Tong Heng Lee

Adaptive-predictive control of a class of SISO nonlinear systems

In this paper, an adaptive-predictive control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalized predictive control (GPC) approach. The design is model-free, based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using a recursive least squares type of identification algorithm. The proposed control is especially useful for nonlinear systems with vaguely known dynamics. Robust stability of the closed-loop system is analyzed and proven in the paper. Simulation and real-time application examples are provided for real nonlinear systems which are known to be difficult to model and control.

Application of evolutionary artificial potential field in robot soccer system

Evolutionary artificial potential field (EAPF) functions are utilized for mobile robot navigation in a microrobot soccer (MiroSot) environment. In a micro-robot soccer system the robots are monitored using an overhead CCD Camera, making it suitable for real time application of the EAPF functions. The effectiveness of the EAPF functions in real time mobile robot navigation are verified through experimentation. The EAPF functions proposed were tested in different scenarios related to ball tracking and ball kicking, while facing competition from other robots.

Predictive PI versus Smith control for dead-time compensation

Abstract In this paper, the design and analysis of a predictive PI controller, capable of dead-time compensation, and which overcome certain deficiencies of Smith control, is presented. The design is based on a first-order plus dead-time model of the actual process, which is representative of the majority of process dynamics encountered in the industry. Only simple classical closed-loop specifications are required from the user. Simulation examples, including one based on an analog process simulator, are provided to highlight the principles of the proposed scheme and to compare its performance with Smith control.

Adaptive-predictive PI control of a class of SISO systems

An adaptive PI control algorithm is developed for a class of SISO nonlinear discrete-time systems based on a generalised predictive control (GPC) approach. The design is model-free, based directly on pseudo-partial-derivatives derived online from the input and output information of the system using a standard recursive least squares identification algorithm. Simulation and real-time experiment are provided for real nonlinear systems which are known to be difficult to model and control.

Evolvable Hardware in Evolutionary Robotics

In recent decades the research on Evolutionary Robotics (ER) has developed rapidly. This direction is primarily concerned with the use of evolutionary computing techniques in the design of intelligent and adaptive controllers for robots. Meanwhile, much attention has been paid to a new set of integrated circuits named Evolvable Hardware (EHW), which is capable of reconfiguring its architectures unlimited time based on artificial evolution techniques. This paper surveys the application of evolvable hardware in evolutionary robotics. The evolvable hardware is an emerging research field concerning the development of evolvable robot controllers at the hardware level to adapt to dynamic changes in environments. The context of evolvable hardware and evolutionary robotics is reviewed, and a few representative experiments in the field of robotic hardware evolution are presented. As an alternative to conventional robotic controller designs, the potentialities and limitations of the EHW-based robotic system are discussed and summarized.

Automatic Tuning of Two-Degree-of-Freedom Control for D.C. Servo Motor Systems

Abstract In this paper, we present the first application of the relay feedback approach to the automatic tuning of a two-degree-of-freedom (2-DOF) controller to achieve robust motion control for d.c. servo motor systems. The only specifications required from the user are simple and classical parameters for the desired closed-loop transfer function and an appropriate sensitivity function determining the robustness of the closed-loop system. Heuristics are provided for the selection of key parameters of these functions. Two variations of the conventional relay feedback methods were proposed and investigated. Both of these variations introduce an appropriate phase angle in the negative inverse describing function of the relay to excite a sustained limit cycle oscillation. Simulation and experimental results from a voltage controllable d.c. motor system illustrate the effectiveness of the proposed methods.

DNA coded GA for the rule base optimization of a fuzzy logic controller

A DNA coded genetic-algorithm (GA) is proposed to optimize the rule-base of a fuzzy logic controller (FLC). The controller is designed for a vehicle-active suspension system to improve the driving comfort. The DNA codes GA constructed optimal decision-making rules for the fuzzy logic controller. Simulation results demonstrate the effectiveness of the algorithm.

Comparison of Khepera robot navigation by evolutionary neural networks and pain-based algorithm

A comparison of mobile robot navigation using evolutionary neural networks and the pain based algorithm is discussed in this paper. The controllers are designed based on evolutionary neural networks and the pain-based algorithms. The performance of the controllers are verified with the Khepera robot.

SISO Nonlinear Systems

The development of feedback linearization techniques provides a powerful tool for nonlinear system control.