Adaptive optimal digital PID controller

Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014

Fully connected multi-objective particle swarm optimizer based on neural network

Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance.Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011

Adaptive sharing scheme based sub-swarm multi-objective PSO

Abstract: To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods

Cask theory based parameter optimization for particle swarm optimization

Abstract: To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013

E-education in an open distance university

Abstract: With the development of internet, software and the relevant techniques, the e-education becomes more and more attractive. As one of most famous open distance universities, the University of South Africa (UNISA) has partly realized e-education. This paper describes and investigates the existing e-education system of UNISA. There are many advantages, such as realizing paperless office, lower cost, high efficiency, and so on, using this e-education system for teaching and learning. Moreover, the challenges and solving methods are also studied and discussed

Analysis of a fractional order nonlinear system based on the frequency domain approximation

Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon

Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance

Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition

Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding window-based method, successfully capturing the analytic and geometric properties of pen strokes with strong local invariance and robustness. A multi-spatial-context fully convolutional recurrent network (MCFCRN) is proposed to exploit the multiple spatial contexts from the signature feature maps and generate a prediction sequence while completely avoiding the difficult segmentation problem. Furthermore, an implicit language model is developed to make predictions based on semantic context within a predicting feature sequence, providing a new perspective for incorporating lexicon constraints and prior knowledge about a certain language in the recognition procedure. Experiments on two standard benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with correct rates of 97.10% and 97.15%, respectively, which are significantly better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure