Fault Tolerant Sliding Mode Predictive Control for Uncertain Steer-by-Wire System.

Fractional-order modified heterogeneous comprehensive learning particle swarm optimizer for intelligent disease detection in IoMT environment

<p>To enhance the efficiency and accuracy of response analysis in practical multivariable complex engineering problems, we introduced a new interval analysis method—multi-body dynamic evolution sequence-assisted particle swarm optimization (DES-PSO) is introduced in this research. This method optimizes the heterogeneous comprehensive learning particle swarm optimization algorithm (HCLPSO) by incorporating a dynamic evolution sequence (DES), addressing the difficulty of HCLPSO in covering the search space, which makes this method suitable for solving multivariable interval analysis problems. The results of two numerical examples prove that both DES-PSO and HCLPSO can give the accurate upper and lower bounds of the response interval. Compared with HCLPSO, DES-PSO improves the computing speed by about 50%.</p>

Study the basic theory and process of chaos particle swarm optimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particle swarm optimization algorithm is effective and feasible to solve the logistics scheduling model problem.

Adaptive heterogeneous comprehensive learning particle swarm optimization with history information and dimensional mutation

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in wide applications in scientific and engineering domains. However, it is inefficient when searching in complex problems spaces. Lots of improved PSO variants with different features have been proposed, such as Comprehensive Learning PSO (CLPSO). CLPSO is an enhanced PSO variant by adopting a better learning strategy that has some chance to choose other particles' historical best information to update velocity. Comparing with the standard PSO, CLPSO has successfully improved the diversity of population and hence avoids the deficiency of premature convergence and local optima. However, this algorithm causes slow convergence speed, especially during the late state of searching process. In this paper, an improved CLPSO algorithm is proposed, termed as ICLPSO, to accelerate convergence speed and keep diversity of population at the same time. We set the learning probability based on particles' own fitness and adaptively construct different learning exemplars for different particles according to particles' own features and properties, which is a more appropriate learning strategy for particles' optimization. Experimental results show that the performance of ICLPSO is better than standard CLPSO and some other peer algorithms, using the functions both on unimodal and multimodal.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Automotive steer-by-wire (SbW) system is a safety-critical system where the safety and reliability issues must be addressed during the operation. This paper proposes a fault detection and isolation-based actuator fault tolerant control for SbW system based on model predictive control. The fault detection and isolation module uses a two-stage Kalman filtering algorithm to provide simultaneous control parameter and state estimation to detect the occurrence of actuator failure. Additionally, a model predictive controller is designed to maintain the original functionality of the SbW systems using the output of fault detection and isolation to update the fault information. We evaluate the performance of the proposed approach by numerical simulations and experiments on our SbW platform. The simulation and experimental results illustrate that our approach achieves a better steering performance than a conventional model predictive controller without fault detection and isolation and stabilise the overall SbW system in occurrence of actuator failure.

Lane changing is one of the crucial tasks for an autonomous vehicle to avoid an obstacle. This task can be performed by controlling the throttle, brake, and steering actuators appropriately based on the analysis of the vehicle’s surroundings. The problem with lane changing is that the control strategy is too complex and needs a high processor for real-time data analysis. In addition, lane changing involves high-level control for vehicle trajectory and low-level control for controlling the steering actuator. This study proposed a well-known method, namely Model Predictive Control (MPC), to determine the vehicle’s lateral position and yaw angle during lane changing maneuver. The optimum steering angle command can control the steer-by-wire (SBW) system from the lateral position and yaw angle in MPC. The Proportional-Integral-Derivative (PID) controller is implemented to control the steering wheel angle in the SBW’s system. Then, the SBW system will turn the wheel of the vehicle plant. From the simulation result, the PID controller can converge the error although the vehicle’s speed is increasing. The result shows that the mean absolute error (MAE) of the SBW system decreases slightly from 0.0115 to 0.0079 as the speed increase from 16 to 41 km/h. From this study, it can be concluded that the MPC and PID controllers can control the vehicle’s trajectory during lane changing by calculating an optimum lateral motion and yaw angle to provide an optimum steering angle for the vehicle to change lanes successfully.KeywordsSteer-by-wire (SBW)Model Predictive Control (MPC)PID controlLane changingAutonomous vehicle

A method of using particle swarm optimisation (PSO) algorithms to optimise the element spacing and lengths of Yagi-Uda antennas is presented. SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2) is used to evaluate the performance of various Yagi-Uda antenna designs. In order to show the capabilities of the PSO algorithm in Yagi-Uda antenna design, three different antenna design cases are optimised for various performance specifications. The three objectives considered are gain only, gain and input impedance only, and gain, input impedance and relative sidelobe level (rSLL). To alleviate the premature convergence problem of PSO, a novel learning strategy is employed. Each design problem is optimised using three variants of PSO algorithms, namely the modified PSO, fitness-distance ratio PSO (FDR-PSO), and comprehensive learning PSO (CLPSO). For the purpose of comparison and benchmarking, equally spaced arrays, genetic algorithm optimised antenna design, and computational intelligence optimised antenna design are considered. The results clearly show that the CLPSO is a robust and useful optimisation tool for designing Yagi antennas for the desired target specifications.

