IET Control Theory and Applications最新文献

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID controller, leveraging its simplicity and minimizing computational load in real-time applications. The fractional idea is an advantageous method that offers several benefits, such as reduced overshoot and settling time, enhanced frequency response, non-integer order dynamics, and, more importantly, higher robustness to noise and parametric variation. Despite the advantages reported by this control technique, a proper gain tuning is needed to enhance its dynamical performance and decrease its sensitivity to error. Thus, a modern algorithm known as SO tunes the values of the gains in the controller to affect the efficiency of this method. This algorithm is a novel strategy with numerous merits compared to others, using its bi-directional search and elite opposition-based learning strategies. The SO algorithm and its variants offer a promising alternative for solving optimization problems, combining efficiency, adaptability, and competitive performance. The contribution of this work lies in utilizing the SO algorithm to enhance the performance of the FOPID controller, enabling faster convergence and improved stability under varying operating conditions. The proposed approach is validated through both simulation and hardware-in-loop experiments, demonstrating superior performance compared to conventional control methods.

This study concentrates on achieving quasi-bipartite synchronization within signed coupled inertial neural networks featuring time-varying delays. The pinning controller technique addresses this problem within a structure incorporating cooperative and competitive interaction among the nodes. With the structurally balanced networks, some linear matrix inequality based sufficient conditions are derived for both reduced and non-reduced order methods with the help of Lyapunov–Krasovskii functional to achieve the quasi-bipartite pinning synchronization criterion. Further, the error bound is derived analytically for the leader–follower representation of the signed coupled inertial neural network model. At last, a numerical simulation result is provided to verify the correctness of the established theoretical results.

This paper proposes a new distributed fault estimation method based on the $�L_1$ performance, along with its circuit implementation. In order to achieve this objective, the paper begins by offering a thorough model of interconnected systems with time-varying delays, which incorporates multiple faults, input/output disturbances, and uncertainties. Next, a set of $�L_1$ distributed estimators is designed to simultaneously estimate the states of the system as well as different types of faults including actuator and sensor faults within all subsystems. This observer is robust against disturbances, uncertainties, and time-varying communication delays. To this end, sufficient conditions are formulated as linear matrix inequalities to ensure that the dynamics related to estimation errors remain robustly stable and also attenuate disturbances. The estimation accuracy and robustness of the proposed approach are studied by an illustrative example. Furthermore, its effectiveness and superior performance are confirmed by comparison with the related methods in the literature. Additionally, the circuit implementations of the system and the suggested estimator are presented for practical applications.

Wheeled mobile robots (WMRs) have become increasingly vital role in modern industries. This research proposes a novel finite-time prescribed performance sliding mode control (SMC) algorithm for the trajectory tracking of WMRs under effects of wheel slipping, wheel skidding, and external disturbances. The proposed approach consists of two key components. First, a novel sliding surface is proposed based on a prescribed performance function (PPF) and a non-singular fast terminal sliding function (NFTSF), referred to as PP-NFTSF. The proposed PP-NFTSF ensures that tracking errors converge to zero in finite time, while the PPF and a transformed error function ensure stability throughout the robot's operation by maintaining error states within predefined bounds. This framework ensures boundaries around zero, thus guaranteeing that the position tracking error will be zero when the transformed error reaches zero. Second, a novel finite-time non-singular fast terminal SMC (NFTSMC) law with prescribed performance tracking errors, referred to as FPP-NFTSMC, is proposed. This control law incorporates a second-order algorithm to generate a continuous control signal, effectively minimizing the chattering phenomenon of SMC. Overall, the proposed control method maintains all the advantages of PPF, NFTSMC, and the second-order algorithm, achieving high position tracking performance, decreasing the chattering phenomenon, obtaining finite-time convergence, guaranteeing tracking error within the boundary of the PPF, and robustness. To illustrate the stability and finite-time convergence of the WMR systems, a proof using the Lyapunov stability theory is performed. The effectiveness of the proposed control method is validated using two working scenarios: tracking straight and U-shaped trajectories for a 4-WMR.

This article considers the stabilization control for a class of chained nonholonomic systems, which are subject to state constraints, external disturbances, and model uncertainties. The studied system is structured in a cascaded form with two subsystems, and the constraint boundaries are functions of both states and time. Then, a unified framework based on state transformations is investigated to implement the constraints on all subsystems. Meanwhile, the neural network technique is used to deal with the system uncertainties. Combined with backstepping method, an adaptive controller is designed to achieve the desired stabilization objectives. The proposed control method is applied to mobile robot systems, and the effectiveness is verified through simulation and experiment.

