Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems

Sliding Mode Controller of Automatic Braking System

This paper presents the development of automatic braking system. The brake modeling that consists of brake pedal mechanism, static control valve, air flow dynamic, variable orifice modeling and brake system hydraulic was developed using a MATLAB SIMULINK software. Then, the braking system will be controlled by using a Sliding Mode Controller (SMC) and PID controller. The result obtained will be validated with the brake torque desired for 100 N.m and 50 N.m. of various frequency. Validation results showed that controller has a better performance compared to the PID controller

Optimal design of vehicle with internal space frame structure subjected to high impact load

Armored military vehicles are heavily used in modern warfare. These vehicles are subjected to lethal attacks from projectiles and land mines. The shocks from these attacks may risk the safety of the occupants and damage the electronic instruments within the vehicle. Extensive research on the analysis and reduction of shocks on civilian vehicles has been performed. Fewer researchers addressed these problems in the case of military vehicles. Space frames are usually used to enhance structural strength of the vehicle while reducing its overall weight. These frames comprise of beams connected together at joints. Recently, space frames were incorporated in military vehicles. In this dissertation, a finite element model of a military vehicle with an internal space frame is developed. The space frame is composed of hollow square cross-section bars and angle sections. These frame members are bolted to the joints. The space frame is enclosed by uniform-thickness armor, except at the turret. The vehicle is subjected to high impact load that simulates a projectile hit. The vehicle design is optimized to reduce the overall mass, and shock at critical locations of the space frame. A lab-scale space frame structure derived from the military vehicle space frame is designed and built. The lab-scale space frame is subjected to non-destructive shock propagation tests. A finite element model of this structure is developed with the objective of matching the experimental results

Rollover warning system for tractor-semitrailer using a modified odenthal rollover index algorithm

Any accident involving a tractor-semitrailer could significantly affect life and component damage as well as the surrounding environment due to the size of the vehicle. One of the main factors that causes tractor-semitrailer accidents is vehicle rollover instability. Therefore, this study aimed to develop a vehicle instability avoidance system for a tractor-semitrailer by implementing it on an accurate tractorsemitrailer model. In developing the model, a new approach was proposed by adopting a virtual Pacejka tire model in modelling the hitch joint of the tractor-semitrailer. The virtual Pacejka tire model has included a 16 degree-of-freedom tractor-semitrailer within MATLAB/Simulink software and later verified using the TruckSim model and validated with published data. It is observed from the verification and validation results, the tractor-semitrailer model using the virtual Pacejka tire model for the hitch joint showed a similar response to the behaviour of the TruckSim model and published data. In terms of vehicle instability avoidance system, the fastest response of the tractor-semitrailer rollover index based on early warning indicator was selected by utilizing several types of rollover index algorithm proposed by the previous researchers. The step steering manoeuvres simulation at a various speed was conducted using MATLAB/Simulink software to obtain the rollover index. It can be observed from the results that the rollover index algorithm proposed by Odenthal provides the fastest index based on the early warning indicator on the tractor unit. In order to optimise the rollover index performance, the Odenthal rollover index algorithm was modified and optimised using Particle Swarm Optimisation (PSO). Finally, the rollover index algorithm was proposed by integrating the modified Odenthal rollover index algorithm with driver steering and vehicle speed inputs instead of lateral acceleration. The modified Odenthal rollover index algorithm performance was evaluated by conducting an experiment involving the step steering manoeuvres, subjected to various vehicle speeds and load conditions through the Hardware-in-the- Loop (HIL) simulation in the TruckSim driving simulator and MATLAB/Simulink software. It was observed from the experimental results that the modified Odenthal rollover index algorithm produced 12.4% faster Time-To-Warn (TTW) than the Odenthal rollover index for the driver. Thus, the modified Odenthal rollover index algorithm demonstrated a better early warning system for the driver to initiate the corrective action

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios

Advances in Fluid Power Systems

The main purpose of this Special Issue of “Advances in Fluid Power Systems” was to present new scientific work in the field of fluid power systems for hydraulic and pneumatic control of machines and devices used in various industries. Advances in fluid power systems are leading to the creation of new smart devices that can replace tried-and-true solutions from the past. The development work of authors from various research centres has been published. This Special Issue focuses on recent advances and smart solutions for fluid power systems in a wide range of topics, including: • Fluid power for IoT and Industry 4.0: smart fluid power technology, wireless 5G connectivity in fluid power, smart components, and sensors.• Fluid power in the renewable energy sector: hydraulic drivetrains for wind power and for wave and marine current power, and hydraulic systems for solar power. • Hybrid fluid power: hybrid transmissions, energy recovery and accumulation, and energy efficiency of hybrid drives.• Industrial and mobile fluid power: industrial fluid power solutions, mobile fluid power solutions, eand nergy efficiency solutions for fluid power systems.• Environmental aspects of fluid power: hydraulic water control technology, noise and vibration of fluid power components, safety, reliability, fault analysis, and diagnosis of fluid power systems.• Fluid power and mechatronic systems: servo-drive control systems, fluid power drives in manipulators and robots, and fluid power in autonomous solutions

