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In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires’ dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle’s handling limits and effectively reduce the slip phenomenon of the vehicle.

The majority of large-scale data intensive applications designed by MapReduce model are deployed and executed on a large-scale distributed Hadoop system. Running such application on a large cluster requires a large amount of energy. Therefore improving energy efficiency and minimizing energy consumption when executing each MapReduce job is a critical concern for data centers. We propose a control model based on model prediction control (MPC) for improving energy efficiency of Hadoop cluster while satisfying performance goal. The controller can dynamically scale CPU frequency in facing the workloads changes. The wavelet neural network is utilized to build the prediction model, which can accurately estimate energy efficiency ratio based on the features extracting from the variation of Hadoop workloads. With the help of a wavelet neural network, the controller can guarantee the energy efficiency ratio converges to an ideal set point within a finite settling time. We perform the extensive experiments on a Hadoop cluster to compare our controller with the previous MPC controller. The results demonstrate that our controller outperforms the baseline on energy efficiency control and adapts to workloads of Hadoop cluster.

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.

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.

The thesis deals with the design of a permanent magnet synchronous machine controller that isimplemented on an embedded platform to replace the off-the-shelf controller currently being used in theelectric race car of the KTH Formula Student team. Software implementation of the control algorithmwas tested in laboratory environment on the hardware prototype of a 2-level three-phase voltage sourceinverter.Field oriented control and finite control set model predictive control algorithms were implemented insimulation environment. The latter performed better in terms of reducing switching activity and torqueripple, but needs vastly more computational resources due to its nature of being an online optimizationproblem. Trade-off curve of phase current harmonic distortion and switching activity showed that themodel prediction control algorithm performs better in the low frequency range (1-20 kHz). Obtainedsimulation results were used for power electronics component selection.Field oriented control was implemented on a TMS320F28335 DSP. SPI communication was employedto configure gate driver circuits and perform error handling. The DSP program follows interrupt basedorganization and the main control loop runs on the variable frequency of the pulse width modulation.Low voltage test results on three-phase inductive-resistive load showed that the controller outputssinusoidal current. Efficiency measurement, high voltage and motor testing were hindered by interferencefrom the Silicon-Carbide MOSFETs that prohibited correct operation of hardware. Den här uppsatsen handlar om designen och implementeringen av en motorstyrning för en permanen- magnetiserad synkronmotor, med syfte att ersätta standardmotorstyrningsenheten i KTH Formula Students tävlingsbil. Implementationen av styralgoritmen testades experimentellt tillsammans med en prototyptillverkad frekvensomriktare i labbmiljö. Regleralgoritmer för field oriented control och finite control set model predictive control implementerades och testades i simuleringsmiljö. Den senare algoritmen visade sig prestera bättre i form av lägre vridmomentsoscillationer trots lägre switch-frekvens men den kräver samtidigt mer beräkningskraft. Övertonsinnehållet (THD) i fasströmmarna som funktion av switchfrekvensen undersöktes för de båda regleralgoritmerna, algoritmen för model predictive control gav lägre THD vid lägre frekvenser (1-20 kHz). Simuleringsresultaten användes för att motivera valet av komponenter till frekvensomriktaren. Regleralgoritmen för field oriented control implementerades och testades experimentellt med hjälp av ett utvecklingskort (TMS320F28335) från Texas Instruments. SPI-kommunikation användes för att konfigurera drivkretsana samt för att utläsa felkoder. Experimentalla tester som utfördes på låg spänningsnivå visade att strömmen till lasten var sinusformad. Mätning av verkningsgrad och provning tillsammans med motorn på en högre spänningsnivå gick inte att geno av att de snabba switchförloppen i kiselkarbidtransistorerna störde ut motorstyrningen.