We introduce an adaptive dynamic surface control (ADSC) method tailored for high-order strict-feedback systems (SFSs) with input saturation, utilizing the fully actuated system (FAS) approach. We simplify the steps in designing the controller by combining the FAS approach with ADSC method to directly control each high-order subsystem as a complete entity, without the need to transform it into first-order systems. Smooth functions and Nussbaum functions are applied to solve the problem of input saturation. We use a sequence of low-pass filters to calculate the higher-order derivatives of the virtual control law. Lyapunov stability theory is used to demonstrate that all signals within the closed-loop system become uniformly bounded, with the tracking error ultimately converging to a small vicinity around zero. We validated the efficiency of the proposed method of control through simulations on a flexible joint manipulator system. In contrast to the traditional first-order system method, which requires four virtual control laws, the proposed method in this paper necessitates only two, resulting in a smaller initial value of the control input.

In this paper, a novel data-driven optimal control method based on reinforcement learning concepts is introduced. The proposed algorithm performs as a workaround to solving the Hamilton–Jacobi–Bellman equation. The main concept behind the proposed algorithm is the so-called IsoCost hypersurface (ICHS), which is a hypersurface in the state space of the system formed by points from which a specific amount of cost is spent by the control strategy in order to asymptotically stabilize the system. The fact that the control strategy requires to spend equal costs in order to stabilize all points on an ICHS is the reason for the naming of the IsoCost concept. Additional assumptions and definitions are mentioned before providing the theory of ICHS optimality. This theory proves, by contradiction, that the ICHS corresponding to the optimal control policy surrounds the ICHSs corresponding to other non-optimal control solutions. This paves the path to finding the optimal control solution using dynamic programming. The proposed method is implemented on the linear, fixed-base inverted pendulum, cart-pole and torsional pendulum bar system models and the results are compared with that of literature. The performance of this method in terms of cost, settling time and computation time is shown using numeric and illustrative comparisons.

This paper proposes an adaptive output-feedback boundary control scheme to stabilize the vibrations of the moving cage in the dual-cable mining elevator system assuming the damping coefficients of the cage axial and roll motions are unknown. The mathematical formulation of the system in the Riemann coordinates is described by a $��4�\times �4�$ hyperbolic partial differential equation (PDE) on a time-varying domain coupled with an ordinary differential equation (ODE) anti-collocated with the control input. At first, the nominal non-adaptive output feedback scheme is formulated by composing a state-feedback controller with the PDE state observer, utilizing the infinite-dimensional backstepping technique. Specifically, we apply two backstepping transformations to design the nominal state-feedback controller. This significantly facilitates the adaptive solutions of the backstepping kernel equations, when unknown parameters are replaced by their time-varying estimates. Then, a Lyapunov-based approach is followed to design the update laws for the unknown damping coefficients and to prove the closed-loop stability. It is shown that all states in the closed-loop system are uniformly bounded and the cage dynamics is asymptotically stable. A numerical simulation is presented to demonstrate the performance of the proposed controller.

This paper addresses the problem of heterogeneous multi-agent systems (HMAS), comprising multiple uncrewed ground vehicles (UGVs) and multiple uncrewed aerial vehicles (UAVs), collaboratively monitoring the wildfire in the presence of actuator faults and communication link faults during the fire monitoring mission. It presents a fixed-time fault-tolerant dynamic formation control scheme designed for HMAS, with the objective of monitoring either the circular or elliptical propagation of a wildfire. The paper adopts a fixed-time extended state observer (FxESO) to estimate the multi-source disturbances arising from external disturbances and actuator faults, ensuring fixed-time convergence of the estimation errors of the FxESO. By utilizing the Lyapunov candidate theorem, the collaborative tracking errors will converge to zero in fixed time, regardless of the initial position, ensuring that all agents in HMAS monitor the dynamic wildfire perimeter. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme.

In this paper, an innovative error-based virtual composite-axis disturbance rejection backstepping control strategy is proposed for electro-optical tracking systems. Tracking accuracy cannot be improved by conventional composite axis structures where target position, velocity and acceleration are unknown and immeasurable. Our proposed method, however, operates without the need for target trajectory input signals or additional sensors. It solely relies on error information to adeptly simulate the compound axis system's functionality. Notably, its error suppression characteristics amalgamate dual-axis suppression features, substantially augmenting tracking performance. Moreover, to further optimize trajectory tracking and counteract the disturbances and uncertainties within the virtual composite axis, a backstepping control strategy is integrated with disturbance rejection. Remarkably, this approach achieves a 31.89% leap in tracking accuracy and a 73.87% boost in disturbance rejection performance. The effectiveness and superiority of the method have been thoroughly corroborated via simulations and experiments.