Dongya Zhao

A new terminal sliding mode control for robotic manipulators

In this study, a new terminal sliding mode control approach is developed for robotic manipulators based on finite-time stability theory and differential inequality principle. The corresponding stability analysis is presented to lay a foundation for theoretical understanding to the underlying design issue as well as safe operation for real system. An illustrative example of a two-link rigid robotic manipulator is presented to validate effectiveness of the proposed approach.

A finite-time approach to formation control of multiple mobile robots with terminal sliding mode

In this study, a new finite-time synchronised approach is developed for multiple mobile robots formation control based on terminal sliding mode control principle and system synchronisation theory. Associated stability analysis is presented to lay a foundation for analytical understanding in generic theoretical aspects and safe operation for real systems. An illustrative example of multiple mobile robots formation control is bench tested to validate the effectiveness of the proposed approach.

A framework of neural networks based consensus control for multiple robotic manipulators

Abstract A framework for neural networks (NN) based consensus control is proposed for multiple robotic manipulators systems (MRMS) under leader–follower communication topology. Two situations, that is, fixed and switching communication topologies, are studied by using adaptive and robust control principles, respectively. Radial basis function (RBF) NN enhances estimator and observer are developed to estimate system uncertainty and obtain the leader manipulator׳s control torque online. By using the Lyapunov stability theory, an adaptive consensus control algorithm is designed to tune the weight of the RBF NN online, which can stabilize the consensus error to a small residual set. On this basis, a novel robust control algorithm is presented to eliminate the estimating errors caused by RBF NN, which can achieve asymptotical stability. The stability of the proposed approaches is analyzed by using Lyapunov methods. Finally numerical bench tests are conducted to validate the effectiveness of the proposed approach.

Synchronized control with neuro-agents for leader-follower based multiple robotic manipulators

In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on the leader-follower network communication topology. The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro-agents, is to facilitate the whole control system design and analysis. Otherwise such design is impossible with classical analytical procedure. The first agent is the neuro-compensator to accommodate uncertainty associated with the follower manipulators, and the second agent is the neuro-estimator to obtain acceleration of the leader manipulator. Correspondingly the stability analysis of the designed control system is formulated with Lyapunov method. Finally numerical bench tests under various critical conditions are conducted to validate the effectiveness of the proposed approach.

Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics

In this study, a new adaptive synchronised tracking control approach is developed for the operation of multiple robotic manipulators in the presence of uncertain kinematics and dynamics. In terms of the system synchronisation and adaptive control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other robotic manipulators. On the other hand, the developed approach can cope with kinematic and dynamic uncertainties. The corresponding stability analysis is presented to lay a foundation for theoretical understanding of the underlying issues as well as an assurance for safely operating real systems. Illustrative examples are bench tested to validate the effectiveness of the proposed approach. In addition, to face the challenging issues, this study provides an exemplary showcase with effectively to integrate several cross boundary theoretical results to formulate an interdisciplinary solution.

Position synchronised control of multiple robotic manipulators based on integral sliding mode

In this study, a new position synchronised control algorithm is developed for multiple robotic manipulator systems. In the merit of system synchronisation and integral sliding mode control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other manipulators. With the integral sliding mode, the proposed approach has insensitiveness against the lumped system uncertainty within the entire process of operation. Further, a perturbation estimator is proposed to reduce chattering effect. The corresponding stability analysis is presented to lay a foundation for theoretical understanding to the underlying issues as well as safely operating real systems. An illustrative example is bench tested to validate the effectiveness of the proposed approach.

Review of rational total nonlinear dynamic system modelling, identification, and control

This paper is a summary of the research development in the rational (total) nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input (controller outputs) are subject to nonlinear to the output. Previously, this class of models has been known as rational models, which is a model that can be considered to belong to the nonlinear autoregressive moving average with exogenous input (NARMAX) model subset and is an extension of the well-known polynomial NARMAX model. The justification for using the rational model is that it provides a very concise and parsimonious representation for highly complex nonlinear dynamic systems and has excellent interpolatory and extrapolatory properties. However, model identification and controller design are much more challenging compared to the polynomial models. This has been a new and fascinating research trend in the area of mathematical modelling, control, and applications, but still within a limited research community. This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion.

Finite time position synchronised control for parallel manipulators using fast terminal sliding mode

A new finite time position synchronised control approach for parallel manipulators is proposed using a fast terminal sliding mode (TSM). By developing a novel synchronisation and coupling position error, a non-singular fast TSM is proposed in coupling position error space. The proposed controller can guarantee position error and synchronisation error converge to zero in a finite time simultaneously without requiring the explicit using system dynamic model. The corresponding stability analysis is presented to lay a foundation for theoretical understanding to the underlying issues as well as safe operation for real systems. An illustrative example is demonstrated in support of the effectiveness of the proposed approach.

A general U-block model-based design procedure for nonlinear polynomial control systems

ABSTRACT The proposition of U-model concept (in terms of ‘providing concise and applicable solutions for complex problems’) and a corresponding basic U-control design algorithm was originated in the first authors PhD thesis. The term of U-model appeared (not rigorously defined) for the first time in the first authors other journal paper, which established a framework for using linear polynomial control system design approaches to design nonlinear polynomial control systems (in brief, linear polynomial approaches → nonlinear polynomial plants). This paper represents the next milestone work – using linear state-space approaches to design nonlinear polynomial control systems (in brief, linear state-space approaches → nonlinear polynomial plants). The overall aim of the study is to establish a framework, defined as the U-block model, which provides a generic prototype for using linear state-space-based approaches to design the control systems with smooth nonlinear plants/processes described by polynomial models. For analysing the feasibility and effectiveness, sliding mode control design approach is selected as an exemplary case study. Numerical simulation studies provide a user-friendly step-by-step procedure for the readers/users with interest in their ad hoc applications. In formality, this is the first paper to present the U-model-oriented control system design in a formal way and to study the associated properties and theorems. The previous publications, in the main, have been algorithm-based studies and simulation demonstrations. In some sense, this paper can be treated as a landmark for the U-model-based research from intuitive/heuristic stage to rigour/formal/comprehensive studies.

An enhanced linear Kalman filter EnLKF algorithm for parameter estimation of nonlinear rational models

ABSTRACT In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.