IET Control Theory and Applications最新文献

This paper studies the differential graphical games for linear multi-agent systems with modelling uncertainties. A robust optimal control policy that seeks the distributed Nash equilibrium solution and guarantees the leader-following consensus is designed. The weighting matrices rely on modelling uncertainties, leading to the Nash equilibrium solution, and the solution can be obtained by solving a decoupled algebraic Riccati equation. Simulation studies are finally reported to illustrate the effectiveness of proposed policy.

This study addresses the adaptive dynamic output feedback (DOF) control for unmanned aerial vehicle systems subjected to hybrid attacks. These attacks are delineated by two Bernoulli variables, signifying denial-of-service (DoS) and deception attacks, respectively. In an effort to enhance energy efficiency and reduce communication costs in the presence of network delays, the study introduces an innovative probability event-triggering mechanism (PETM). Subsequently, it crafts a DOF controller that operates based on the available measurement outputs. By harnessing Lyapunov stability theory, the study identifies sufficient conditions that guarantee the stability of the system's augmented dynamics. Finally, the efficacy and applicability of the proposed method are underscored through a case study on an unmanned aerial vehicle.

This paper proposes a novel finite-time limit cycle controller for DC to AC conversion in power boost converters. The stable limit cycle is categorized as a positive limit set. Achieving the control law, based on the concepts of set stability and finite-time stability, is a challenging problem. To fulfill the control objective, a novel sliding manifold is suggested concerning the structure of the wanted limit cycle which facilitates conditions for the development of finite-time stability for the limit cycle. By utilizing the sliding mode control method, a control strategy is proposed that guarantees the desired stable limit cycle is created in the phase plane of the closed-loop system and the phase trajectories reach it in a finite time and remain on it for all future times. This ensures the finite-time generation of the biased sinusoidal oscillations in the output of the power boost converter from a DC input source. The simulation results of a practical boost converter have validated the effectiveness and feasibility of the presented algorithm.

A new payoff function is proposed for cancer treatment optimisation, the tumour elimination payoff (TEP), that incorporates the increase in lifespan if tumour elimination is achieved. The TEP is discounted by drug toxicity and by potential risks, such as metastasis and mutation. An approximation is used for the probability of tumour elimination, $�e^{�-�\alpha �N�\left(�T�\right)�}$, giving a terminal payoff with an exponential dependence on the final tumour size $��N�\left(�T�\right)�$. The optimal solutions for this payoff for simple tumour growth models, (logistic and Gompertz growth), are determined. Using Pontryagin's maximum principle it is proved that bang–bang optimal solutions exist with a single switch; specifically delayed treatment and treat-and-stop solutions at maximum tolerated dose (MTD) exist. There is also a singular arc with constant tumour size. Solutions either have a high probability, respectively, low probability of tumour elimination; these correspond to a post-treatment high probability of cure, and a high probability of relapse, respectively. Optimising over the time horizon $�T$ results in solutions that are either MTD throughout or no treatment, that is, treatment is either beneficial or detrimental. For the logistic growth model, the treatment benefit phase diagram is derived with respect to the patient's expected increase in lifespan and tumour size.

This article introduces an advanced nonlinear controller designed for optimizing the performance of a single-link robot arm featuring a flexible joint. The proposed nonlinear control strategy incorporates a Proportional-Integral (PI) controller in conjunction with a nonlinear velocity feedback component, aimed at providing effective nonlinear damping and suppressing vibrations. To validate the controller's performance, extensive simulations are conducted utilizing machine learning techniques within the Python environment. The performance of the proposed nonlinear damping controller is benchmarked against a conventional linear cascaded P-PI control structure, with both controllers fine-tuned using the Nelder-Mead algorithm. Simulation results demonstrate that the nonlinear damping controller yields substantial improvements in the dynamic behavior of the robot axis arm, showcasing superior step and sinusoidal position tracking performance, along with active vibration damping capabilities. This research contributes valuable insights into the enhanced nonlinear control strategies for flexible-joint robot arms, offering promising advancements in their overall dynamic performance.

