predictive control

This article aims to control the group flight of unmanned helicopters. After presenting the dynamic equations using the Newton-Euler method, the dynamic inversion control has been applied to control the position and attitude of helicopters. generalized predictive control was also used to control the position and attitude of the helicopter. And the results of the simulation of these two controllers were compared, which showed the better performance of the predictive control. Therefore, in the design of the formation control, the independent control of the helicopters was done using the predictive control method. To control the formation, the method based on the leader-follower model has been used, An integral sliding mode controller is also used to control the dynamics of the formation error. The results of the simulation showed the success of the tracking by increasing the number of helicopters and the linear and spiral paths in the control commands.

Fuel consumption in the transportation has been rising in recent decades and consequently,, it has increased pollution and environmental footprint. To address this worldwide problem, the aim of this study is to design a high-level controller with the capability to be implemented to a hybrid electric car in real time, while optimizing fuel consumption and drivability. To achieve this goal, nonlinear model predictive control is utilized based on the longitudinal dynamics of the powertrain. First, the controller is designed for a specific reference. In the next step, the controller is set for a wide range of reference inputs, through tuning the weighting parameters of the optimal control problem. It ultimately shows that all control targets are satisfied. Also applying the proposed energy management system to the powertrain model leads to considerable fuel consumption reduction. Meanwhile, the controller has the capability of being implemented in real time while considering the complex dynamics of the system.

This paper presents a model predictive control algorithm for path following of an autonomous vehicle in a curvilinear path. To define vehicles’, motion a nonlinear bicycle model of vehicle with 6 degrees of freedom is employed. Some kinematic constraints imposed to the state and control variables, as well as constraints on vehicle acceleration and braking deceleration for desired path following of the vehicle. Then, an appropriate prediction horizon and a quadratic optimization model is formulated and employed for minimizing the path following error and control variable’s variation of the vehicle. In the optimization computation process suitable weight coefficient for all state and control variables are considered. Finally, a simulation study is performed to analyze the capability and efficiency of the presented algorithm for following the desired path. The simulation results show that the presented path following algorithm is acceptably able to follow the desired path with acceptable accuracy and desirable speed.

One of the main topics in the field of robotics is the modeling and control of the mobile robots intrajectory tracking problem. In this paper, the kinematic and dynamic models of a manipulator connetcted by revolute-prismatic joints and installed in a non-holonomic wheeled mobile platform are first derived by applying the recursive Gibbs-Appell method. Indeed, by employing this dynamic methodology, one gets rid of the difficulties of Lagrange Multipliers that originate from non-holonomic constraints. Then, a nonlinear predictive approach is applied to design the kinematic and dynamic control laws to generate trajectory tracking control commands of the non-holonomic robot. In this method, the nonlinear responses of the mobile robot are predicted using Taylor series. The optimal control laws are analytically developed by minimizing the difference between the predicted and the desired responses of the system outputs. The multivariable kinematic controller specifies the desired angular and linear velocities of the mobile base and the manipulator links. The obtained control inputs are then used as the desired values to be tracked by the dynamic controller. Finally, the results of numerical simulations are presented to emphasize the ability the proposed method in the mathematical modeling and simultaneous trajectory tracking control of the mobile base and end-effector of such complex robotic systems.

In this paper, a new constraint predictive PID controller is proposed and simulated for a flight vehicle with too many operating points. In this approach, the PID and the incremental predictive control methods are combined in order to use the benefits and resolve the disadvantages of the both methods. The constrained optimal signal of the new method is calculated by a simple algorithm without need to generate large matrices. Simplicity of the presented controller with small dimensions is the reason for the implementation capability of the method for flight vehicle control system with many operating points. Since the generalized incremental predictive control has high robustness in the presence of the unmodeled uncertainties, the presented predictive based controller covers many operating points. The designed method is implemented for three channels of a flight vehicle and then, successfully tested in the hardware in the loop lab. Simulation results show high robust performance and real time implementation capability.

One of the major challenges in the use of predictive control systems, particularly in the fast system is computational burden. To accelerate the process and reduce the amount of computation and optimization of functions, we use the Laguerre functions. The purpose of this paper is to provide a linear predictive control method using Laguerre functions with constraints on signal and the rate control signal for a helicopter flight control system design in the status of hover maneuver. By tuning parameters of predictive control in nonlinear model, the performance of the proposed method evaluate by two index entries, maneuverability and coupling effect based on standards for design a helicopter flight control system. Multiplicative uncertainty in state-space model has been described due to uncertainty on system modeling to study the robustness of the proposed method. The simulation results of this method have been compared with the results of generalized predictive control method. Simulation results show that in addition of public benefits in generalized predictive control, Laguerre predictive control has the advantage of reducing the time and burden of computation and it is clear to see that the control law we have obtained for the hovering flight condition achieve the top level performance in two categories under examination.

Nowadays, Hybrid Electric Vehicles (HEVs) are introduced in order to reduce fuel consumption and emissions. The issue that is very important in HEVs, is how to split power between main components of powertrain. Best energy management can be obtained when all future conditions are available. With the advancement of the intelligent systems, access to the road conditions, traffic and other online information has been provided up to the limited prediction horizon. In this paper, a combination of predictive control and Dynamic Programming methods have been used for obtaining online sub-optimal trajectory. Change in the state of traffic in the path has great effect on reduction of fuel consumption. Therefore, According to the state of traffic, a fuzzy logic system is proposed for the online estimating of the vehicle speed. Unlike many energy management methods that use historical data, the proposed strategy leads to reducing the dependence of the controller on the drive cycle. The simulation is implemented on a Plug-in Hybrid Electric Vehicle with parallel structure. The proposed method is compared with Dynamic Programming and instantaneous optimization. Evaluation of results shows that the proposed method, while simplicity and avoiding complicated mathematical relationships, in addition to fuel consumption reduction compared with instantaneous optimization, can manage SOC, properly. The results of this method are close to the global optimal solution of Dynamic Programming.

This paper talked about spacecraft formation flying control. Leader-flower structure is used in formation flying. A non-linear PID controller is designed based on predictive control. The formation relative equation is obtained from nonlinear Hill equation. First, the frequency control is achieved with the using of predictive control algorithm. In control frequency, disturbances have been replaced from disturbance observer. Equations are rewritten in the form of PID gains. Stability of the closed-loop system is proven by closed-loop error dynamics. Nonlinear PID controller performance in the pursuit of desired arrangement has been tested in simulations. Also effects of various factors on the quality of controller results are studied. It is shown that choosing predictive horizon time and disturbance observer gains have the most effect on system response. It is shown that if predictive time increase the settling time increase and the control effort decrease. if disturbance observer gain increase from a limit, it has no effect on settling time but control effort increase. As shown in simulation, the tracking response show the controller method ability. the simulation show the ability of this nonlinear control method in tracking.

Due to different flight conditions, variation in mass-inertia characteristics, aerodynamic coefficients, disturbance and etc, dynamic equations are unknown and uncertain. therefore use of adaptive or robust control method for designing of autopilot which is related to unknown parameters variation in missile dynamic, autopilot parameters are variant,It cause this method to be necessary. In this paper first of all a predictive control is designed based on nonlinear model (NMPC) and tailor expansion and then by help of neural network in approximation of unknown functions in missile dynamic an model-reference predictive adaptive control is proposed for STT missile.