Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research.

Dissipative filtering for two-dimensional LPV systems: A hidden Markov model approach

In this article, the dissipative filtering problem is explored for two-dimensional (2-D) Markov jump linear parameter varying (MJLPV) systems. The underlying system is described based on the frequently-used Fornasini-Marchesini (FM) model with the measurement missing phenomenon. The hidden Markov model (HMM) is adopted to depict the partial accessibility between system and the designed filter modes. Based on this, a HMM-based filter is proposed, and sufficient conditions in the form of parameterized linear matrix inequalities (PLMIs) are established to guarantee that the filtering error system (FES) is asymptotically stable (AS) and strictly dissipative. Then, the corresponding filter synthesis problem is converted into a convex optimization problem. Ultimately, the simulation example is presented to demonstrate the utility of the obtained results.

Surrogate-Based Optimization Design for Air-Launched Vehicle Using Iterative Terminal Guidance

In recent years, the penetration of low-cost air-launched vehicles for nano/micro satellites has significantly increased worldwide. Conceptual design and overall parameters optimization of the air-launched vehicle has become an exigent task. In the present research, a modified surrogate-based sequential approximate optimization (SAO) framework with multidisciplinary simulation is proposed for overall design and parameters optimization of a solid air-launched vehicle system. In order to reduce the large computation costs of time-consuming simulation, a local density-based radial basis function is applied to build the surrogate model. In addition, an improved particle swarm algorithm with adaptive control parameters is proposed to ensure the efficiency and reliability of the optimization method. According to the LauncherOne air-launched vehicle, the overall optimization design problem aims to improve payload capacity with the same lift-off mass. Reasonable constraints are imposed to ensure the orbit injection accuracy and stability of the launch vehicle. The influences of the vehicle configuration, optimization method, and terminal guidance are considered and compared for eight different cases. Finally, the effect on the speed of optimization convergence of employing a terminal guidance module is investigated. The payload capability of the optimized configurations increased by 27.52% and 23.35%, respectively. The final estimated results and analysis show the significant efficiency of the proposed method. These results emphasize the ability of SAO to optimize the parameters of an air-launched vehicle at a lower computation cost.

Neural Networks Application for the Data of PID Controller for Acrobot

Acrobots are a system that has levels of operating states in many investigated cases, and they are subjects to many events during operation due to the mechanisms of locomotion processes. These states have been investigated in specific situations. Due to the limited nature of surveying under conditions without the aid of software fine-tuning the desired output values, designers have to create a number of algorithms that control the system most appropriately in a complex working environment of this system. In this study, the author has proposed design problems to suit the working needs. The author has modeled the objects to be investigated, and at the same time, the author has combined simulation of phenomena that have been documented from the theoretical model of this system before. Control an Acrobot system, including Neural Network Application for the data of the PID controller (closed form). Mathematical models, Simulink, are also presented specifically through this study. The simulation parameters have been adjusted to match the set criteria, and the reader's perception will be more intuitive through simulation interpretations. Based on simulation data, the system performance analysis becomes more accurate than ever. The above suggestions are intended to serve vocational education and scientific research, and at the same time, they also contribute to suggesting new and unique ideas. The ANN is the most intelligent control method currently added in this study to firmly confirm its effectiveness in all problems related to artificial intelligence. Proposing control strategies for different models is also suggested by the author in the conclusion. Neural networks application is a highly applicable method because it works in sync with other methods. In other words, neural networks application can be applied to any situation based on given data. Specifically, in this study, the application of neural networks becomes more flexible and vivid thanks to the data of the PID controller. The purpose of this method is to increase the security of the system against the attack of hackers on facilities of the automatic control system. Simulation is done by Matlab.

Correction-Efficiency-Coefficient-Based Trajectory Optimization for Two-Dimensional Trajectory Correction Projectile

Based on the lift-to-drag ratio of a two-dimensional trajectory correction projectile, in this paper, a novel correction efficiency coefficient model has been proposed for the trajectory optimization of a two-dimensional trajectory correction projectile, and research on the influence a correction efficiency coefficient has on the flight parameters of correction trajectory is carried out. A series of results are obtained through theoretical analysis and simulation calculations, indicating that, the smaller the value of the correction efficiency coefficient is, the stronger the correction ability of the projectile reserves. The trajectory and canard geometry of the correction section of the two-dimensional trajectory correction projectile are optimized by the Gauss pseudo-spectral method and correction efficiency coefficient, and, after taking the correction efficiency coefficient into account, the projectile can accurately hit the target and effectively eliminate the swing phenomenon of the projectile’s lateral trajectory. Meanwhile, a stable roll control command is obtained. When the diameter aspect ratio of the canard is 0.4, both the flight state quantity of the optimized projectile and the roll control command are more stable, and, when the canard shape is trapezoidal, the correction efficiency coefficient is smaller, the result of trajectory optimization is more stable, and the stability of the output roll control command is better. The research results of this paper can provide certain references for both the designs of the two-dimensional trajectory correction projectile’s trajectory and the canard geometry.

Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm

The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies’ co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot’s response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques.

Studies on Multi-Constraints Cooperative Guidance Method Based on Distributed MPC for Multi-Missiles

Cooperative terminal guidance with impact angle constraint is a key technology to achieve a saturation attack and improve combat effectiveness. The present study envisaged cooperative terminal guidance with impact angle constraint for multiple missiles. In this pursuit, initially, the three-dimensional cooperative terminal guidance law with multiple constraints was studied. The impact time cooperative strategy of virtual leader missile and follower missiles was designed by introducing virtual leader missiles. Subsequently, based on the distributed model prediction control combined with the particle swarm optimization algorithm, a cooperative terminal guidance algorithm was designed for multiple missiles with impact angle constraint that met the guidance accuracy. Finally, the effectiveness of the algorithm was verified using simulation experiments.