Nonlinear model predictive control, which is conceptually similar to its linear counterpart except that nonlinear dynamic model is used for process prediction and optimization, is a developing control field. The proposed nonlinear model predictive control scheme, which is called nonlinear sliding mode model predictive control, integrates model predictive control and sliding mode variable structure control. A feasible dual-mode control scheme is presented in the paper, sliding mode predictive controller is implemented while the system state is outside the terminal region, besides sliding mode variable structure control designed off-line is used. The resulting control scheme has strong points of the two control methods. By predicting the pre-designed switching function, the predictive control sequence can be found by solving constrained open-loop optimal control problem, and the current control is implemented. At next sampling time the optimizing procedure is repeated. By constraining terminal sliding mode with an inequality, the state is steered into the predesigned sliding mode region, then the closed-loop stability is proved, and furthermore the system performance is analyzed. With some conditions, the method can be extended to a quasi-infinite horizon formulation. Simulation results show that less calculation is needed.

Moth-Flame Optimization (MFO) algorithm is a widely used nature-inspired optimization algorithm. However, for some complex optimization problems, such as high dimensional and multimodal problems, the MFO may fall into the local optimal solution. Hence, in this paper an ameliorated Moth-Flame Optimization (AMFO) algorithm is presented to improve the solution quality and global optimization capability. The key features of the proposed algorithm are the Gaussian mutation produce flames and the modified position updating mechanism of moths, which can improve the ability of MFO to jump out of local optimum solutions. In addition, opposition-based learning is adopted to initialize the population. The AMFO algorithm is compared with 9 state-of-the-art algorithms (such as Lévy Moth-Flame Optimization (LMFO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO)) on 23 classical benchmark functions. The comparative results show that the AMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Furthermore, the AMFO is adopted to optimize the parameters of fast learning network (FLN) to build the prediction model of silicon content in liquid iron for blast furnace, and simulation experiment results from field data show that the root mean square error of the AMFO-FLN model is 0.0542, hit ratio is 91 and the relative error is relatively stable, the main fluctuation is between -0.1 and 0.1; compared with other ten silicon content in liquid iron models, the AMFO-FLN model has better predictive performance.

In this paper, a novel chaos particle swarm optimization(PSO) clustering algorithm for texture image segmentation was proposed. By means of stochastic property and ergodicity of chaos search mechanism, chaos PSO algorithm can effectively avoid getting into the local convergence. The proposed method combined chaos PSO algorithm with nearest neighbor clustering theorem, which can enhance the efficiency and precision of clustering. And the proposed chaos PSO clustering algorithm was applied to texture image segmentation with gray level co-occurrence matrix. The experimental results indicate that the proposed method can get better segmentation results than PSO algorithm and effectively improve the segmentation precision of texture image.Keywordsparticle swarm optimizationchaostexture segmentationgray level co-occurrence matrix (GLCM)

In this article, two dual-loop control methods are proposed for attitude control and reaction wheels momentum management of the spacecraft simulator by assimilating the advantages of sliding mode and model predictive control (MPC). In the first method, by utilizing the high accuracy and constrained features of the MPC, an optimal controller is designed in the inner loop. MPC drives the angular velocity to the desired value while keeping the reaction wheel velocity to a small region near the origin, considering the constraints of the actuators. Furthermore, based on the fixed-time terminal sliding mode (TSM) the proper angular velocity is produced in the outer layer of this composite controller for the MPC part. In the second method, contrary to the first one, in the exterior loop MPC and in the interior loop finite-time TSM have been designed. The incorporated twofold nonlinear controllers’ policy insures a good dynamic and robustness against external disturbances. The high yielding of the control algorithms has been analyzed by simulation and experimental validation utilizing MATLAB software with real-time connection based on LabVIEW.

An improved chaos particle swarm optimization (CPSO) algorithm is proposed on path planning for unmanned aerial vehicle (UAV) to overcome the inadequacy of particle swarm optimization (PSO) algorithm, which falls into local optimum easily and converges slowly in process with poor precision. Through the in-depth analysis of PSO algorithm, the chaos optimization (CO) algorithm principle is introduced into it based on the traditional update operations on the particles’ velocity and position; as a result, the diversity of particles is increased, the suboptimal search on path planning is avoided and the quickness accompanied with accuracy of convergence is improved. Combined with digital map for modeling the UAV’s flight environment, the 3-D path planning is achieved. As the simulation results demonstrated, this hybrid algorithm is superior to the traditional PSO algorithm on path searching, especially in the 3-D environment.