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 system's 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

Force reflecting joystick control for applications to bilateral teleoperation in construction machinery

This paper presents a simple and effective force reflecting joystick controller for applications to bilateral teleoperation in construction machinery. First, this controller is a combination of an advanced force reflecting gain tuner and two local adaptive controllers, master and slave. Second, the force reflecting gain tuner is effectively designed using recursive least square method and fuzzy logics to estimate directly and accurately the environmental characteristics and, consequently, to produce properly a force reflection. Third, the local adaptive controllers are simply designed using fuzzy technique and optimized using a smart leaning mechanism to ensure that the slave follows well any given trajectory while the operator is able to achieve truly physical perception of interactions at the remote site. An experimental master-slave manipulator is setup and real-time control tests are carried out under various environmental conditions to evaluate the effectiveness of the proposed controller

A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter

This paper introduces a novel control approach to maximizing the output energy of an adjustable slope angle wave energy converter (ASAWEC) with oil-hydraulic power take-off. Different from typical floating-buoy WECs, the ASAWEC is capable of capturing wave energy from both heave and surge modes of wave motions. For different waves, online determination of the titling angle plays a significant role in optimizing the overall efficiency of the ASAWEC. To enhance this task, the proposed method was developed based on a learning vector quantitative neural network (LVQNN) algorithm. First, the LVQNN-based supervisor controller detects wave conditions and directly produces the optimal titling angles. Second, a so-called efficiency optimization mechanism (EOM) with a secondary controller was designed to regulate automatically the ASAWEC slope angle to the desired value sent from the supervisor controller. A prototype of the ASAWEC was fabricated and a series of simulations and experiments was performed to train the supervisor controller and validate the effectiveness of the proposed control approach with regular waves. The results indicated that the system could reach the optimal angle within 2s and subsequently, the output energy could be maximized. Compared to the performance of a system with a vertically fixed slope angle, an increase of 5% in the overall efficiency was achieved. In addition, simulations of the controlled system were performed with irregular waves to confirm the applicability of the proposed approach in practice

Proposition and experiment of a sliding angle self-tuning wave energy converter

The hydraulic power-take-off mechanism (HPTO) is one of the most popular methods in wave energy converters (WECs). However, the conventional HPTO with a fixed direction motion has some drawbacks which limit its power capture capability. This paper proposes a sliding angle self-tuning wave energy converter (SASTWEC) to find the optimal sliding angle automatically, with the purpose of increasing the power capture capability and energy efficiency. Furthermore, a small scale WEC test rig was fabricated and a wave making source has been employed to verify the sliding angle performance and efficiency of the proposed system throughout experiments