This work proposes a weighting-factor-free finite control set predictive torque control (FCS-PTC) for induction motor drives by incorporating a hysteresis controller to eliminate stator flux error from the cost function. The tuning of the weighting factor coefficient in FCS-PTC is critical for improved drive performance. Conventionally, the tuning is based on an intuitive understanding of the dynamical system and empirical methods, which may not yield optimal results. The proposed method regulates the flux by incorporating a separate two-level hysteresis controller and torque by the conventional cost function-based PTC. The cost function uses torque error only and employs a reduced number of voltage vectors. The reduction of voltage vectors is obtained through the flux hysteresis output and sector determination. The proposed work is implemented experimentally on the dSpace DS1104 controller board for a two-level three, three-phase voltage source inverter-fed induction motor drive. The experimental results validate the effectiveness of the proposed work under different drive tests compared to conventional PTC and direct torque control (DTC). The obtained results show comparable dynamic performance of the drive with $��43�\%�$ lower computational burden than conventional PTC. Along with computational benefit, the proposed work demonstrates improved total harmonic distortion (THD) at low-speed regions due to the absence of a weighting factor in the cost function.

A multi-motor coordinated tracking control strategy based on a disturbance sliding-mode observer and an anti-saturation non-singular fast-terminal sliding mode is proposed to address the issues of slow convergence and controller output saturation in multi-motor coordinated control systems. Firstly, a mathematical model of a multi-motor traction system considering uncertain parameter perturbations, external disturbances, and dead zones was established. Secondly, a disturbance sliding-mode observer was designed based on the mathematical model to eliminate motor disturbances and estimate the torque. The observer's forward compensation was added to design a total-consensus-based fast non-singular terminal sliding-mode controller. Then, a fast anti-saturation auxiliary system with fast finite-time convergence was constructed. Finally, a comparative experiment was conducted with traditional anti-saturation sliding-mode control to demonstrate that the proposed method had faster convergence, stronger disturbance rejection, and better tracking performance in the presence of multi-motor parameter perturbations, unknown disturbances, and input saturation.

This study proposes a reinforcement learning-based finite-time cross-media tracking control approach for a slender body cross-media vehicle encountering unknown hydrodynamics, wind, and wave disturbances. Initially, a reinforcement learning framework consisting of the actor neural network and critic neural network is constructed. The critic neural network monitors the actions of the actor neural network and approximates the cost function, while the actor neural network estimates the unknown hydrodynamics and disturbances, minimising the cost function to optimise performance. Subsequently, the command filter featuring finite-time convergence is formulated, effectively managing the corresponding filter error through a proposed error compensating signal. By integrating these techniques, a reinforcement learning-based finite-time control strategy is developed, circumventing the singularity issue inherent in traditional finite-time backstepping strategies. Comparative analysis with existing methods demonstrates the strong robustness of the proposed scheme against unknown hydrodynamics and disturbances, ensuring finite-time convergence of the system's states and optimising controller performance. Finally, simulations confirm the effectiveness and superiority of the presented approach.

This article investigates the problem of monitoring wildfires using a quadcopter uncrewed aerial vehicle (UAV) subject to external perturbations and nonlinear uncertainties. A finite-time control scheme is designed for the quadcopter UAV to monitor the elliptical propagation of a wildfire. The control scheme is composed of an improved nonsingular fast terminal sliding mode control (NFTSMC) and a higher order sliding mode observer (HOSMO). The HOSMO is constructed first to estimate external perturbations, nonlinear uncertainties, and incomplete system states resulting from the system's uncertainties. Then, a novel NFTSMC is developed that utilizes the estimated system states. The Lyapunov theorem is used to demonstrate that the quadcopter UAV can elliptically monitor a wildfire, and the tracking error can rapidly converge to zero within a finite time. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme.

This study presents an energy-based attack detection method involving an encrypted bilateral control system using wave variables. In the considered bilateral control system, the leader and follower receive the follower's force information and the leader's velocity information, respectively, through the wave variables. The considered attack model multiplies the wave variables by an attack parameter, which is possible due to the malleability of the encryption scheme. The bilateral control system will be destabilized if the attacker chooses a relatively large parameter value. This motivates in developing a passivity observer for each leader and follower side to compute the total energy and constructing an energy-based detection method that can be incorporated into the encrypted bilateral control system and is summarized in the presented theorem. Furthermore, this study provides a specific design for reasonable threshold parameters concerning the control system energy. The theorem and the experimental validation confirm that the developed encrypted wave-variable-based bilateral control system with the proposed attack detector is secure and effective as a countermeasure against malleability-based attacks.