Investigations into New Algorithms for Self-Organising Fuzzy Logic Control using Type-1 and Type-2 Fuzzy Sets

The number of applications of intelligent control systems has grown significantly over the last few decades, and today they are used in various challenging industrial application domains, where they provide particularly useful solutions. The term „intelligent controllers‟ describes a field where control approaches are represented by mechanisms similar to those used by the human brain. These characteristics include, for example, learning, modification, adaption, effective working with high levels of uncertainty and coping with large amounts of data. Intelligent control systems are particularly useful for complex systems such as biomedical and chemical plants, which are expected to work under optimal conditions. A good example of an intelligent controller is the so called Self-Organising Fuzzy Logic Control (SOFLC) proposed by Procyk and Mamdani in the late 1970s. The SOFLC scheme involves a control policy that allows its structure to be adapted based on the environment in which it operates. The SOFLC combines a conventional fuzzy logic controller with a supervisory layer which monitors and regulates the performance of the system. In this thesis, new architectures are proposed for single input single output (SISO) and multi-input multi- output (MIMO) structures to improve on the original SISO SOFLC design in terms of performance and robustness, as well as extend the analysis and design issues relating to such algorithms to the MIMO case using hybrid approaches. The work proposed in this thesis includes: 1. A new development of type-1 and type-2 Self-Organising Fuzzy Logic Control with a Dynamic Supervisory Layer (SOFLC-DSL) for the SISO case: In this part of the thesis, the work is mainly focused on designing a sophisticated SOFLC algorithm by combining a type-1 fuzzy system with a new Particle Swarm Optimisation (PSO) algorithm, so as to make the SOFLC scheme more flexible and effective in terms of responding to changes in the process to be controlled or the environment surrounding it. A new on-line PSO algorithm is developed by using the idea of credit assignment and fitness estimation to allow the optimisation of the consequent parts of the performance index (PI) table on-line. The proposed scheme is tested on a non-linear and uncertain Muscle Relaxation Model. Computer results demonstrate that the proposed algorithm achieve satisfactory performance, and is superior to the standard SOFLC scheme. In order to enhance the capabilities of the controller to deal with environments where the level of uncertainties and noise are high, both interval and zSlice type-2 fuzzy sets are deployed. Simulation results show that the performance of the SOFLC-DSL algorithm improves in terms of set-point tracking properties and the smoothness of the generated control signals. 2. A new extension of the SOFLC-DSL to the multivariable case: The proposed SOFLC-DSL algorithms are applied as the dominating controllers within multivariable control architectures. In order to deal with the effects of interactions between the input and output channels, both the relative array gain matrix as well as a linguistic switching mode compensator are considered. The proposed algorithms are tested on a drug dynamic process, and the results show they have good control abilities in terms of maintaining the desired set-points with smooth control effort, as well as in handling the interaction between different control channels