysis in on-line way. Therefore, the aim to reduce topology analysis time could be achieved.

In this paper, a novel fault location method based on Principal Component Analysis (PCA) is proposed, in which synchronized information data are utilized provided by the WAMS/PMU measurement system. PCA method is an efficient mathematic tool to reduce dimensions. Therefore, the state matrix, consisting of raw synchrophasor information versus multi-points and various times, is compressed effectively. By the remained key features, the corresponding principal component scores could be obtained. The simulation results have shown that, according to these scores, the fault component can be confirmed exactly. A great number of simulation trials have fully proved that the results of principal component analysis are accurate and reliable.

Boiler steam temperature in thermal power plant is very complex and hard to control, many control strategies have been applied to it. Linear Active Disturbance Rejection Controller (LADRC) is adopted to the control of boiler steam temperature in this paper. The nonlinear model of boiler steam temperature is set up firstly, then the principle of the LADRC is briefly introduced and the LADRC based boiler steam temperature closed loop control system is designed and the closed loop system is studied with simulation. Compared with the traditional PID, more robust and better disturbance rejection properties are obtained.

The topology characteristics of the nodal matrix in power system analysis and the adjacent matrix in graph respectively has been proved to be absolutely identical. According to this feature and modification principles of the nodal impedance matrix, a new tracking algorithm for the state change of the circuit breaker has been built based on the general multiplication and general addition operations. The new algorithm is able to reduce the computation burden than the traditional way. Finally, the text example has validated the efficiency of the novel tracking algorithm in substation configuration.

In order to overcome some disadvantages of the conventional adjacent matrix method, the relationship between the elements of the adjacent matrix and the connected routes among nodes is analyzed as the fundamental introduction. Subsequently, the graphic characteristic of the main electrical connection in power plant or substation is extracted as three types: single branch, two branches in-parallel and multi-branches in-parallel, and an unified principle for the formulation of connected routes among arbitrary two nodes in wiring connection is also proposed as the cornerstone for the novel method. Thirdly, a novel method of substation configuration is established based on the set of connected routes among arbitrary two nodes, and by mean of taking the route state as variables, the value of connected routes set is used to directly determine the element in corresponding place of the adjacent matrix. Finally, the example results show that the new method is able to finish the original topology analysis and track the state status of switches more accurately and rapidly.

The faults in electric power system can not be completely avoided. When power system operates from normal state to failure or abnormal operates, its electric quantities may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of PMU, basing on principal components analysis technology, we have completed the fault location of electric power system. Of course, because of the complexity of different types of faults in power system, there still exists enormous problems need a close and intensive study.

With the widely installation of Phasor Measurement Units (PMUs) in power system and the increasing requirement of the real-time monitoring and control, a novel power system topology analysis algorithm is proposed with high precise information from PMUs to improve the performance of current network topology analysis. First of all, a method of identifying switch status is established to correct the state mistake of circuit breakers (CB) relying on the analog data acquired by PMUs. Subsequently, a topology analysis in substation combined with the PMUs information (including the digital data of CBs state and the phasor of voltage and current) is advanced in this paper. And then an efficient topology analysis algorithm introduces into topology search process using Cotree-to-Bus structure array. Finally, simulation results based on the PSCAD and MATLAB software have validated the efficiency and accuracy of the novel method introduced in this paper.

It is difficult to deal with the variable speed constant frequency (VSCF) wind turbine systems due to the stochastic characteristics and the pneumatic effects of wind. In this paper, a new pitch controller based on generalized predictive control theory is designed to improve the power-output quality of variable speed constant frequency wind turbines. An application to a 300MW wind turbines is given, and simulation results show that the proposed method is effective in wind speed interference suppression and constant out power.

Based on the analysis of the relationship between the actual weight and the pseudo-weight of the rational square-root B-spline based PDF model, this paper presents a new method to the modeling of the rational square-root B-spline based PDF model and the controller design is discussed based on the model. Both the modeling and controller design algorithms are applied to the simulation study of the molecular weight distribution (MWD) process, the results verify the validity of the algorithms.

Substation configuration is a vital part of the power system network topology, and its implementation has taken most of the total time of network topology analysis. The improvement of the network topology efficiency focuses on the faster and more accurate substation configuration based on the distributed modeling formation, which can be finished in parallel way in order to reduce the time consumed. Based on the analysis of the common characteristic of adjacent matrix and node admittance matrix and impedance matrix, a new method of original and tracking topology analysis in the substation has been proposed in this paper, which uses the addition of the virtual switch impedances and the virtual ground branches. The directional treatment to the original adjacent matrix is avoided in this new method. According to the characteristic of node impedance matrix, the work for substation configuration has been realized. Finally, the examples have validated the function of the new method in analyzing the original topology and tracking the variation in switch status. Compared to other methods based on adjacent matrix, this method features the easy-to-program property.

In this paper a crossed application and physical layer retransmission scheme is proposed for video streaming services in hybrid wireline and wireless networks. Each video packet carries the importance levels of succeeding packets at the application layer. In wireless transmission, by mapping the packet importance levels to different number of physical layer HARQ retransmissions, data packets are differentially protected which can greatly alleviate higher layer retransmissions and at the same time reduce the transmission delay for realtime services.

A predictive control algorithm for probability density function (PDF) is presented based on the rational square-root B-spline based PDF model. The rational square-root PDF model is studied first, based on the analysis of the relationship between the pseudo weight and the actual weight, the rational square-root B-spline model can be easily set up; then the predictive model is deduced; a performance index is chosen next to design the predictive controller; and simulation study is performed for the molecular weight distribution (MWD) control of a styrene polymerization process to test the validity of the predictive control algorithm.

