IET Control Theory and Applications最新文献

Within a robotic context, the techniques of passivity-based control and reinforcement learning are merged with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. The contribution is framed in a scenario where passivity-based control is implemented by means of virtual energy tanks, a control technique developed to achieve closed-loop passivity for any arbitrary control input. Albeit the latter result is heavily used, it is discussed why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity-based techniques are associated with a loss of performance. The use of reinforcement learning allows to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy-aware robotics.

A new dual-motor servo steering system with improved reliability and safety is proposed. But, despite the advantages of the proposed servo system, it is strongly coupled, non-linear and multivariable, where the biggest challenge lies in its tracking and synchronization control. To improve the tracking and synchronization control performance of the proposed servo system, a tracking and synchronization control strategy based on backstepping super-twisting control and multi-level differential coupling is presented. First, a parallel model of the dual-motor is established. Then, backstepping and super-twisting control algorithms are integrated while adaptively optimizing key parameters to ensure the robustness and tracking performance of each motor. After that, an angular synchronization controller with multi-level differential coupling and backstepping super-twisting algorithm is proposed to compensate for the synchronization error of the dual-motor system caused by parameter uncertainty. Finally, the simulation is carried out using Simulink to verify the effectiveness of the proposed control strategy.

With the popularity of smart meters, the frequent information exchange between smart grids and consumers leads to easy leakage of consumers' electricity consumption data. These leaked electricity consumption data are obtained by some malicious attackers and used to infer consumers' behavioural patterns by non-intrusive load monitoring (NILM), which seriously threatens consumers' privacy. Therefore, the multi-agent Hidden Markov energy management model is proposed in this paper to safeguard the privacy of consumers. First, a weighted Bayesian risk model is proposed, which combines privacy leakage risks and energy storage system (ESS) losses in a microgrid with multiple agents. Next, a three-loop model for lithium batteries is constructed to quantify the capacity degradation and cost issues of the ESS. Finally, the multi-objective optimization problem is resolved by integrating the Bayesian risk model with a hidden Markov model to simulate attackers. The proposed multi-agent Markov decision process method is validated on Electricity Consumption and Occupancy (ECO) dataset, and control strategies are evaluated based on different weights in the Bayesian risk model. The results demonstrate that by incorporating the multi-agent approach and energy storage system capacity degradation into the privacy protection strategy, the lifespan of the energy storage system can be significantly increased.

In this paper, the problem of state estimation for systems over wireless networks using user datagram protocol is focused on. It is known that for such a system, the probability density function of the system state follows a Gaussian mixture model (GMM), and the number of components in this model grows exponentially over time, which makes the computation of optimal estimates infeasible. To compute optimal estimates, based on Kullback-Leibler divergence, a strategy with variable step-sizes to truncate and fuse the GMM is proposed. Based on the obtained GMM, a variable step-size estimator is designed to compute optimal estimates during an estimation cycle. The advantages of the proposed estimator are twofold: (1) its estimation performance is superior to that of existing one-step fast estimators; (2) its estimation efficiency is much higher than that of the optimal estimator. Finally, trajectory tracking has been proposed for a real-world hydrogen-powered unmanned aerial vehicle to show the effectiveness of our methods.

In this paper, a novel nonlinear unknown input observer is proposed in order to fault estimation for nonlinear uncertain systems with time delays. By the estimation of the faults, the features are detected such as shape, size occurrence time etc. The time delay is considered a constant and known parameter in the states. The disturbances are investigated in the states and outputs and also, and sensor and actuator faults are considered. The stability of the closed-loop system is guaranteed by Lyapunov–Krasovskii theory and some feasible Linear matrix inequalities (LMI). The proposed method is simulated on a continuous-stirred tank reactor (CSTR) with uncertainties and time delay. Simulation results show the appropriate efficiency of the proposed method.

This paper presents a novel detection and rerouting mechanism for distributed adaptive platoon control of non-linear autonomous connected vehicles under denial of service (DoS) attacks. DoS attacks can cause delays or losses of data packets due to blocked communication channels, leading to reducing platoon performance or even collisions among vehicles. To tackle this issue, the proposed mechanism detects and reroutes communication topology depending on the real-time topology and the number of link failures. Real-time detection divides the scenario of DoS attacks into three parts. According to the different scenarios, rerouting mechanisms will be utilized. A controller adapted to real-time variable communication topology is also designed in this scheme. The adjacency matrix of the real-time communication topology generated by the rerouting mechanism is used to update the controller so that the platoon can remain in a stable state without being affected by DoS attacks. In addition, the sliding mode controller and the observer are designed by solving linear matrix inequalities, and the platoon stability and internal stability are proven. Numerical simulation studies demonstrate that the proposed mechanism and control design can reduce the vehicle state estimate error and platoon-tracking error to ideal states under DoS attacks. The proposed method solves the problem that the existing methods have not considered the number of link failures and the inability to restore communication when the communication topology is paralyzed.

In this paper, an adaptive event-triggered control scheme is proposed for a class of continuous-time linear cyber-physical systems (CPSs) with unknown false data injection attacks (FDIA) and state constraints. First, the adaptive boundary estimation mechanism and Nussbaum-type function are introduced into the two-step backstepping control, which successfully reduces the impact of unknown attack gain. Second, considering that the Nussbaum function causes the chattering problem of the system state, the Tangent Barrier Lyapunov function (TBLF) is used to constrain the state. Then, the controller is designed in combination with the event-triggered mechanism (ETM), which saves communication resources. Based on the designed adaptive event-triggered security control method, all signals of the closed-loop system can be guaranteed to be bounded, and the state constraints are not violated. Finally, the simulation results verify the effectiveness and rationality of the proposed control strategy.

This paper proposes explicit solutions for the algebraic Riccati matrix equation. For single-input systems in controllable canonical form, the explicit Hermitian solutions of the non-homogeneous Riccati equation are obtained using the entries of the system matrix, the closed-loop system matrix, and the weighting matrix. The unknown entries of the closed-loop system matrix are solved by scalar quadratic equations. For a homogeneous Riccati equation with a zero weighting matrix, the explicit solutions are proposed analytically in terms of the system eigenvalues. The advantages of the explicit solutions are threefold: first, if the system is controllable, the solution is directly given and the invariant subspaces of the Hamiltonian matrix are not required; second, if the system is near singularity, the explicit solution has higher numerical precision compared with the solution computed by numerical algorithms; third, for a real system in the controllable canonical form, the non-negativity can be analysed for the explicit almost stabilizing solution.

This article deals with the robust planar rigid formation control problem of three second-order coleaders with unknown flowfields acting on the velocity and acceleration respectively. To yield the uniform boundedness property of the resulting system, an adaptive projection is introduced to design the novel adaptive neural control law. To avoid the derivatives of Gaussian functions of neural networks, dynamic surface is used. Simulation results are provided to illustrate the effectiveness of the proposed control law.

This study presents a systematic methodology for developing a stabilizing controller for a general hybrid systems model. The approach is based on utilizing the small-gain theorem as a means of constructing the Lyapunov function and analyzing the input–output stability of the subsystems in the feedback loop. By considering the control system in a closed-loop configuration with the hybrid system, the small-gain theorem can be applied. In this scheme, a dynamic control system is proposed that satisfies the closed-loop stability conditions. This method applies to various hybrid systems' applications due to its generality. To demonstrate the effectiveness and performance of the proposed control approach, two simulation examples, including a linear hybrid system and a bipedal walking robot, are examined.