http://flash.lakeheadu.ca/~kliu/automatica_2009_2.pdf

Brief paper: Direct adaptive fuzzy control of nonlinear strict-feedback systems

Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発

A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining ?dynamic surface control? and ?minimal learning parameter? techniques. The key features of the algorithm are that, first, the problem of ?explosion of complexity? inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.

DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems

In this paper, a robust adaptive tracking control problem is discussed for a general class of strict-feedback uncertain nonlinear systems. The systems may possess a wide class of uncertainties referred to as unstructured uncertainties, which are not linearly parameterized and do not have any prior knowledge of the bounding functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate the uncertainties. A unified and systematic procedure is employed to derive two kinds of novel robust adaptive tracking controllers by use of the input-to-state stability (ISS) and by combining the backstepping technique and generalized small gain approach. One is the robust adaptive fuzzy tracking controller (RAFTC) for the system without input gain uncertainty. The other is the robust adaptive fuzzy sliding tracking controller (RAFSTC) for the system with input gain uncertainty. Both algorithms have two advantages, those are, semi-global uniform ultimate boundedness of adaptive control system in the presence of unstructured uncertainties and the adaptive mechanism with minimal learning parameterizations. Four application examples, including a pendulum system with motor, a one-link robot, a ship roll stabilization with actuator and a single-link manipulator with flexible joint, are used to demonstrate the effectiveness and performance of proposed schemes.

A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems

Robotics and autonomous systems

A novel model reference adaptive robust fuzzy control algorithm is presented for ship steering autopilot, which is an uncertain nonlinear system In the proposed algorithm, fuzzy logic systems have been used to approximate lumped unknown function in the ship steering systems and the adaptive mechanism with minimal learning parameter, ie only one parameter, has been achieved by use of Lyapunov approach The proposed methodology is verified using the simulation mode of the Dalian Maritime University's ocean-going training ship named Yulong It is shown that the proposed algorithm guarantees that the ship steering autopilot system is asymptotically stable and its tracking error can approach to zero

Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems

Dynamic balance of a biped robot using fuzzy reinforcement learning agents

Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

Thisresearch reportbrings together presenttrends in advanced welding robots, robotic welding, artificial intelligent and automatic welding. It includes important technical subjects on welding robots such as intelligent technologies and systems, and design and analysis. Modeling, identification and control of the welding process are presented, as well as knowledge-based systems for welding and tele-robotic welding. Other topics covered are sensing and data fusion, computer vision and virtual-reality applications of the welding process. An overview of intelligent and flexible manufacturing systems is given in addition to artificial intelligent technologies for industrial processes.

Changjiu Zhou

Papers

Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems

Dynamic balance of a biped robot using fuzzy reinforcement learning agents

Robust adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems via small-gain approach

Robotic Welding, Intelligence and Automation

Robot learning with GA-based fuzzy reinforcement learning agents