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Abstract-This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a PMSM. The new results are based on a cascade model predictive control scheme with embedded disturbance model, where the per unit model is utilized to improve the numerical condition of the scheme. Results from an experimental application are given to support the design.

In this paper, a constrained space maneuver vehicles trajectory optimization problem is formulated and solved using a new three-layer-hybrid optimal control solver. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability when compared with other typical optimal control solvers. A Monte-Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.

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The Space Maneuver Vehicles (SMV) [1, 2] will play an increasingly important role in the future exploration of space, since their on-orbit maneuverability can greatly increase the operational flexibility and are more difficult as a target to be tracked and intercepted. Therefore, a well-designed trajectory, particularly in skip entry phase, is a key for stable flight and for improved guidance control of the vehicle [3, 4]. Trajectory design for space vehicles can be treated as an optimal control problem. Due to the high nonlinear characteristics and strict path constraints of the problem, direct methods are usually applied to calculate the optimal trajectories, such as direct multiple shooting method [5], direct collocation method [5, 6], or hp-adaptive pseudospectral method [7, 8]. Nevertheless, all the direct methods aim to transcribe the continuous-time optimal control problems to a Nonlinear Programming Problem (NLP). The resulting NLP can be solved numerically by well-developed algorithms such as Sequential Quadratic Programming (SQP) and Interior Point method (IP) [9, 10]. SQP methods are used successfully for the solution of large scale NLPs. Each Newton iteration of the SQP requires the solution of a quadratic programming subproblem a Ph.D.

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multi-layer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.

Virtual physics based approach is one of the major solutions for self-deployment in mobile sensor networks with stochastic distribution of nodes. To overcome the connectivity maintenance and nodes stacking problems in the traditional virtual force algorithm (VFA), an extended virtual force-based approach is investigated to achieve the ideal deployment. In low-Rc VFA, the orientation force is proposed to guarantee the continuous connectivity. While in high-Rc VFA, a judgment of distance force between node and its faraway nodes is considered for preventing node stacking from nonplanar connectivity. Simulation results show that self-deployment by the proposed extended virtual force approach can effectively reach the ideal deployment in the mobile sensor networks with different ratio of communication range to sensing range. Furthermore, it gets better performance in coverage rate, distance uniformity, and connectivity uniformity than prior VFA.