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The purpose of this paper is to propose a receding horizon control approach for the problem of locating unknown wireless sensor networks by using a mobile robot.

A control framework is used and consists of two levels: one is a decision level, while the other is a control level. In the decision level, a spatiotemporal probability occupancy grid method is used to give the possible positions of all nodes in sensor networks, where the posterior probability distributions of sensor nodes are estimated by capturing the transient signals. In the control level, a virtual robot is designed to move along the edge of obstacles such that the problem of obstacle avoidance can be transformed into a coordination problem of multiple robots. On the basis of the possible positions of sensor nodes and virtual robots, a receding horizon control approach is proposed to control mobile robots to locate sensor nodes, where a temporary target position method is utilized to avoid several special obstacles.

When the number of obstacles increases, the average localization errors between the actual locations and the estimated locations significantly increase.

The proposed control approach can guide the mobile robot to avoid obstacles and deal with the corresponding dynamical events so as to locate all sensor nodes for an unknown wireless network.

The purpose of this paper is to present a hybrid method of intelligent optimization algorithm and Receding Horizon Control. The method is applied to solve the problem of cooperative search of multi‐unmanned aerial vehicles (multi‐UAVs).

The intelligent optimization of Differential Evolution (DE) makes the complex problem of multi‐UAVs cooperative search a regular function optimization problem. To meet the real‐time requirement, the idea of Receding Horizon Control is applied. An Extended Search Map based on hormone information is used to describe the uncertain environment information.

Simulation results indicate effectiveness of the hybrid method in solving the problem of cooperative search for multi‐UAVs.

The paper presents an interesting hybrid method of DE and Receding Horizon Control for the problem of cooperative multi‐UAVs.

The purpose of this paper is to propose an attitude control algorithm for spacecraft with geometric constraints.

The geometric constraint is reformulated as a quadratic form when quaternion is used as attitude parameter, then the constraint is proved to be nonconvex and is further transformed to a convex one. By designing a new constraint formulation to satisfy the real constraint in the predictive horizon, the attitude control problem is reshaped to a convex planning problem which is based on receding horizon control.

The proposed algorithm is more effective in handling geometric constraints than previous research which used single step planning control.

With novel improvements to current methods for steering spacecraft from one attitude to another with geometric constraints, great attitude maneuver path can be achieved to protect instruments and meanwhile satisfy mission requirements.

The attitude control algorithm in this paper is designed especially for the satisfaction of geometric constraints in the process of attitude maneuver of spacecraft. By the application of this algorithm, the security of certain optical instruments, which is critical in an autonomous system, can be further assured.

The purpose of this paper is to present research in the area of control method for the man‐machine systems with brain machine interface (BMI). Concrete target system is, for instance, a car cruising system and so on.

The improved receding horizon control (RHC) method for the sampled‐data systems and the adaptive digital‐to‐analog (DA) converter which has the way to switch the sampling functions according to the system status are used. The feature selection method based on the kernel support vector machines with the backward stepwise selection for the BMI signals are also used.

This paper proposes the new improved RHC method with the adaptive DA converter for the application of the BMI‐based systems. The proposed method is illustrated as useful and effective method for the systems to which switch of control laws is indispensable by the simulations.

Although the proposed method is effective for the BMI‐based systems with switching of control laws, the faster algorithm for RHC will be need to apply to the man‐machine systems with the BMI in practical use.

The basic concept or framework of the proposed method can be used for the real man‐machine systems with the BMI, for examples, car crusing systems, wheel‐chaired systems and so on.

The paper contributes to the development of the new effective control method for the BMI‐based man‐machine systems.

The purpose of this paper is to propose a novel concept of model prediction control (MPC) parameter optimization method, which is based on pigeon-inspired optimization (PIO) algorithm, with the objective of optimizing the unmanned air vehicles (UAVs) controller design progress.

The PIO algorithm is proposed for parameter optimization in MPC, which provides a new method to get the optimal parameter.

The PIO algorithm is a new swarm optimization method, which consists of two operators, so it can be better adapted for the optimal problems. The comparative consequences results with the particle swarm optimization (PSO) demonstrate the effectiveness of the PIO algorithm, and the superiority for global search is also verified in various cases.

PIO algorithm can be easily applied to practice and help the parameter optimization of the MPC.

In this paper, we first present the concept of using the PIO algorithm for parameter optimization in MPC so as to achieve the global best optimization. By using the PIO algorithm, the choice of the parameter could be easier and more effective. The authors also applied the algorithm to the designing of the MPC controller to optimize the response of the pitch rate of UAV.

The purpose of this paper is to present a novel approach for trajectory tracking of UAVS. Research on unmanned aircraft is constantly improving the autonomous flight capabilities of these vehicles to provide performance needed to use them in even more complex tasks. The UAV path planner (PP) plans the best path to perform the mission. This is a waypoint sequence that is uploaded on the flight management system providing reference to the aircraft guidance, navigation and control system (GNCS). The UAV GNCS converts the waypoint sequence in guidance references for the flight control system (FCS) that, in turn, generates the command sequence needed to track the optimum path.

