International Journal of Control Automation and Systems最新文献

This note aims to empirically verify the control system model of profit rate established by Park and Yang [4]. Specifically, it is shown that the real profit rate has gradually fallen in 8 countries over the last several decades. Then, using the parameters estimated from the real data, the simulated profit rates are obtained from the control system model, and compared with the real profit rates. As a result, this note shows that although the short-run behaviors are different, the control system model well captures the long-run decline in profit rate for all countries.

Nowadays, the efficient use of renewable energies is of great importance both socially and economically. This research focusses on capturing energy from the Sun and studying the modelling and control of the SF60 solar furnace at the Plataforma Solar de Almería (PSA) in Spain. The main goal is to regulate the temperature profile of a silicon carbide (SiC) sample. To do this, two main stages of analysis are established. The first one involves modelling the system, where the nonlinear and linear models are designed and calibrated. The next part is the control of the plant, where control strategies based on both linear and nonlinear models are developed, taking into account the speed of response, steady-state specifications, and process constraints. The main control schemes applied have been PI control (with anti-windup mechanism), feedforward control, and PI-based feedback linearisation control, which have been properly tuned using different methodologies. One of the major contributions of this work has been the validation of the developed control schemes in the real installation, after simulation using the nonlinear model. Finally, the advantages and disadvantages of each controller are discussed.

This study presents a consensus control method for generic linear multi-agent systems (MASs) subject to stochastic deception attacks on actuators and random denial of service (DoS) attacks over communication channels. In this case, the MASs are communicating over a compromised network where the attackers are performing random DoS attacks and injecting data into the control inputs. A fully-distributed robust-adaptive consensus protocol is designed for both types of stochastic cyber-attacks by taking into account the immediate partial information per node rather than global information and by accounting for the signals added by adversaries. To the best of the authors’ knowledge, a fully-distributed consensus control approach with adaptive gains (without requiring any global knowledge for the design) under hybrid cyber-attacks of stochastic nature over a directed graph has been addressed for the first time. Finally, simulation results are presented to further illustrate the theoretical results’ effectiveness.

There are many existing publications on balancing two-wheeled, differential drive robot (TWDDR) covering dynamic modeling, kinematic modeling, path planning, control architecture design and/or simulations. However, there are few papers that cover all of these in a comprehensive manner that is approachable to beginner robotics researchers. This paper provides step-by-step details of the robotic design process including dynamic modeling, kinematic modeling, linearization, autonomous navigation, path planning, and stability control. A cascaded PID control architecture is presented that is capable of stabilizing the robot in less than 1 s with minimal steady-state error and performing large force and torque disturbance rejection. Additionally, a high-level path planning algorithm based on artificial potential fields is demonstrated.

Recently, research on condition-based maintenance (CBM) using artificial intelligence has attracted attention to reduce the maintenance costs of railway vehicles. The air compressor of a railway vehicle that adopts an air brake system is a target for CBM, and in order to reduce maintenance costs and ensure driving stability, technology to detect anomalies in the air compressor is necessary. The long short-term memory (LSTM) autoencoder is used to process sequence data. However, LSTM has limitations in that it cannot perform parallel processing due to its recurrent neural network characteristics, and it is difficult to learn the dependency between data as the sequence data lengthens. In this paper, we propose a novel transformer architecture as an anomaly detection model to learn dependency between air compressor data of a railway vehicle and we demonstrate superior data reconstruction and generalization ability of proposed transformer. We conduct simulation tests based on actual railway vehicle air compressor data and confirm improved anomaly detection performance. We propose an anomaly score definition method using mean squared error to perform reconstruction error-based anomaly detection. With the successful anomaly detection results in the air compressor of a railway vehicle, we demonstrate the effectiveness of a proposed anomaly detection algorithm that applies the moving average of anomaly scores and defines anomaly criteria using three-sigma. We test the proposed anomaly detection method using sensor data received from actual urban railway air compressors and prove its usefulness.

In this paper, a fault estimation technique is addressed to simultaneously estimate system states, actuator, and sensor faults for discrete-time dynamic systems. Specifically, an augmented system is constructed by defining an extended state vector composed of original system states and actuator faults. For this augmented system, an augmented proportional and integral observer is addressed to simultaneously estimate system states, actuator faults as well sensor faults for a discrete-time linear system. A robust augmented proportional and integral observer is designed for Lipschitz nonlinear systems subjected to unknown input uncertainties. The proposed approaches are applied to wind turbine drive train system and electro-mechanical servo system for the validation, which have shown satisfactory estimation performance.

In this paper, a novel approach to workspace expansion for eye-in-hand industrial robots is presented to address the problem of the camera’s field-of-view (FOV) limitation in hybrid vision/force control. During the interaction with the workpiece, the camera and the workpiece are at a short distance from each other. Thus, the FOV is very small, which restricts the robot’s workspace. To handle this issue, instead of using only a feature object in conventional image-based visual servoing (IBVS), an array of objects is provided on the workpiece in a way that at least one object is entirely in the FOV. However, the conventional IBVS cannot be employed for hybrid vision/force control of such tasks. Thus, for this purpose, using a fuzzy inference system (FIS) and orthogonality principle, a novel hierarchical sliding surface is devised, and the continuous integral sliding mode controller (CISMC) is adopted, which leads to a robust and precise control method to fulfill the mentioned task. The stability of the proposed method is also proved. Experimental tests are conducted using an industrial robot on a workpiece whose results reveal the feasibility and effectiveness of the proposed approach. The results are also compared with traditional methods and show that the workspace expansion and control performance have been improved to a great extent.

This paper presents a linear compound control strategy for nonlinear aircraft system at high angle of attack. Firstly, extended state observers are designed to overcome model dependence by estimating and compensating the total disturbances including the parameters uncertainties and strong coupling among channels. Secondly, the controlled subsystems are approximately converted to linear integrals based on dynamics compensation linearization, respectively. Subsequently, the generalized predictive controllers are designed based on the integral type prediction model instead of solving the Diophantine equation online, which reduces the computation burden of inverse control incremental matrix. Finally, some simulation results and analysis for aircraft at high angle of attack are carried out to prove the effectiveness of the proposed control strategy.

The partial potential energy shaping control of fully actuated torque–driven robot manipulators in joint space is addressed in this paper. In contrast to the well-known potential energy shaping control of robot manipulators–which achieves global joint position regulation–here the term partial means to cancel out the natural potential energy at the joints selected by the user via the feedback control law. This formulation is useful when the robot joints are intended to track a desired time-varying trajectory that has joints with null potential energy. To the best of the authors’ knowledge, this is the first time that a formal analysis is presented on joint position tracking of robot manipulators by means of an adequate kinetic energy shaping plus total damping injection with partial potential energy shaping. The proposed controller is designed via an energy shaping plus damping injection approach, and the closed-loop system analysis is carried out via the Lyapunov’s theory and LaSalle’s theorem. Real-time experimental results on a manipulator arm model of two degrees of freedom illustrate the main results.

Extensive research has been conducted on mission autonomy for multiple aircraft and air mobility operations because of the increasing demand for developing manned-unmanned teaming. Besides a few studies, research focusing on real terrain environments and non-linear dynamics for multiple large unmanned aircraft operations for high-speed maneuvers remains lacking. This study proposes an integrated framework that includes mission planning for low-level flight control laws in 3D terrain environments with complex non-linear flight dynamics. A genetic algorithm with double chromosomes was used for task allocation, and a rapidly exploring random tree star with a line-of-sight path optimization method was used for fast and optimal path planning. Further, spline interpolation was applied to generate flyable paths and trajectory commands for control laws. A demonstration was performed based on non-linear flight dynamics with incremental backstepping-based trajectory tracking control laws. In addition to integrating these methods, a trajectory envelope was introduced to consider the effects of the complex non-linear dynamics. The integrated method predicts whether the given trajectory will be stable for the selected rotorcraft unmanned aerial vehicles by checking the trajectory before the flight and converting it to a continuous connection of the maneuver library using flight regime recognition. Based on this simulation, the proposed framework for multiple aircraft operations with 3D terrain and non-linear flight dynamics proves its capabilities.