Otimização de proteções balísticas de baixo peso para absorção de energia

Technology advances continue to revolutionise military equipment. The development of new firepower induces an interest in the enhancement of protection gear, both for transportation vehicles and personnel. There has been a significant amount of research of methods to increase protection capabilities without increases in the weight of a given defence system. This dissertation seeks to develop an optimisation tool that results in light-weight armour plates without compromising protection capabilities. A thorough study on the propagation of elastic and plastic stress waves aims for a better understanding of how an armour system behaves upon ballistic impact. The first part of this dissertation focuses on the development of a Python script that provides an efficient approach to model generation in Abaqus. It enables the user to avoid time consuming actions when designing ballistic test models to later simulate through the software. This script is also used to validate the theory behind elastic and plastic stress wave propagation while also being able to access output databases and interpret obtained results. The importance of the script is relevant for the second part of the dissertation, which takes advantage of the Abaqus Python Application Programming interface (API) to perform optimisation procedures automatically. Focusing particularly on the application of the particle swarm optimisation algorithm, this work continuously improves the efficiency and accuracy of the mentioned algorithm by dividing three different optimisation problems into several experiments. Each one of the experiments is carefully defined to highlight the impact of a specific operating parameter of the algorithm. A validation of the stress wave propagation and how it is affected upon contact with layered media is carefully conducted through a series of different analysis approaches. It is shown that the plastic stress wave propagates slower than the elastic one and that plastic deformation affects the properties of the generated stress wave, such as wavelength. The implemented particle swarm optimisation algorithm proved to be an effective approach to problem solving, however, for complex problems the operational parameters must be carefully chosen.Os avanços na tecnologia continuam a revolucionar equipamentos militares. O desenvolvimento de novas armas de fogo induz interesse no aprimoramento de equipamento de proteção, para veículos de transporte e pessoal. Tem havido uma quantidade significativa de investigação de métodos para aumentar as capacidades de proteção sem aumento de peso de um dado sistema de proteção. Esta dissertação tem como objetivo o desenvolvimento de uma ferramenta de otimização que resulta em placas de armadura de baixo peso sem comprometer capacidades de proteção. Um estudo cuidadoso acerca da propagação de ondas de tensão elásticas e plásticas procura compreender a forma como um sistema de armadura reage após um impacto balístico. A primeira parte desta dissertação foca-se no desenvolvimento de um código em Python que fornece uma abordagem eficiente à geração de modelos no Abaqus. Isto permite que o utilizador evite ações que consumam tempo ao criar modelos de teste balístico para simular mais tarde através do software. Este código é também usado para validar a teoria por detrás da propagação de ondas de tensão elásticas e plásticas e ao mesmo tempo habilitar o acesso a dados de saída do software e interpretar resultados obtidos. A importância do código é relevante para a segunda parte da dissertação, que tira vantagem da interface de aplicação e programação do Abaqus Python (API) para executar procedimentos de otimização de forma automática. Com foco em particular na aplicação do algoritmo de otimização por enxame de partículas, este trabalho melhora continuamente a eficácia e precisão do algoritmo mencionado através da divisão de três diferentes problemas de otimização em várias experiências. Cada uma das experiências é cuidadosamente definida para destacar o impacto de um parâmetro operacional específico do algoritmo. A validação da propagação da onda de tensão e como é afetada após contacto com um meio material de múltiplas camadas é cuidadosamente estudada através de séries de diferentes análises. É mostrado que a onda de tensão plástica se propaga mais lentamente que a elástica e que deformação plástica afeta as propriedades da onda de tensão gerada, tal como o comprimento de onda. O algoritmo de otimização por enxame de partículas implementado prova ser uma abordagem eficaz para a resolução de problemas, no entanto, para problemas complexos os parâmetros operacionais devem ser escolhidos com cuidado.Mestrado em Engenharia Mecânic

Hybrid machine learning approaches for scene understanding: From segmentation and recognition to image parsing

We alleviate the problem of semantic scene understanding by studies on object segmentation/recognition and scene labeling methods respectively. We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation). On the other hand, under situations when target boundaries are not obviously observed and object shapes are not preferably detected, we explored some sparse representation classification (SRC) methods on ATR applications, and developed a fusion technique that combines the traditional SRC and a group constrained SRC algorithm regulated by a sparsity concentration index for improved classification accuracy on the Comanche dataset. Moreover, we present a compact rare class-oriented scene labeling framework (RCSL) with a global scene assisted rare class retrieval process, where the retrieved subset was expanded by choosing scene regulated rare class patches. A complementary rare class balanced CNN is learned to alleviate imbalanced data distribution problem at lower cost. A superpixels-based re-segmentation was implemented to produce more perceptually meaningful object boundaries. Quantitative results demonstrate the promising performances of proposed framework on both pixel and class accuracy for scene labeling on the SIFTflow dataset, especially for rare class objects