A new guidance system (GS) is presented in this paper, based on the graph search algorithm kinematic A* (KA*). The GS is linked to a nonlinear model predictive control (NMPC) system that tracks the reference path, solving online (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with genetic algorithm (GA). The GA finds the command sequence that minimizes the tracking error with respect to the reference path, driving the aircraft toward the desired trajectory. The same approach is also used to demonstrate the ability of the guidance laws to avoid the collision with static and dynamic obstacles.

The tracking system proposed reflects the merits of NMPC, successfully accomplishing the task. As a matter of fact, good tracking performance is evidenced, and effective control actions provide smooth and safe paths, both in nominal and off-nominal conditions.

The GNCS presented in this paper reflects merits of the algorithms implemented in the GS and FCS. As a matter of fact, these two units work efficiently together providing fast and effective control to avoid obstacles in flight and go back to the desired path. KA* was developed from graph search algorithms. Maintaining their simplicity, but improving their search logics, it represents an interesting solution for online replanning. The results show that the GS uploads the collision avoidance path continuously during flight, and it obtains straightforward the reference variables for the FCS, thanks to the KA* model.

The purpose of this paper is to concern the design of a robust model predictive controller for distributed networked systems with transmission delays.

The overall system is composed of a number of interconnected nonlinear subsystems with time-varying transmission delays. A distributed networked system with transmission delays is modeled as a nonlinear system with a time-varying delay. Time delays appear in distributed systems due to the information transmission in the communication network or transport of material between the sub-plants. In real applications, the states may not be available directly and it could be a challenge to address the control problem in interconnected systems using a centralized architecture because of the constraints on the computational capabilities and the communication bandwidth. The controller design is characterized as an optimization problem of a “worst-case” objective function over an infinite moving horizon.

The aim is to propose control synthesis approach that depends on nonlinearity and time varying delay characteristics. The MPC problem is represented in a time varying delayed state feedback structure. Then the synthesis sufficient condition is provided in the form of a linear matrix inequality (LMI) optimization and is solved online at each time instant. In the rest, an LMI-based decentralized observer-based robust model predictive control strategy is proposed.

The authors develop RMPC strategies for a class of distributed networked systems with transmission delays using LMI-Based technique. To evaluate the applicability of the developed approach, the control design of a networked chemical reactor plant with two sub-plants is studied. The simulation results show the effectiveness of the proposed method.

The purpose of this paper is to present the autopilot design for the missile under various disturbances.

In this study, model predictive control (MPC) method has been used for autopilot design for each axis. The aim of autopilot is that to keep the roll angle value around the zero degree and to track pitch/yaw acceleration commands. This three-axes control methodology also takes into consideration the interaction between pitch, yaw and roll motions.

The purpose of using MPC method for three-axes of the autopilot is to decrease the control effort and to make the close-loop system insensitive against modeling uncertainties and stochastic effects.

This study shows that the missile is able to reach to the desired target with good robustness, low control effort and little miss-distance under disturbances such as aerodynamic uncertainties, thrust misalignment and gust affect by using this alternative control method.

The purpose of this paper is to introduce a new danger‐aware Operational Flight Program (OFP) for the unmanned helicopter's auto‐navigation based on the well‐known time‐triggered message‐triggered object (TMO) model.

In this design with the TMO, the danger‐awareness means two things. First, an unmanned helicopter maneuvers on safe altitudes to avoid buildings or mountains when navigating to the target position. It is assumed that minimum safe altitudes are given on evenly spaced grids and on the center points of every four adjacent grids. A three‐dimensional (3D) path‐finding algorithm using this safe‐altitude information is proposed. Second, a helicopter automatically avoids a zone with very high temperature caused by a fire.

Since the auto‐flight control system requires componentized real‐time processing of sensors and controllers, the TMO model that has periodic and sporadic threads as members, has been used in designing the OFP. It has been found that using the TMO scheme is a way to construct a very flexible, well‐componentized and timeliness‐guaranteed OFP.

As the RTOS, RT‐eCos has been used. It was developed a few years ago based on the eCos3.0 to support the real‐time thread model of the TMO scheme. To verify this navigation system, a hardware‐in‐the‐loop simulation (HILS) system also has been developed.

Designing an OFP by using the real‐time object model TMO and the proposed 3D safe path finding algorithm is a whole new effective deadline‐based approach. And the developed OFP can be used intensively in the phase of disaster response and recovery.

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control (MPC) have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters.

The proposed neuro‐fuzzy scheme is able to precisely learn the control relation between input‐output training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation (RAMA) Laboratory, it was found that the neuro‐fuzzy controller was able to learn fuzzy logic rules and parameters accurately.

The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated.

The neuro‐fuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about input‐output heuristic relation is not available.

The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.