T-S Fuzzy H∞ Tracking Control of Input Delayed Robotic Manipulators

Time delays are often encountered by practical control systems while they are acquiring, processing, communicating, and sending signals. Time delays may affect the system stability and degrade the control system performance if they are not properly dealt with. Taking the classical robot control problem as an example, the significant effect of time delay on the closed-loop system stability has been highlighted in the bilateral teleoperation, where the communication delay transmitted through a network medium has been received widespread attention and different approaches have been proposed to address this problem (Hokayem and Spong, 2006). In addition, examples like processing delays in visual systems and communication delay between different computers on a single humanoid robot are also main sources that may cause time delays in a robotic control system (Chopra, 2009), and the issue of time delay for robotic systems has been studied through the passivity property. For systems with time delays, both delay dependent and delay independent control strategies have been extensively studied in recent years, see for example (Xu and Lam, 2008) and references therein. For the control of nonlinear time delay systems, model based Takagi- Sugeno (T-S) fuzzy control (Tanaka and Wang, 2001; Feng, 2006; Lin et al., 2007) is regarded as one of the most effective approach because some of linear control theory can be applied directly. Conditions for designing such kinds of controllers are generally expressed as linear matrix inequalities (LMIs) which can be efficiently solved by using most available software like Matlab LMI Toolbox, or bilinear matrix inequalities (BMIs) which could be transferred to LMIs by using algorithms like iteration algorithm or cone complementary linearisation algorithm. From the theoretical point of view, one of the current focus on the control of time delay systems is to develop less conservative approaches so that the controller can stabilise the systems or can achieve the defined control performance under bigger time delay

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation