IET Control Theory and Applications最新文献

Considering the importance of solder paste printing in the production process of surface mounted technology (SMT), as well as the decisive impact of key process parameters on the solder paste printing effect. Traditional methods, whether manual or machine tuning, suffer from significant production capacity losses due to long downtime, and machines cannot adaptively adjust parameters based on human expert knowledge, thereby affecting the qualification rate of solder paste printing and the efficiency of SMT production lines. This paper proposes a human–machine integration optimization method for key printing process parameters. By establishing a printing quality prediction model and a key process parameter strategy model, a closed-loop control system has been formed to achieve machine autonomous parameter tuning with expert knowledge. And this paper has completed the establishment of the strategy model based on deep reinforcement learning methods, enabling the SMT production line to predict and adjust key process parameters in real time based on SPI data. In addition, the optimization method described in this paper retains the final decision-making authority of human operators to ensure emergency correction of prediction bias and decision failure history in the system. The final experimental results of this paper indicate that the proposed optimization method performs well in terms of qualification rate, correction effect, SPI data prediction, etc. These demonstrate the effectiveness and value of the proposed human-on-the-loop optimization method in SMT production lines.

As the demand for various practical applications continues to increase, challenges such as time consumption have compromised the real-time capabilities of formation agents. Model predictive control (MPC) is known for its computational complexity, which can result in synchronisation issues among followers and leaders. In this study, we propose a dual-layer formation control strategy. The upper layer focuses on trajectory planning and collision avoidance, utilising MPC and control barrier functions to derive the desired velocities. Within the MPC framework, this approach simplifies the control of second-order systems—incorporating both trajectories and velocities—into first-order systems that only require trajectory management. In the lower layer, we establish a new predefined-time leader-follower formation control for multiple vessels, designed to achieve the desired velocity. The proposed method is validated through simulations involving multiple unmanned surface vessels.

In safety-critical control systems such as autonomous vehicles and medical devices, managing the risk of rare but severe tail events under uncertainty is crucial. This paper addresses this challenge by proposing a risk-aware control framework that integrates the worst-case conditional value-at-risk (CVaR) with control barrier functions (CBFs). Specifically, we formulate risk-aware safety constraints based on the worst-case CVaR, and show that the resulting risk-aware controllers can be computed via quadratic programs (for half-space and polytopic safe sets) or a semidefinite program (for ellipsoidal safe sets). Numerical simulations on an inverted pendulum illustrate that the proposed approach ensures safety under various scenarios and significantly reduces the safety constraint violation compared to existing CBF approaches. Overall, we show that incorporating worst-case CVaR into CBF design offers a tractable solution for safety-critical applications under uncertainty.

This article solves dominance regions for boundary-guarding games based on optimal control, where autonomous vehicles with rotation constraints serve as defenders to guard the target zone. Based on the definition of transition point, the minimum reach time is explicitly expressed in unbounded and convex domains, respectively. Using the proposed explicit expression of minimum reach time, this article develops a numerical algorithm to generate dominance regions for boundary-guarding games. Finally, simulation results are provided to verify the algorithmic validity to generate dominance regions for rotationally-constrained autonomous vehicles.

The paper studies the distributed Nash equilibrium (NE) seeking problem for noncooperative games of networked Euler-Lagrange systems with unknown parameters. Unlike the existing work based on the tracking method, resorting to a sliding-mode extended state observer for the estimation of unknown parts in the Euler-Lagrange system, a distributed algorithm is proposed to seek NE of the game. Moreover, to facilitate the practical applications of the designed continuous-time algorithm, a local event-triggered communication scheme is presented to alleviate communication burden. Finally, the formation of networked Euler-Lagrange systems is taken as an example to verify the proposed algorithm.

This paper is concerned with the problem of elevator speed tracking. To reduce the computational complexity of the standard model predictive control (MPC), we propose an event-triggered MPC method that guarantees control effectiveness. This method includes two control stages: initialisation and online optimisation. During the initialisation stage, a supervised learning technique is employed to approximate the MPC using sample data. The online optimisation stage involves controlling the elevator system to track an ideal speed curve with a designed event-triggering mechanism. The proposed method is evaluated against the standard MPC in the simulation by tracking various speed curves. The results demonstrate that the proposed method significantly reduces computational time while preserving tracking accuracy, making it more suitable for real-world elevator systems.

