In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

Coordination of Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) has been widely used for non-autonomous vehicle lateral stability control. Recently, some researchers used it (AFS/DYC) for path-following of autonomous vehicles. However, current controllers are not robust enough with respect to uncertainties and different road conditions to guarantee lateral stability of Autonomous Four In-wheel Motor Drive Electric Vehicles. Thus, a coordinated control is proposed to address this issue. In this paper, a two-layer hierarchical control strategy is utilized. In the upper-layer, a self-tunable super-twisting sliding mode control is utilized to deal with parametric uncertainties, and a Model Predictive Control (MPC) is used in order to allocate the control action to each AFS and DYC. Parametric uncertainties of tires’ cornering stiffness, vehicle mass and moment of inertia are considered. Simulations with different road conditions for path-following scenario have been conducted in MATLAB/Simulink. An autonomous vehicle equipped with Four In-wheel Motor and two degrees of freedom vehicle dynamics model is used in this study. In the end, the performance of the proposed controller is compared with the MPC controller. Simulation results reveal that the proposed controller provides better path-following in comparison with the MPC controller.

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Iris (formerly ManitobaSat-1) is a 3-U CubeSat being built in Manitoba via a collaboration between the University of Manitoba and the University of Winnipeg, with additional inputs from the Interlake School Division and York University. The scientific goal of the mission is to investigate the changes to geological materials relevant to a number of planetary bodies, when exposed to the low-Earth orbit space environment. These exposure effects, generally referred to as space weathering, are of interest to the planetary remote sensing community because they can impede our ability to determine the geology of planetary surfaces. Space weathering is expected to most strongly affect the visible spectral region; as a result, we will monitor changes in the "colours" of a variety of relevant geological materials using a three-colour camera.The payload will consist of a suite of ~24 geological samples and a few calibration standards. The geological samples considered for inclusion on the mission are a mix of minerals relevant to multiple planetary bodies.Sample preparation: The geological samples selected for the payload were ground to a fine grain size (<45 µm grain size) to ensure homogeneity at the resolution of the cameras. The samples were vacuum sintered at CIRIMAT in France using the same procedure as for the NASA Perseverance rover's SuperCam calibration targets. The melting temperatures were provided to CIRIMAT to ensure the samples were not destroyed during the sintering process from overheating. For multi-component samples (i.e., rocks) we selected a melting temperature corresponding to the major mineral component. The sintered pellets were sanded by hand to remove a graphite coating that results from the sintering process.Payload testing: The payload samples are required to survive launch conditions, ensure astronaut safety (as they will be deployed from the International Space Station), and the low-Earth environment. To ensure this, we conducted a qualification vibration test campaign at Magellan Aerospace. A prototype sample plate was assembled and subjected to twice the expected vibration profile of the launch vehicle. Following the test, samples were inspected for signs of fragmentation that could pose a debris hazard to the astronauts in the microgravity environment of the International Space Station. This presentation will describe the sample selection, preparation, and qualification process in preparation for the Iris CubeSat mission.