Truong Quang Dinh

An Integrated Intelligent Nonlinear Control Method for a Pneumatic Artificial Muscle

This paper proposes an advanced position-tracking control approach, referred to as an integrated intelligent nonlinear controller, for a pneumatic artificial muscle (PAM) system. Due to the existence of uncertain, unknown, and nonlinear terms in the system dynamics, it is difficult to derive an exact mathematical model with robust control performance. To overcome this problem, the main contributions of this paper are as follows. To actively represent the behavior of the PAM system using a grey-box model, neural networks are employed as equivalent internal dynamics of the system model and optimized online by a Lyapunov-based method. To realize the control objective by effectively compensating for the estimation error, an advanced robust controller is developed from the integration of the designed networks, and improvement of the sliding mode and backstepping techniques. The convergences of both the developed model and the closed-loop control system are guaranteed by Lyapunov functions. As a result, the overall control approach is capable of ensuring the systems performance with fast response, high accuracy, and robustness. Real-time experiments are carried out in a PAM system under different conditions to validate the effectiveness of the proposed method.

A Novel Robust Predictive Control System Over Imperfect Networks

This paper aims to study on feedback control for a networked system with both uncertain delays and, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1) delays and packet dropouts are accurately detected online by a network problem detector; 2) a so-called proportional-integral-based neural network grey model (PINNGM) is developed in a general form to be capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3) using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4) based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability, and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach.

Powertrain modelling for engine stop-start dynamics and control of micro/mild hybrid construction machines

Engine stop–start control is considered as the key technology for micro/mild hybridisation of vehicles and machines. To utilise this concept, especially for construction machines, the engine is desired to be started in such a way that the operator discomfort can be minimised. To address this issue, this paper aims to develop a simple powertrain modelling approach for engine stop–start dynamic analysis and an advanced engine start control scheme newly applicable for micro/mild hybrid construction machines. First, a powertrain model of a generic construction machine is mathematically developed in a general form, which allows to investigate the transient responses of the system during the engine cranking process. Second, a simple parameterisation procedure with a minimum set of data required to characterise the dynamic model is presented. Third, a model-based adaptive controller is designed for the starter to crank the engine quickly and smoothly without the need of fuel injection while the critical problems of machine noise, vibration and harshness can be eliminated. Finally, the advantages and effectiveness of the proposed modelling and control approaches have been validated through numerical simulations. The results imply that with the limited data set for training, the developed model works better than a high fidelity model built in AMESim while the adaptive controller can guarantee the desired cranking performance.

A Real-Time Bilateral Teleoperation Control System over Imperfect Network

Functionality and performance of modern machines are directly affected by the implementation of real-time control systems. Especially in networked teleoperation applications, force feedback control and networked control are two of the most important factors and determine the performance of the whole system. In force feedback control, generally it is necessary but difficult and expensive to attach sensors (force/torque/pressure sensors) to detect the environment information in order to drive properly the feedback force. In networked control, there always exist inevitable random time-varying delays and packet losses, which may degrade the system performance and, even worse, cause the system instability. Therefore in this chapter, a study on a real-time bilateral teleoperation control system (BTCS) over an imperfect network is discussed. First, current technologies for teleoperation as well as bilateral teleoperation control systems are briefly reviewed. Second, an advanced concept for designing a bilateral teleoperation networked control (BTNCS) system is proposed and the working principle is clearly explained. Third, an approach to develop a force-sensorless feedback control (FSFC) is proposed to simplify the sensor requirement in designing the BTNCS while the correct sense of interaction between the slave and environment can be ensured. Forth, a robust adaptive networked control (RANC) -based master controller is introduced to deal with control of the slave over the network containing both time delays and information loss. Case studies are carried out to evaluate the applicability of the suggested methodology.

A data-based hybrid driven control for networked-based remote control applications

This paper develops a data-based hybrid driven control (DHDC) approach for a class of networked nonlinear systems compromising delays, packet dropouts and disturbances. First, the delays and/or packet dropouts are detected and updated online using a network problem detector. Second, a single-variable first-order proportional-integral (PI)-based adaptive grey model is designed to predict in a near future the network problems. Third, a hybrid driven scheme integrated a small adaptive buffer is used to allow the system to operate without any interrupt due to the large delays or packet dropouts. Forth, a prediction-based model-free adaptive controller is developed to compensate for the network problems. Effectiveness of the proposed approach is demonstrated through a case study.