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Among the multitude of modern control methods, model predictive control (MPC) is one of the most successful [1] – [4] . As noted in “Summary,” this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), and nonlinear control methods, such as feedback linearization and sliding mode control. Although MPC is computationally intensive, it is more broadly applicable than Hamilton–Jacobi–Bellman-based control and more suitable for feedback control than the minimum principle. In many cases, the constrained optimization problem for receding-horizon optimization is convex, which facilitates computational efficiency [5] .

This paper focuses on the control of a cooperative system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) manipulating an object. The two units are subject to input saturations and collaborate to move the object to a desired pose characterized by its position and inclination. The dynamics are derived using the Euler–Lagrange method. A stabilizing control law is proposed where the UGV is tasked to deploy the object to a certain position whereas the UAV adjusts its inclination. In particular, a proportional–derivative control law is proposed for the UGV, and a cascade control is used for the UAV, where the inner loop controls the attitude of the UAV and the outer loop stabilizes the inclination of the object. The stability of the points of equilibrium is proven using small gain arguments. To ensure constraints satisfaction at all times, a reference governor unit is added to this stabilizing control law. Simulations and experimental results are provided to validate the effectiveness of the proposed control scheme.

In this paper, sampled-data output-feedback model predictive control (MPC) with online identification is used to control nonlinear continuous-time plants. Using a linear model structure for system identification, the paper provides a numerical investigation of the domain of attraction of MPC. Three examples are considered within the context of sampled-data control, namely, the second-order Duffing oscillator with a single measurement, the fourth-order ball-and-beam system with two measurements, and the sixth-order circular restricted three-body problem with three position measurements.

With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that energy consumption is minimized. To handle the strong uncertainties in the traffic model in the future, a constrained controller is generally employed in the existing researches. However, its expensive computational feature largely prevents its commercialization. This paper addresses computational burden of the constrained controller by proposing a computationally tractable model prediction control (MPC) for real-time implementation in autonomous electric vehicles. We present several remedies to achieve a computationally manageable constrained control, and analyze its real-time computation feasibility and effectiveness in various driving conditions. In particular, both warmstarting and move-blocking methods could relax the computations significantly. Through the validations, we confirm the effectiveness of the proposed approach while maintaining good performance compared to other alternative schemes.

This paper presents recursive-least-squares-based model predictive control (RLSMPC). RLSMPC uses only output feedback, and thus does not require full-state measurements. Online learning is performed through concurrent system identification, and thus no a priori model is needed. RLSMPC employs separate RLS algorithms for identification, offset determination, and control. Variable-rate forgetting is used to facilitate system identification and offset estimation.

This paper focuses on a passivity-based distributed reference governor (RG) applied to a pre-stabilized mobile robotic network. The novelty of this paper lies in the method used to solve the RG problem, where a passivity-based distributed optimization scheme is proposed. In particular, the gradient descent method minimizes the global objective function while the dual ascent method maximizes the Hamiltonian. To make the agents converge to the agreed optimal solution, a proportional-integral consensus estimator is used. This paper proves the convergence of the state estimates of the RG to the optimal solution through passivity arguments, considering the physical system static. Then, the effectiveness of the scheme considering the dynamics of the physical system is demonstrated through simulations and experiments.

This paper presents a general control framework for the constrained control of multibody systems actuated by vectorized thrusters . A cascade control scheme augmented with a constraint-enforcement unit is proposed to stabilize the system while ensuring constraint satisfaction at all times. The cascade controller consists of an inner loop and an outer loop that are interconnected by a suitable mapping. The inner loop is tasked to control the attitude of the vectorized thrusters. The outer loop is designed to steer the task configuration of the system to a desired pose, while providing to the inner loop the desired attitude through a mapping. To prove the stability of the interconnected system, input-to-state stability (ISS) and small gain arguments are used. All stability properties are derived in the absence of constraints and are shown to be local. Hence, the control scheme is augmented with a Reference Governor (RG) to enforce constraints at all times. Simulations on an unmanned tiltrotor docked to a stationary platform for a refueling operation are carried out to demonstrate the effectiveness of the proposed control framework.

This paper presents a strategy for the position control of mobile robotic networks subject to geometric con- straints. The main idea of this paper is to show that passivity arguments can be used to ensure both stability and constraint satisfaction. It is shown that it is possible to pre-stabilize the system dynamics with a proportional-integral consensus estimator and a proportional feedback loop which make use of passivity arguments. Then, the very same passivity arguments are used to ensure constraint satisfaction, using a set-invariance based Reference Governor. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed solution.

This paper focuses on the attitude constrained control of a rigid body in three dimensions, and proposes the Explicit Reference Governor (ERG) as a solution to handle constraints on SO (3). Two types of constraints are considered: (i) input torque constraints, and (ii) geometric conic pointing constraints. The objective of this paper is to design a control law that is able to steer the attitude of the rigid body to the desired attitude reference while enforcing constraints satisfaction at all times. To do so, the first step is to design a controller that makes the desired attitude asymptotically stable when constraints (i) and (ii) are neglected. Then, the proposed control scheme is augmented with a suitably designed ERG to add constraint-handling capabilities to the scheme. All the proposed solutions are defined in terms of elements on SO (3) and do not make use of any parameterizations such as e.g., quaternions and Euler angles. Finally, numerical simulations are carried out to show the effectiveness of the proposed control scheme.

This paper focuses on the constrained control of UAVs in geofencing applications. Although geofence systems are becoming more attractive as a research topic, most works are focusing on defining the boundaries of the admissible geographical region without addressing the control issues and boundary-handling problems. In this paper, we propose a constrained control scheme to steer an UAV to the desired position while ensuring constraints satisfaction at all times. To do so, we make use of the recently introduced Explicit Reference Governor framework. The proposed scheme is validated through extensive experimental studies carried out in a laboratory environment.

This paper focuses on the control of a system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which cooperate to manipulate an object. The two units are subject to actuator saturations and cooperate to move the object to a desired pose, characterized by its position and inclination. The paper proposes a control strategy where the ground vehicle is tasked to deploy the object to a certain position, whereas the aerial vehicle adjusts its inclination. The ground vehicle is governed by a saturated proportional-derivative control law. The aerial vehicle is regulated by means of a cascade control specifically designed for this problem that is able to exploit the mechanical interconnection. The stability of the overall system is proved through Input-to-State Stability and Small Gain theorem arguments. To solve the problem of constraints satisfaction, a nonlinear Reference Governor scheme is implemented. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.