Flight simulators as an integral component of today aviation industry, play an important role in training the pilots and development of the new equipment. Optimal motion cueing beside the positive characteristics of easy computation and implementation, due to limited performance in keeping the motion system within the workspace in complex maneuvers, is faced with serious obstacles. Predictive control method featured with inherent capabilities of dealing with constraints on inputs and state variables, while maintaining the high quality of the output, is faced with progressive development. The task of model predictive control is solving the optimal problem over the control horizon to accommodate the feasible movement of the flight simulator by decreasing as could as the difference of the perception of motion between the pilots in real vehicle and the simulator. This approach is based on minimizing the quadratic cost function incorporating the sensation of motion, the motion system configuration related state variables as well as input control signal. Although in this method, the design of washout filters are not needed. In this article, the systematic design of motion cueing algorithm based on model predictive control is described and its performance in comparison with optimal washout filter cueing method is illustrated. The proposed motion cueing method posing with much limited and smoother movements in surge-pitch maneuver tends to efficiently maintaining the motion system with in its workspace while preserving the same sense of motion. This results in increasing the capabilities of the motion system to be employed in much complex maneuvers.

In this paper we present a robust hybrid motion/force control1er for rigid robot manipulators. The main contribution of this paper is that the proposed hybrid control system is able to at complies motion objectives in free directions and force objectives in constrained directions under parametric uncertainty both in robot dynamics and stiffness constraint constant. Also, the given scheme is proved global1y stable in the sense that the control objectives are achieved asymptotically, when a signum function is used in the control law, though giving rise to chattering effects. To avoid this problem a saturation function is used. In this case the motion and force errors are proved to be bounded functions. Using the proposed control structure there is no need to measure the derivative of the interaction forces. Some simulation results are given to illustrate the control system performance.

Motion estimation (ME) is a major part of modern video codecs, which requires a huge amount of computations. To overcome this drawback, various techniques have been proposed. In this paper with the aid of fuzzy inference, an efficient algorithm is devised. The proposed algorithm exploits spatial correlation as well as temporal correlation among motion vectors. This algorithm uses fuzzy rules to determine the initial motion vector. After that, a local search around initial vector is carried out. In order to decrease the complexity of the algorithm, a look-up table is used. In this table, defuzzified values are stored. Also, to further reduction of complexity, few computations are performed in the proposed algorithm for stationary and quasi stationary blocks. To determine which block can be regarded as stationary or quasi stationary block, a simple comparison with a predefined threshold is done. The experimental results show that the proposed algorithm performs better than other fast block matching algorithms in terms of picture quality and computational complexity.

In this paper, a new algorithm called Motion Coding Gravitational Search Algorithm (MGSA) is proposed to find a moving target using a unmanned aerial vehicles (UAVs). Using the laws of physics and the properties of the earth, each dimension has its own equation of motion based on the type of variable. Many traditional exploratory methods can not achieve the desired solution in high-dimensional spaces to search for a moving target. The optimization process of the gravitational search algorithm, which is based on the gravitational interaction between particles, the dependence on the distance and the relationship between mass values, and the fit calculation, make this algorithm unique. In this paper, the proposed MGSA algorithm is proposed to solve the path complexity challenge problem in order to find the moving target through motion coding using UAVs. A set of particles in the path of search for the target will reach a near-optimal solution through the gravity constant, weight factor, force and distance, which evolved with many search scenarios in a GSA algorithm. This coded method of motion makes it possible to preserve important particle properties, including the optimum global motion. The results of the existing simulation show that the proposed MGSA improves the detection performance by 12% and the time performance by 1. 71 times compared to APSO. It works better.