To recover steady, straight-level flight of a high-angle-of-attack aircraft from its oscillatory spin, a MIMO super-twisting sliding control approach is proposed in this study. Since at high angles of attack, the aerodynamics governing the aircraft is highly nonlinear, tabulated data are utilised to ensure the validity of the results up to an angle of attack of 90°. Regarding uncertain aerodynamic coefficients, the robustness of the control approach is necessary. It is shown that the first-order classical sliding control and power rate reaching law methods are successful approaches to recover an aircraft from its state of spin in the absence of aerodynamic parameter uncertainties. However, in the presence of these uncertainties, chattering affects their performance and the altitude required to perform the recovery manoeuvre, referred to as altitude gain, significantly increases. To overcome these issues, a second-order sliding control algorithm is proposed in this study. The system outputs are considered as roll, pitch, rate of yaw change to attain level flight, and rate of change of altitude to assure straight flight. Thus, a 4 × 4 super-twisting SMC scheme is developed. Finite-time convergence of sliding variables, which guarantees asymptotic stability of the aircraft control system, is proven via the Lyapunov direct method. Simulation results illustrate that the proposed control algorithm serves not only as a reliable approach to perform the recovery manoeuvre but also as a highly effective method to overcome aerodynamic uncertainties without inducing chattering in control inputs. In addition, it enables the recovery manoeuvre to be performed with lower altitude gain.

Sizing and energy management strategy (EMS) for a hybrid electric propulsion system (HEPS), taking into account failures, are challenging areas, especially for regional aircraft. In this paper, a failure-based sizing method and a resilient switching-fuzzy logic control (RSFLC) for a regional hybrid aircraft concept named AFT-ATR42 are presented. For this purpose, the sizing procedure for the HEPS components under the failures of either the all-turbine or the battery pack, which is equivalent to one engine inoperative (OEI) condition in fossil fuel aircraft, has been formulated. The reference battery state of charge (SOC) trajectory has then been determined based on the HEPS simulation during the flight mission. In addition, using the data generated by a combined rule-based regulator and optimal EMS, an RSFLC is tuned by the genetic algorithm that is able to satisfy the reference SOC trajectory. Moreover, model-in-the-loop results are provided to show the satisfaction of HEPS operating constraints. Furthermore, by comparing the performance of the hybrid AFT-ATR42 and conventional aircraft, the effectiveness of the proposed RSFLC for reducing fuel consumption and emissions has been demonstrated. Finally, using the hardware-in-the-loop testing, the suitable and resilient operation of the RSFLC in real-world conditions has been confirmed.

Nonlinear filtering algorithm is the key technology for dealing with complex systems in sensor data processing. To improve the filtering accuracy of the nonlinear filtering algorithm in the non-Gaussian case, an improved version of the Gaussian sum algorithm, the Gaussian sum adaptive sparse grid quadrature filter (GSASQF), is proposed. The proposed algorithm overcomes the challenges by introducing the Gaussian sum principle, which converts the non-Gaussian state and noise in the system into the form of weighted sum of Gaussian components. Based on the Bayesian filtering framework, a three-level sparse grid sampling rule is introduced, with the sparse grid orthogonal filtering algorithm serving as the sub-filter. By determining the sampling point parameters, the filtering process for each combination of Gaussian components is implemented, thereby ensuring the filtering accuracy of each group. In addition, in combination with the ideal of data-driven, the weight of each Gaussian component combination is adaptively updated inversely by the values of the sensor measurement, which improves the global filtering accuracy of nonlinear system under non-Gaussian noise. The combination of these three improvements enables high-precision filtering of non-Gaussian non-linear systems. Theoretical analysis and simulation confirm that the proposed GSASQF algorithm provides advantages in filtering accuracy for nonlinear non-Gaussian filtering problems.

This study concentrates on end effector tracking control of robotic manipulators actuated by brushless direct current (BLDC) motors, having parametric uncertainties in their kinematic, dynamical and electrical sub-systems. Specifically, an operational space controller formulation is proposed that does not rely on inverse kinematics calculations at position level and still ensures practical end effector tracking despite the presence of uncertainties related to the mechanical and electrical dynamics, and the kinematics of the robotic manipulator. Compensation for the uncertainties throughout the entire system is achieved via the use of neural network-based dynamical adaptations, and the overall stability of the closed-loop system is guaranteed via Lyapunov-based arguments. We would like to note that the work addresses the following problems: (i) incorporation of actuator dynamics into the error system in order to achieve increased efficiency, (ii) elimination of the need for position level inverse kinematics calculations for the controller formulation to remove the computational burden and (iii) compensation of the uncertainties throughout the entire subsystem. Experiment studies were carried out on a two degree of freedom planar robot manipulator equipped with BLDC motors to evaluate the effectiveness of the proposed formulation.