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With the increasing reliance on unmanned logistics through drone technology, the challenge of efficiently transporting heavy cargo has emerged. The study delves into the complexities of multi-drone cargo transportation, focusing on the stability of transported goods. To address the limitations of single drones in carrying heavy loads, we propose the integration of a disturbance observer-based (DOB) controller to effectively estimate disturbances without intricate calculations or additional sensors and specific knowledge of the cargo’s characteristics, thereby enhancing cargo movement stability. This estimated disturbance is countered by dynamically adjusting the drone’s inclination, reducing the cargo swing angle, and maintaining formation to ensure a balanced weight distribution during transportation. In addition, we investigate the impact of positional accuracy on overall performance and find that a PID controller combined with the DOB controller outperforms both standalone PID control and the combination of PID and formation control relative to the desired reference. Real-world experiments, which have rigorously validated the efficacy of our proposed method, demonstrate its ability to neutralize disturbances and stabilize drones within constrained areas quickly, instilling confidence in its reliability and effectiveness.

This paper deals with longitudinal motion control of electric vehicles (EVs) driven by in-wheel-motors (IWMs). It shows that the IWM-EV is fundamentally a multi-agent system with physical interaction. Three ways to model the IWM-EV are proposed, and each is applicable to certain control objectives. Firstly, a nonlinear model with hierarchical structure is established, and it can be used for passivity-based motion control. Secondly, a linearized model with rank-1 interconnection matrix is presented for stability analysis. Thirdly, a time-varying state-space model is proposed for optimal control using linear quadratic regulator (LQR). The proposed modellings contribute the new understanding of IWM-EV dynamics from the view point of multi-agent-system theory. By choosing the suitable control theory for each model, the complexity level of system design is maintained constant, no matter what the number of IWMs installed to the vehicle body. The effectiveness of three models and their design approaches are discussed by several examples with Matlab/Carsim co-simulator.

In this study we give a feedback control method for steering support system. In this method we consider the stability of the path tracking control system under the limitation of steering angle, so that drivers can drive safer and more comfortable. In order to do this, we have derived linearized system from the nonholonomic kinematic system by the differential flatness consideration and linear control law which stabilizes the path tracking control system. Furthermore, feedback gains are tuned to satisfy the closed loop stability and the limitation of the steering angle by using Particle Swarm Optimization. Usefulness of the proposed control method has been demonstrated by experiments using a 1/10 scale robot car.

A new method for the planning and autonomous execution of a single-trajectory, velocity-independent, parallel parking manoeuvre for autonomous vehicles is presented. The procedure commences with the identification and pre-selection of a smooth sigmoidal trajectory known as the Gompertz curve in parametric format. Trajectory parameters are determined in real-time during the path-planning phase using an optimization scheme in order to generate a candidate path. The optimization scheme takes into account the maximum steering angles that can be physically realized and checks the generated candidate trajectory for collisions. Thereafter, the trajectory is reparametrized to arc-length format using the cubic interpolation method and the vehicle orientation at every point of the trajectory is deduced. Following that, values of the steering angle(s) are determined. In the final step, the vehicle uses dead-reckoning to follows the arc-length parametrized path in reverse in order to park itself in a single-manoeuvre. The proposed method is substantiated through both extensive simulations and real sensor data.