Study on a new and effective fuzzy PID ship autopilot

Ship autopilots are usually designed based on Proportional Differential (PD) and Proportional Integrate Differential (PID) controllers because of their simplicity, reliability, and ease of construction. However, their performance in various environmental conditions is not as good as desired. This disadvantage can be overcome by adjusting works or constructing adaptive controllers, although these methods are complex and not easy. This article presents a new method for constructing a ship autopilot based on the combination of fuzzy logic control (FLC) and linear control theory (PID control). The new ship autopilot has the advantages of both the PID and FLC control methodologies: ease of construction, and optimal control laws can be established based on ship master knowledge. Therefore, the new ship autopilot can be well adapted with parameter variations and strong environment effects. Simulation using MATLAB software for a ship with real parameters showed high effectiveness of the fuzzy PID autopilot in course-keeping and course-changing maneuvers in comparison with the ordinary PID ship autopilots.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Adaptive sliding mode control for MIMO nonlinear systems based on fuzzy logic scheme

In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.     

Mechanical proof testing

A computer program has been written in PL/I to check the correctness of proofs in an axiomatic theory. A notation for the statements of the theory is described. Application examples are provided.     

Validating the PSL/Sugar Semantics Using Automated Reasoning

The Accellera organisation selected Sugar, IBMs formal specification language, as the basis for a standard to drive assertion-based verification in the electronics industry. Sugar combines regular expressions, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) into a property language intended for both static verification (e.g. model checking) and dynamic verification (e.g. simulation). In 2003 Accellera decided to rename the evolving standard to Accellera Property Specification Language (or PSL for short). We motivate and describe a deep semantic embedding of PSL in the version of higher-order logic supported by the HOL 4 theorem-proving system. The main goal of this paper is to demonstrate that mechanised theorem proving can be a useful aid to the validation of the semantics of an industrial design language.     

Automatic Theorem Proving and Fuzzy Situational Search for Decisions

The use of automatic theorem proving is considered as a methodological basis of fuzzy situational search for decisions.     

Parameter Estimation in Stochastic Logic Programs

Stochastic logic programs (SLPs) are logic programs with parameterised clauses which define a log-linear distribution over refutations of goals. The log-linear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions.We analyse the fundamental statistical properties of SLPs addressing issues concerning infinite derivations, 'unnormalised SLPs and impure SLPs. After detailing existing approaches to parameter estimation for log-linear models and their application to SLPs, we present a new algorithm called failure-adjusted maximisation (FAM). FAM is an instance of the EM algorithm that applies specifically to normalised SLPs and provides a closed-form for computing parameter updates within an iterative maximisation approach. We empirically show that FAM works on some small examples and discuss methods for applying it to bigger problems.