In this paper, we adopt a novel applied approach to fault detection based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve fast and exact discrimination of faulty components and faulty sections, and finally accomplish fault detection in electric power systems. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of fault detection.

In the control of electric power systems, especially in the wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be detected quickly and defined exactly. In our researches, global information will be introduced into the backup protection system, basing on discriminant analysis theory of multivariate statistical analysis theory, we are going to seek after for the statistical laws of electrical quantities' marked changes by analyzing and computing real-time PMU measurements, so that we can carry out fast and exact identification of faulty components and faulty sections, and finally accomplish fault isolation. Multivariate statistical analysis theory is an efficient theory that can resolve different kinds of complex problems. It has been applied successfully to many researches of various fields. Discriminant analysis is an important theory in multivariate statistical analysis theory. A common approach to discriminant analysis is to apply the decision theory framework. In the study of electric power systems, discriminant analysis theory must also have a good prospect of application.

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our researches, global information has been introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis technology to seek after for the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact identification of faulty components and faulty sections, and finally accomplish fault isolation. Multivariate statistical analysis theory is an efficient theory that can resolve different kinds of complex problems. It has been applied successfully in many researches of various fields, such as geology, weather, hydrology, iatrology, industry, agriculture, and economy, etc. In the study of electric power systems, multivariate statistical analysis theory must also have a good prospect of application.

In this paper, we adopt a novel topological approach to fault diagnosis. In our researches, global information will be introduced into electric power system, we are using mainly BFS of graph theory algorithms to resolve fast and exact discrimination of faulty components and faulty sections, and finally accomplish fault diagnosis. Graph theory algorithms can be used to model many different physical and abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. In the study of fault diagnosis in electric power system, especially the fault diagnosis for mechanically-driven sections of equipments in power plant, graph theory algorithms must also have a good prospect of application.

A novel scheme for boiler combustion control system is proposed, where the firing rate demand is represented by furnace temperature distribution (FTD). In this scheme, the firing rate is defined to be FTD which characterizes the status of combustion directly. A real-time FTD shape control system is formulated and applied to boiler combustion control. FTD is fed back to combustion controller and adjusted by fuel valve actuators. Compared with the gradient approach, particle swarm optimization (PSO) is used to solve the control input, the simulation results verify the effectiveness of the proposed scheme.

Path visual coverage is the region which could be seen from the path in the terrain. This paper advances the concept of "average horizon " which represents the wide extent of the visual field of each path point to analyze and model the max and min path visual coverage problems, and resolves them utilizing simulated annealing algorithm based on the operation of viewshed amalgamation with pre-computed and stored viewshed information.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

This paper presents a closed-loop combustion control algorithm for a coal-fired boiler using the recently developed output distribution control theory. The furnace temperature distribution (FTD) is regarded as the controlled variable, which demonstrates the stability, economy and reliability of combustion process. Following the FTD obtained by Fluent simulation software, the equivalent B-spline FTD model is then established. Consequently, the control strategy is provided so as to ensure the output FTD will follow the desired distribution. The simulation study shows the effectiveness of the proposed approach.

This paper presents a novel networked control scheme for robotic manipulators by combing T-S fuzzy control with descriptor representation. The dynamic characteristic of nonlinear control system for robotic manipulator is built by T-S fuzzy model. In specific, both time-delay induced by network and packet loss are considered. A delay-dependent criterion for determining the stability of networked control system is obtained with the aid of linear matrix inequality (LMI) approach. A fuzzy controller is then designed by parallel distributed compensation principle. An augmented system is also introduced such that the tracking error is minimized. Finally, the proposed control algorithm is applied in a networked single-link robotic manipulator control system, and the result of simulation shows its effectiveness.

In this paper, the dynamics of a superheated steam temperature process in a 600MW supercritical once-through boiler is first analyzed. The dynamics is influenced by the operating conditions represented by steam flow and steam pressure mainly. An intelligent cascade control system is then designed for controlling superheated steam temperature over wide range conditions. The parameter of the proportional controller in the inner loop is fixed all the time. The controller in the main loop is a self-tuning PID neuro-controller whose parameters are adjusted by gradient algorithm, where the Jacobian information of the plant is obtained by RBF neural networks. Finally, the designed control system is applied to control the superheated steam temperature in a 600MW supercritical once-through boiler, and the simulation indicates it has satisfactory performances over wide range

Control of an inverted pendulum has been a benchmark example in demonstrating and motivating various controller design techniques. This paper makes a study of application of μ synthesis with regional pole assignment for stabilizing a double inverted pendulum system with some parametric uncertainties and designs state-feedback controller by solving a multi-objective controller synthesis problem. The researching results of this paper are applied to the inverted pendulum experiment equipment produced by Googol Technology LTD. Simulation results show that the controller has favorable performance in robustness and disturbance rejection.

Two predictive PDF control algorithms and one new algorithm of iterative learning PDF control have been compared with the standard PDF control of dynamic systems. The two predictive algorithms are developed on the general 2-norm distance and the Kullback-Leibler performance, respectively. A new iterative algorithm is proposed in which the control input in the current batch is updated based on the control input and the PDF tracking errors in the previous batch. Simulation studies demonstrate the improvement in PDF tracking performance when advanced control strategies are introduced to the standard algorithm.