Nonlinear Model Predictive Tracking Control Research Articles

Continuous-Time Nonlinear Model Predictive Tracking Control with Input Constraints Using Feedback Linearization

This paper presents a tracking control scheme for nonlinear systems with input constraints by combining the continuous-time model predictive control and the feedback linearization. Although there are some similar combinations for nonlinear systems presented in literature, their formulations are complex and massive computations are unavoidable. This study aims to simplify the formulations and reduce the computational loads by imposing the Laguerre functions to approximate the control signals. Since the Laguerre functions have the property of orthogonality, the tracking control problem, by applying the combination, leads to a constrained quadratic optimization problem in terms of the coefficients associated with the Laguerre functions, where the input constraints are converted so as to be state-dependent, based on feedback linearization. The Hildreth’s quadratic programming algorithm is used to solve the optimization problem, so as to determine the coefficients. Moreover, this study also summarizes some common linearization schemes and shows their pros and cons. Furthermore, the proposed approach is applied to two illustrative examples, and the control performances are compared with those by linear control strategies combined with those linearization schemes.

Approximate Quadratic Programming Algorithm for Nonlinear Model Predictive Tracking Control of a Wheeled Mobile Robot

Nonlinear model predictive control (NMPC) has proved its competency in controlling constrained nonlinear processes. Although NMPC can achieve exemplary tracking performance, the related computation effort as well as guaranteeing tracking convergence are its main drawbacks. Indeed, constrained NMPC is a nonlinear and nonconvex optimization problem where it is difficult to find a feasible solution within a reasonable time. Motivated by these difficulties, this paper proposes a procedure, using Euler approximation, to transform the nonlinear optimization problem of NMPC into a constrained quadratic optimization problem. The proposed tracking controller is applied to the autonomous navigation problem of a wheeled mobile robot (WMR) in a constrained space. Under certain assumptions, we prove the closed-loop system stability and the boundedness of the tracking error. Further, we show the recursive feasibility of the solution. Simulations are performed, first to determine the adequate control parameters, and secondly to show the effectiveness of the proposed algorithm, while its real-time implementation is experimentally verified using a differential drive mobile robot.

Neural-network-based nonlinear model predictive tracking control of a pneumatic muscle actuator-driven exoskeleton

Pneumatic muscle actuators &#x0028 PMAs &#x0029 are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonlinearities, hysteresis, and uncertainties, etc., complex mechanisms are rarely involved in the study of PMA-driven robotic systems. In this paper, we use nonlinear model predictive control &#x0028 NMPC &#x0029 and an extension of the echo state network called an echo state Gaussian process &#x0028 ESGP &#x0029 to design a tracking controller for a PMA-driven lower limb exoskeleton. The dynamics of the system include the PMA actuation and mechanism of the leg orthoses; thus, the system is represented by two nonlinear uncertain subsystems. To facilitate the design of the controller, joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP. A gradient descent algorithm is employed to solve the optimization problem and generate the control signal. The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics. Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

A nonlinear model predictive tracking control strategy for modular high-temperature gas-cooled reactors

Modular high-temperature gas-cooled nuclear reactor (MHTGR) has attracted comprehensive attention for its reliable inherent safety, and an effective control strategy is needed to control the output power of the MHTGRs at the desired level. Based on model predictive control (MPC) strategy, this paper aims to develop a nonlinear power-level tracking controller for the MHTGRs. By introducing Takagi-Sugeno (TS) fuzzy system for nonlinear modeling, the nonlinear MHTGR model is represented by TS models. Based on the TS models, a nonlinear MPC (NMPC) controller is developed. Moreover, system constraints are considered, and quadratic programming (QP) is introduced for system optimization under the constraints. In addition, the system stability is analyzed based on Pole-Zero map and Nyquist diagram. To evaluate the performance of the proposed NMPC strategy, a Proportional-Integral-Derivative (PID) control strategy and a fuzzy adaptive Proportional-Integral-Derivative (FPID) control strategy are presented for comparison. The effectiveness and the advantages of the proposed NMPC strategy are demonstrated by simulation results.

