Iterative Learning Control With Data-Driven-Based Compensation.

The robust iterative learning control (RILC) can deal with the systems with unknown time-varying uncertainty to track a repeated reference signal. However, the existing robust designs consider all the possibilities of uncertainty, which makes the design conservative and causes the controlled process converging to the reference trajectory slowly. To eliminate this weakness, a data-driven method is proposed.

Inferring Centrality from Network Snapshots.

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network.

Constrained motion model of mobile robots and its applications.

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation.

Incremental learning with balanced update on receptive fields for multi-sensor data fusion.

This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm.

Nonlinear visual mapping model for 3-D visual tracking with uncalibrated eye-in-hand robotic system.

A new control scheme for uncalibrated robotic visual tracking problem is proposed that compromises the computational expenses of overall system with offline modeling and online control. A nonlinear visual mapping model for the uncalibrated hand-eye coordination is first proposed with an artificial neural network implementation. An online visual tracking controller is then developed together with a real-time motion planner.

Design PID controllers for desired time-domain or frequency-domain response.

Practical requirements on the design of control systems, especially process control systems, are usually specified in terms of time-domain response, such as overshoot and rise time, or frequency-domain response, such as resonance peak and stability margin. Although numerous methods have been developed for the design of the proportional-integral-derivative (PID) controller, little work has been done in relation to the quantitative time-domain and frequency-domain responses. In this paper, we study the following problem: Given a nominal stable process with time delay, we design a suboptimal PID controller to achieve the required time-domain response or frequency-domain response for the nominal system or the uncertain system.

A new two-degree-of-freedom level control scheme.

In this paper, a two-degree-of-freedom level control scheme for delay free processes is analyzed. The nominal performance and robustness are examined. And sufficient and necessary conditions for robust stability are derived.