IET Control Theory and Applications最新文献

This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum-wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite-time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer-based finite-time time-varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite-time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented.

Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed-loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed-loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear-quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL-controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non-linear system and then tested on the corresponding real-world hardware platform without any fine-tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).

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.

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.

The paper is devoted to the adaptive tracking control based on the mismatched disturbance observer for motor-driven robotic manipulators. A novel mismatched disturbance observer is introduced, which does not make bounded derivative assumptions on mismatched disturbances in the robotic manipulator system. Additionally, a new adaptive tracking controller for a motor-driven robotic manipulator based on the mismatched disturbance observer is designed, which guarantees that all closed-loop signals are globally uniformly bounded and the tracking error is asymptotic stable. Finally, the simulation comparisons are carried out between the proposed and existing control strategies to demonstrate the superiority of the developed controller.