A soft robotics nonlinear hybrid position/force control for tendon driven catheters

Minimally invasive steerable catheters, commonly implemented in cardiac ablation, are currently operated by interventionalists exposing them to X-ray radiation and requiring the dexterity for accurate steering. To conduct robot-assisted cardiac ablation, highly accurate stable control platform for precise force/position control on the moving tissue is required. This paper introduces hybrid force/position control strategy to apply a constant force to the cardiac tissue while tracking the desired trajectory. The position controller is based on a nonlinear model predictive tracking control satisfying the input constraints. Cosserat rod theory is incorporated for the distal shaft modeling of tendon-driven catheters, and the model is reformulated for controller design and stability proof. Lyapunov-based stability analysis is conducted. To apply the controller, the force-displacement mapping of the cardiac tissue is obtained through ex vivo experimental tests. The performance of the controller is evaluated, and the catheter is capable of regulating the force with the RMSE of 4.9 mN and tracking the position with the RMSE of 0.89 mm. The promising results verify the potential of the application of the introduced approach in real applications including in vitro and clinical cardiac ablation.

Experimental verification of nonlinear model predictive tracking control for six-wheeled unmanned ground vehicles

This paper presents a nonlinear model predictive tracking control scheme for a six-wheeled nonholonomic unmanned ground vehicles (UGVs). It is employed as a high-level guidance control with kinematic approximation for UGV motion. A nonlinear model predictive control algorithm solves trajectory planning and optimal control problems by sequentially solving an online numerical optimization problem. The optimal control inputs for the UGV are obtained with a gradient descent optimization algorithm considering constraints of UGV motion as well as its input constraints. The characteristics of the proposed controller in terms of tracking performance and collision avoidance were investigated. The real-time performance of the proposed numerical optimization algorithm was verified with an experimental six-wheeled UGV platform in indoor and outdoor environments.

A distributed control framework for motion coordination of teams of autonomous agricultural vehicles

A distributed control framework intended to coordinate the motions of teams of autonomous agricultural vehicles operating in proximity is presented; master–slave and peer-to-peer operation modes are supported. Each vehicle has a nonlinear model predictive tracking controller, which keeps it as close as possible to the path demanded by the task, and coordinates and avoids collisions with nearby vehicles. To do this, it receives the motion trajectories of all other vehicles in its vicinity that may interfere with its own projected motion via a wireless network, and incorporates these trajectories in the computation of its own optimal control action. Each controller is supervised by a higher-level task controller that determines a limited set of the controller's parameters. Simulation experiments have shown that the minimisation of the tracking error along a finite horizon enabled the controller to track paths containing sharp turns, by applying appropriate steering well in advance of the turn. It has also been shown that the variation of specific predictive controller parameters results in a wide range of behaviours; i.e., vehicles can move in both operating modes, coordinate with nearby vehicles by altering their velocity profiles or the shapes of their paths, and avoid collisions.

Switched and Symmetric Pursuit/Evasion Games Using Online Model Predictive Control With Application to Autonomous Aircraft

This paper describes a supervisory controller for pursuit and evasion of two fixed-wing autonomous aircraft. Novel contributions of the work include the real-time use of model-predictive control, specifically nonlinear model predictive tracking control, for predictions of the vehicle under control, as well as predictions for the adversarial aircraft. In addition to this inclusion, the evasive controller is a hybrid system, providing switching criteria to change modes to become a pursuer based on the current and future state of the vehicle under control, and that of the adversarial aircraft. Results of the controller for equally matched platforms in actual flight tests against a US Air Force trained F-15 test pilot are given. Extensive simulation analysis of the symmetric games is provided, including regressive analysis based on initial conditions of height advantage, and relative velocity vectors, and in particular the effect of allowing the evading aircraft to switch modes between “evader” and “pursuer” during the game.