Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints

Fast and accurate tracking control of robotic manipulators subject to state constraints and input saturation by effectively integrating planning strategies

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments.

Multi-stage trajectory tracking of robot manipulators under stochastic environments

For robot manipulators composed of Lagrange subsystems driven by direct current (DC) motors under stochastic environments, multi-stage trajectory tracking is investigated in this paper. The main challenge is how to achieve the end-effector drive of manipulators from a given initial state to a final state. First, the inverse kinematics method and the partition of the task space are adopted to tackle multi-stage trajectory planning. Second, the adaptive backstepping technique is used to design tracking controller for stochastic Lagrangian subsystems. Then, based on the state-dependent switching signal, a multi-stage switched controller is designed for trajectory tracking of robot manipulators. All signals in the close-loop error switched system are bounded in probability, and the tracking error in mean square can be made arbitrarily small enough by parameters-tuning The effectiveness of the proposed control method is illustrated by simulation results.

Sliding-mode anti-disturbance speed control of permanent magnet synchronous motor based on an advanced reaching law

In order to improve the performance of a permanent magnet synchronous motor (PMSM) speed controller, an advanced reaching law sliding mode control (ASMC) strategy is proposed in this study. The advanced sliding mode reaching law (ASMRL) introduces a power term of the system state and a checkmark function term about the sliding mode function based on the traditional constant-proportional rate reaching law(TSMRL) , and replaces the sign function with a hyperbolic tangent function. A detailed theoretical analysis of the characteristics of the ASMRL is then presented. The theoretical analysis shows that the ASMRL converges to the sliding mode surface more quickly and with less chattering than the TSMRL. In addition, a sliding mode disturbance observer (SMDO) is designed to estimate the total disturbance of the system, and the estimated disturbance is compensated to ASMC. Then the stability of the system with ASMC and the stability of the system with ASMC+SMDO is proved by Lyapunov's theorem. Finally, the proposed control strategy is validated on an experimental platform of PMSM. The experimental results show that the ASMC has a faster convergence speed, smaller chattering, better disturbance rejection performance than the traditional constant-proportional rate reaching law sliding mode control(TSMC), and better performance with the addition of SMDO.

High-order robust control and Stackelberg game-based optimization for uncertain fuzzy PMSM system with inequality constraints

There exist the uncertainties and the inequality constraints in permanent magnet synchronous motor (PMSM) system. In order to meet the safety control requirements in industrial applications, the state transformation is used to meet the inequality constraints for limiting the outputs within desired bounds. Then, fuzzy set theory, which is different from fuzzy logic, is used to describe uncertainty, and the fuzzy PMSM dynamical model is established. Based on that, a robust control with high-order term is proposed to compensate for the time-varying uncertainty. Furthermore, for improving the system performance and decreasing the control cost, the Stackelberg game is introduced into the optimization scheme design, in which the leader plays a more important role than follower. These characteristic corresponds to the influence of the two tunable control parameters on the system. Thus, the optimal parameters are obtained by the rules of Stackelberg game. Finally, experimental results show the effectiveness of the above theories.

Beyond the quadratic norm: Computationally efficient constrained nonlinear MPC using a custom cost function

A new approach to nonlinear Model Predictive Control (MPC) is discussed in this work. A custom user-defined cost function is used in place of the typically considered quadratic norm. An approximator of the cost function is applied to obtain a computationally simple procedure and linearization of two trajectories is carried out online. The predicted output trajectory of the approximator and the predicted trajectory of the manipulated variable, both over the prediction horizon, are repeatedly linearized online. It yields a simple quadratic programming task. The algorithm is implemented for a simulated neutralization benchmark modeled by a neural Wiener model. The resulting control quality is excellent, identical to that observed in the MPC scheme with nonlinear optimization. Validity of the described MPC algorithms is demonstrated when only simple box constraints are considered on the process input variable and in a more demanding case when additional soft limitations are put on the predicted output. Two structures of the approximator are compared: polynomial and neural; the advantages of the latter one are shown and stressed.

Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics

In the framework of the backstepping algorithm, this article proposes a new function approximation technique (FAT)-based compound learning control law for electrically-driven robotic manipulators with output constraint. The Fourier series expansion is adopted in the learning-based design to approximate unknown terms in the system description. The accuracy of FAT approximation is also studied by defining an identification error, which is derived from a serial-parallel identifier. Furthermore, the output constraint is taken into account by integrating the error transformation, the performance function and the dynamic surface control in a compact framework. Following this idea, new compound adaptation laws are then constructed. The proposed compound learning controller confirms that all the signals of the overall system are uniformly ultimately bounded, ensuring the tracking error within the predefined bounds during operation. Different simulation scenarios applied to a robotic manipulator with motor dynamics illustrate the capability of the control algorithm.

A novel selected force controlling method for improving robotic grinding accuracy of complex curved blade

Nonlinear time-varying contact state is a crucial factor to prevent the traditional robotic belt grinding method from precision machining of blade. In this case, a novel selected force controlling method (SFC) with consideration of regional division (RD) based on machining allowance is proposed for improving robotic grinding accuracy of complex curved blade, on basis of the self-developed adaptive impedance controller. Ideal normal grinding force at each cutter-contact (CC) point is calculated by principal curvature radius and regional allowance of blade surface. Then, the CC points with similar ideal normal grinding force are divided into one region along grinding path based on the force threshold. Furthermore, an adaptive impedance controller with neural network online compensation algorithm (AICNN) is developed, and the verification test results of grinding four profile areas of intake side, exhaust side, convex and concave, indicate that the force control accuracy with AICNN has increased by 80.33%, 50.58%, 82.65% and 69.01% than that without the controller, respectively. Based on this, the grinding experiment of typical turbine blade is conducted with SFC, and the surface profile accuracy values at the four profile areas have evidently improved by 48.79%, 35.67%, 59.54%, and 66.90% than that with conventional grinding (CG), respectively.

UDE-based task space tracking control of uncertain robot manipulator with input saturation and output constraint

This paper investigates the trajectory tracking problem of uncertain robot manipulators with input saturation and output constraints. Uncertainty and disturbance estimator (UDE) is used to tackle the model uncertainties and external disturbances. Different from most existing methods, UDE only needs the bandwidth of the unknown plant model for design, which makes it easy to be implemented. Nonlinear state-dependent function is employed to cope with output constraints and a second order auxiliary system is constructed to solve the input saturation. Finally, an UDE-based tracking controller is proposed based on the backstepping method. With the proposed control scheme, the input saturation and the output constraints are not violated, and all signals in the closed-loop system are bounded. The comparative simulation results of a two-link robot manipulator are utilized to validate the effectiveness and superiority of the proposed control method.