Robotic Unicycle Intelligent Robust Control Pt I: Soft Computational Intelligence Toolkit

The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle discussed. A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems applied. The results of stochastic simulation of a fuzzy intelligent control system for various types of external / internal excitations for a dynamic, globally unstable control object - extension cableless robotic unicycle based on Soft Computing (Computational Intelligence Toolkit - SCOptKBTM) technology presented. A new approach to design an intelligent control system based on the principle of the minimum entropy production (minimum of useful resource losses) determination in the movement of the control object and the control system is developed. This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle. An algorithm for entropy production computing and representation of their relationship with the Lyapunov function (a measure of stochastic robust stability) described

Literature Review of PID Controller based on Various Soft Computing Techniques

This paper profound the various soft computing techniques like fuzzy logic, genetic algorithm, ant colony optimization, particle swarm optimization used in controlling the parameters of PID Controller. Its widespread use and universal acceptability is allocated to its elementary operating algorithm, the relative ease with the controller effects can be adjusted, the broad range of applications where it has truly developed excellent control performances, and the familiarity with which it is deduced among researchers. In spite of its wide spread use, one of its short-comings is that there is no efficient tuning method for PID controller. Given this background, the main objective of this is to develop a tuning methodology that would be universally applicable to a range of well-liked process that occurs in the process control industry

GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks

Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection

Building Temperature Control with Intelligent Methods

Temperature control is important for both human comfort and the need in industry. In the thesis, two good intelligent control methods are compared to find their advantages and disadvantages. Matlab is used as the tool to make models and process calculations. The building model is one simple room in Akwesasne in New York State and the target is to keep the temperature indoor around 22 degree Centigrade from 12/29/2013 to 12/31/2013. Heat pump is used to provide or absorb heat. All data in the experiments is from JRibal Environmental eXchange network and PJM. The first method is fuzzy neural network (FNN). With the control from fuzzy logic and the learning process in neural network, the temperature is kept around 22 degree Centigrade. Another method is model predictive control (MPC) with genetic algorithm (GA). And the temperature is also controlled around 22 degree Centigrade by predicting the temperature and solar radiation. In addition, the cost is saved by using genetic algorithm with an energy storage system added in the building model. In summary, FNN is easy to build but the result is not very accurate; while the result of MPC is more accurate but the model is hard to develop. And GA is a good optimization method

Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit

The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system

Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and non-membership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi-Sugeno-Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decou- pled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made between the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK) and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems