Advanced Control for Applications最新文献

The four-switch Buck-Boost (FSBB) converter are widely used in combination with other isolated converter to extend the voltage range capability of the overall structure. When the converter operates in buck-boost mode and boost mode, it exhibits a right half plane zero (RHPZ) in the control to output transfer function. This characteristic would cause negative impact to stability of the converter. In order to eliminate the RHPZ, a novel modulation method is proposed in this paper. The buck mode is introduced to the modulation to adjust the voltage gain, and the corresponding average state space modeling for the FSBB with the proposed modulation is established. The simulation of the converter with the proposed modulation method and traditional modulation are presented. Finally, experiment results by hardware in the loop (HIL) platform is employed to verity the correctness of theoretical analysis results.

The study of missile guidance systems is a well-known nonlinear control engineering area of research. To enhance the control performance of a missle guidance system, several technologies have been proposed in existing works. To resolve the weighting matrix selection issue of a linear quadratic Gaussian (LQG) controller for the surface-to-air missile guidance control system, this study utilizes the particle swarm optimization (PSO) technique. Selecting the best state (Q) and input (R) weighting matrices is a significant difficulty in the design of the LQG controller for real-time applications since it affects the controller's performance and optimality. The weighting matrices are often chosen by a trial-and-error method that not only complicates the design but also does not yield optimal outcomes. Therefore, in this paper, a PSO method is developed and used in the design of the linear quadratic regulator (LQR) and LQG controllers for the surface-to-air missile control system to choose the elements of the Q and R matrices in the best possible way. Finally, a comparative analysis between the designed controllers was presented. The results shows that a good performance was achieved by using the proposed PSO-tuned design process. The LQG and LQR are designed by manually adjusting the weighting matrices and utilizing an intelligent procedure, PSO algorithm which achieved optimal results. Further results indicate that the designed controllers, the PSO tuned LQR and LQG achieved a better performance over the manually adjusted LQR and LQG controllers.

Active disturbance rejection control (ADRC) is considerably applied due to its advantage of focusing on merely dominant parameters. Research on flexible systems frequently confronting perplexing disturbances can utilize this method to simplify irrelevant items as a single variable. In this paper, we focused on vibration control problems in flexible systems with the application of ADRC and constructed a second-order system model under the guideline of fundamental principles of ADRC and an innovative algorithm for tuning feedforward compensation ADRC. During the simulation, we discussed three cases in which each solely one parameter varies while others are kept invariant. Time, open-loop frequency, and close-loop frequency responses were respectively analyzed in all cases as to determine the stability of the system. According to the simulation results, we arrived at the conclusion: we should choose the specification of a flexible system within an intermediate range and evade from critical system parameters to procure stability and efficiency.

Steering motion bestows autonomous underwater vehicles (AUVs) with the agility to navigate intricate paths and trajectories precisely. Ensuring effective steering position stabilization in underwater vehicles is paramount, as it enables precise navigation and enhances safety, efficiency, data accuracy, adaptability to changing conditions, and the overall success of diverse underwater missions. This article addresses the challenging task of steering position stabilization in underactuated AUVs. To achieve this, we employ an interconnection and damping assignment passivity-based control method to design a control law tailored for steering position stabilization. Our approach considers the nonlinear dynamics of a six-degrees-of-freedom steering motion in AUVs. The control objective involves assigning a suitable energy function and reshaping the interconnection and damping structure to render the closed-loop system asymptotically stable at the desired equilibrium point. The robustness of our proposed control law is assessed rigorously, subjecting it to modeling uncertainties and underwater disturbances. Our findings are substantiated with simulation results that support the efficacy of the designed control law. Notably, we base our simulations on experimentally validated steering motion parameters obtained from the REMUS 100 AUV, enhancing the real-world applicability of our research.

This paper studies the formation control of multiple quadrotor unmanned aerial vehicle systems (MQUAVSs) with external disturbance. A new adaptive fixed-time cooperative control protocol is designed for MQUAVSs. A fixed-time command filtered compensation control technology is presented to overcome the “explosion of complexity” issue, and a new fixed-time error compensation signal is designed to compensate the filtering error, which improves the convergence speed of the system. Adaptive neural network technology is introduced to deal with unknown nonlinear functions in the system. A fixed-time stability theorem is presented for MQUAVSs to ensure that MQUAVSs can reach the predetermined formation and the formation tracking errors converge to the neighborhood of the origin in a fixed time. Finally, the effectiveness of the proposed method is verified by the formation simulation of MQUAVs.

This paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747-400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747-400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747-400 with small disturbance theory linearization methods and ANN based non-linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747-400 commercial aircraft was presented. The ANN controller outperformed the PID-PSO and PID-GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID-GUI, PID-PSO, and ANN controllers for pitch control of the Boeing 747-400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID-PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory.

This paper provides an overview of various control strategies for DC–DC converters along with a brief discussion on various performance indices for evaluating the reliability of designed controller. DC–DC converters are emerging as the fastest-growing interfacing devices in the world because of their emergent applications in almost all domains of engineering. Notably, the non-linear behavior of DC–DC converters offers a perplexing problem in designing a robust controller. A robust controller must ensure proper closed-loop stability with desired performance and reliable control for output voltage regulation in the event of various possible perturbations. This creates the requirement for practically implementable non-linear controllers that can effectively overlook the drawbacks of linear controllers. The prominence of this composition is based on the expansion in tuning techniques of proportional-integral-derivative controller gain parameters using linear strategies and heading towards non-linear strategies by reviewing 196 research papers. The modification in non-linear control strategies in terms of their fundamental features associated with key benefits and limitations are comprehensively reviewed. This study mostly revolves around non-linear internal model control (IMC) strategies for parameter tuning of IMC-based controllers for various order of plants with an improved degree of freedom.

For the needs of vehicle vibration test platform with high precision, large load capacity and fast response, the three-dimensional model design and analysis of vehicle vibration test platform are carried out; in order to improve the motion performance of the platform, a vibration test plat-form control strategy combining hybrid heuristic algorithm and PID control is proposed. Based on the designed 3D model parameters, the single-channel mathematical model of the servo-electric cylinder is derived and a hybrid heuristic algorithm PID optimization model is established to compare and analyze the control performance of the platform with the Ziegler-Nichols method PID. The results show that the step system overshoot is 3.80% and the dynamic performance of the system is significantly improved when the hybrid heuristic algorithm PID control is used. The simulation system model of vehicle vibration test platform control is established, and the operation results show that the platform is closer to the input signal in the spatial position change curve when the hybrid heuristic algorithm PID control is used. Its maximum displacement error is 0.09 mm, and the motion accuracy of the system is improved by 61% compared with the Ziegler-Nichols method PID control.

Underactuation poses a significant challenge to space mission control and performance. This article investigates the non-linear attitude tracking control problem for a remote sensing satellite underactuated by a reaction wheel (RW) actuator fault. First, a timeline close to the in-orbit reality of an underactuation fault is presented. Then, the fault detection and diagnosis strategy is performed in a finite-time decision window. The failed actuator is excluded from the control loop by forming the proposed reconfiguration window to transition from a 3 RWs configuration to 2 RWs. The underactuation fault-tolerant control is designed according to the active method, where the adaptive robust control law employed for fault-free conditions is switched to the underactuated attitude tracking control (UATC). The structure of UATC is based on kinematic and adaptive backstepping dynamic controllers. The effect of unknown bounded external disturbances is considered with an adaptive estimation term. The asymptotic stability of the closed-loop control system is proved via Lyapunov theory in the presence of parametric uncertainty. Due to the underactuation, a new approach proposed in the prescribed performance function is interval error constraints, which include the pointing accuracy and stability requirements in imaging time intervals. Finally, the results of the multidisciplinary simulation and experimental test confirm the applicability of the underactuation fault-tolerant control.

Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.