Explore the vast range of titles available.

This study presents an investigation into fractional-order predefined-time terminal sliding mode control (FoPtSMC) for robotic manipulators, particularly focusing on addressing uncertainties and external disturbances. The study introduces a new predefined-time fractional-order SMC method to ensure guaranteed predefined-time convergence and superior tracking performance. This approach also aims to mitigate control input chattering, a common issue in such systems. The Lyapunov analysis is used, and the study establishes the predefined time stability of the proposed closed system. Furthermore, the effectiveness of the proposed FoPtSMC technique is validated through computer simulations applied to a robotic manipulator system.

This study investigates adaptive predefined-time proportional–derivative terminal sliding mode control (APtPDSMC) for robotic manipulators subject to uncertainties and external disturbances. We propose a novel predefined-time PDSMC (PtPDSMC) scheme utilizing proportional–derivative control to achieve guaranteed predefined-time convergence and superior tracking performance. Additionally, this approach reduces control input chattering, a common problem. Then, APtPDSMC is designed to not require previous knowledge of the boundaries of the uncertain system dynamics it estimates. By using the Lyapunov theorem, the predefined-time stability of the closed-loop system is established. Finally, the efficacy of the suggested APtPDSMC technique is confirmed by computer simulations applied to a robotic manipulator system.

This study presents a second-order sliding-mode control (2-SMC) scheme for a wind turbine-driven doubly fed induction generator (DFIG). The tasks of grid synchronisation and power control are undertaken by two different algorithms, designed to command the rotor-side converter at a fixed switching frequency. Effective tuning equations for the parameters of both controllers are derived. A procedure is also provided that guarantees bumpless transfer between the two controllers at the instant of connecting the DFIG to the grid. The resulting 2-SMC scheme is experimentally validated on a laboratory-scale 7 kW DFIG test bench. Experimental results evidence both the high dynamic performance and the superior robustness achieved with the proposed control scheme.

In this study, a fractional-order adaptive-sliding mode control (SMC) scheme is proposed to stabilise commensurate fractional-order polytopic non-linear differential inclusion systems containing sector and dead-zone nonlinearities in the control inputs and unknown bounded disturbances. The suggested control method is composed of fractional-order sliding surfaces, adaptive-SMC inputs and adaptation laws for unknown bounds of disturbances. The Lyapunov stability theorem is used to prove the stability of the closed-loop system. A practical system and two numerical examples are simulated to show the effectiveness and performance of the proposed control technique.

We dedicate to the investigation of asynchronous sliding mode control (SMC) for Takagi–Sugeno fuzzy Markov jump systems. By constructing a special threshold parameter, a novel dynamic event-triggered scheme (DETS) is proposed to save computation resources. Considering those data dropouts, time delays and environmental disturbances, we use hidden Markov model to represent the resultant asynchronization between the sliding mode controller and the controlled system. Then, by constructing the Lyapunov functional, we provide a sufficient condition for closed-loop system stochastic stability and deduce a SMC law with DETS. The system trajectories are forced into the proposed sliding manifold under the dynamic event-triggered SMC law. Finally, an illustrative example is provided, indicating that the presented theoretical results are available.

In this study, the authors address the problem of optimal routing and relative motion control in a network of robots. The path planning scheme has been designed using a fuzzy-based potential function employing optimal routing parameters. The optimal routing variables, such as routing probability and the transmission rate are obtained using a discrete optimisation problem. To deal with the disturbances and uncertainties in the physical system, an adaptive second-order sliding mode control(SMC) scheme has been proposed for the relative motion control of the networks of robots, where the disturbances are estimated using a novel disturbance observer and the controller parameters are updated online using an adaptive tuning algorithm derived based on Lyapunov theory. The robustness of the proposed path planner and the control scheme are validated through simulation as well as through real-time experimentation based on Pioneer P3-DX robots. The comparison results based on conventional SMC and adaptive SMC are also drawn.

This study proposes a parameter estimation method, together with a grid synchronisation algorithm, for wind turbine-driven doubly fed induction generators (DFIGs). Aiming at achieving an automated control procedure, their integration with a previously published power control strategy is also addressed. During a normal operation mode, the DFIG is grid-connected and generated power is commanded by combining a sliding-mode control (SMC) scheme, which provides high dynamic performance and robust behaviour, with a model reference adaptive system observer, estimating both rotor position and speed without the use of mechanical sensors. In order to preserve performance during start-up and grid connection without the additional requirement of an encoder, the proposed parameter estimation and grid synchronisation schemes are both sensorless. Moreover, the same SMC structure of the power control strategy is also adopted for the grid synchronisation algorithm, which facilitates transfer between synchronisation and power controllers at the instant of grid connection. Thereby, a global sensorless SMC configuration result, which is self-matched by the parameter estimation process. The resulting scheme has been applied to a hardware-in-the-loop-based DFIG virtual prototype under realistic wind conditions, obtaining satisfactory results.

In the energy resource-constrained wireless applications, turbo codes are frequently employed to guarantee reliable data communication. To both reduce the power dissipation of the turbo decoder and the probability of data frame retransmission in the physical layer, memory capacity reduced near optimal turbo decoder is of special importance from the perspective of practical implementation. In this regard, a state metrics compressed decoding technique is proposed. By inserting two modules in the conventional turbo decoding architecture, a smaller quantization scheme can be applied to the compressed state metrics. Furthermore, structure of the inserted modules is described in detail. We demonstrate that one or two rounds of compression/decompression are performed in most cases during the iterative decoding process. At the cost of limited dummy decoding complexity, the state metrics cache (SMC) capacity is reduced by 53.75%. Although the proposed technique is a lossy compression strategy, the introduced errors only have tiny negative influence on the decoding performance as compared with the optimal Log-MAP algorithm.

In the implementation of turbo-like decoder, the size of state metrics cache (SMC) has a predominant impact on the core area and the overall power dissipation. Different from previous reported decoding schemes, in the proposed decoding schemes, a compressing module and a regeneration module are added to the decoder. The compressing module sorts the forward state metrics from the minimum to the maximum, by which an index sequence and the corresponding increase metrics are calculated, and subsequently are stored in the SMC. In the regeneration module, the forward state metrics are estimated with the index sequence and the increase metrics that accessed from the SMC. With the cost of dummy calculation that is performed by the compressing and the regeneration modules, two decoding schemes are proposed. For an eight-state turbo codes, the linear and the nonlinear estimation based decoding schemes reduce the SMC size by 62.5% and 57.5%, respectively. The bit error rate (BER) simulation is performed for both binary turbo code and duo binary convolutional turbo code, and shows BER of the linear estimation-based scheme is superior to that of the enhanced max-log-MAP (the maximum a posteriori probability) algorithm, whereas BER of the non-linear estimation-based decoding scheme is very close to that of the near optimal decoding scheme.