Showing papers by "Ye-Hwa Chen published in 2021"

Sensorless Fault-Tolerant Control With Phase Delay Compensation for Aerospace FTPMSM Drives With Phase Open-Circuit and Short-Circuit Faults

Abstract: To enhance the reliability of fault-tolerant permanent-magnet synchronous motor (FTPMSM) drives, a new sensorless control based on the robust observer, nonorthogonal phase-locked loop (PLL), and variable phase delay compensation is proposed, which can guarantee the medium- and high-speed sensorless control performance for the FTPMSM even in the phase open-circuit and short-circuit fault conditions. A robust observer is proposed to achieve any two healthy phase back electromotive force (EMF) estimation, regardless of the parameter variation, external disturbance, and the phase fault. The nonorthogonal PLL is presented to extract the information of the rotor position from the observed two nonorthogonal phase back-EMFs. To enhance the estimation accuracy of the rotor position as the speed changes, the variable cut-off frequency low-pass filter (VLPF) is proposed to eliminate the high-frequency noises of the observed phase back-EMFs, while a phase delay compensation is presented to compensate for the estimation deviation caused by the VLPF. The resulting sensorless FTPMSM system has excellent speed control performance both in normal and fault conditions, which is also demonstrated by a six-phase FTPMSM system experimental platform.

Optimal Longitudinal Control for Vehicular Platoon Systems: Adaptiveness, Determinacy, and Fuzzy

Abstract: This article addresses the control design problem for a fuzzy vehicular platoon system consisting of one leading vehicle and $N$ following vehicles. Due to external disturbances, uncertainty exists in the platoon system and is usually nonlinear and possibly fast time varying. In order to deal with uncertainty, fuzzy theory is employed to describe the platoon system, thereby the so-called fuzzy vehicular platoon system. Based on the fuzzy property of uncertainty, a type of adaptive law is proposed. Since the original state of the system is one-side bounded when collision avoidance is taken into account, a state transformation is made, by which a new global state is obtained. Then, the swarm intelligence is embedded into the platoon system via a function, which mimics the swarm behavior. For the control design, we focus on adaptiveness and optimization. A switching-type adaptive robust control is proposed. The control is not IF–THEN rule based. We further explore the problem of control parameter optimization. A performance index consisting of transient control cost and average control cost is proposed. Aiming at minimizing the performance index, the optimal problem is formulated. The solution to the optimal problem (i.e., optimal control parameter) exists and is unique. The control with optimal parameter is called optimal control, which guarantees both deterministic performance (uniform boundedness, uniform ultimate boundedness, and collision avoidance) and fuzzy performance of the platoon system.

Deterministic Adaptive Robust Control With a Novel Optimal Gain Design Approach for a Fuzzy 2-DOF Lower Limb Exoskeleton Robot System

Abstract: To enhance the lower limbs’ rehabilitation training of stroke patients, in this article, a deterministic adaptive robust control with control gain parameters optimized by a novel cooperative game theory is proposed for the two-degree-of-freedom (DOF) lower limb exoskeleton robot system (LLERs) with uncertainties and external disturbances. On the one hand, the deterministic adaptive robust control put forward will guarantee the uniform boundedness and uniform ultimate boundedness of gait constraint deviation and parameter estimation error. On the other hand, for uncertainties and external disturbance (possibly fast time-varying), which will arise in the two-DOF LLERs inevitably, we suppose that these uncertainties and disturbances are bounded, and their bounds are lying within the fuzzy sets, which can be characterized by membership functions; with such descriptions and control performance analysis, a novel cooperative game with two players participated will be formulated to seek the Pareto optimal solutions for the control gains of deterministic adaptive robust control proposed, and furthermore, the existence of optimal solutions is also shown by invoking a numerical technique. Eventually, the simulation results presented have verified the effectiveness of our proposed methodology on improving rehabilitation training of lower limbs.

Possibility-Based Robust Control for Fuzzy Mechanical Systems

Abstract: This study proposes a new robust control design framework for uncertain mechanical systems, which may be fullyactuated or underactuated. The uncertainty is (possibly fast) time-varying, lies in prescribed fuzzy sets (hence fuzzy mechanical systems), and may be unbounded. The control goal is formulated as servo constraints (hence constraint-following control), which may be holonomic or nonholonomic. We introduce the possibility theory into the Lyapunov stability analysis (LSA), proposing possibilitybased Lyapunov stability analysis (PBLSA), which allows a maximum failure possibility (generally small) prescribed by designers. It can be viewed as a generalization of the conventional LSA, and the resultant performance is interpreted in the context of possibility. By the PBLSA, a class of robust constraint-following controls that is not IF-THEN heuristic rules-based is proposed, which renders approximate constraint-following for the system performance with a prescribed maximal failure possibility. Optimal design of a control parameter considering both system performance and control cost is investigated. The benefits of the proposed design framework are discussed and simulations on two applications are given for demonstrations.

Regulating Constraint-Following Bound for Fuzzy Mechanical Systems: Indirect Robust Control and Fuzzy Optimal Design.

Abstract: This article proposes an optimal indirect approach of constraint-following control for fuzzy mechanical systems. The system contains (possibly fast) time-varying uncertainty that lies in a fuzzy set. It aims at an optimal controller for the system to render bounded constraint-following error such that it can stay within a predetermined bound at all time and be sufficiently small eventually. First, for deterministic performance, the original system is transformed into a constructed system. A deterministic (not the usual if-then rules-based) robust control is then designed for the constructed system to render it to be uniformly bounded and uniformly ultimately bounded, regardless of the uncertainty. Second, for optimal performance, a performance index, including the average fuzzy system performance and control effort, is proposed based on the fuzzy information. An optimal design problem associated with the control gain is then formulated and solved by minimizing the performance index. Finally, it is proved when the constructed system renders uniform boundedness and uniform ultimate boundedness, the original system achieves the desired performance of bounded constraint following.

Cooperative Game Approach to Robust Control Design for Fuzzy Dynamical Systems.

Abstract: There is uncertainty in the system, and we consider that uncertainty is (possibly fast) time varying, but with definite bound. Fuzzy set theory is used to describe the inexact boundary and then the problem of robust control of uncertain dynamical systems is studied. Based on two adjustable design parameters, a robust control method for general mechanical systems is proposed. The control is deterministic, not the conventional IF-THEN rule based. By using the Lyapunov minimax approach, it is proved that the proposed control can guarantee system performance to be uniformly bounded and uniformly ultimately bounded. In order to find the optimal solution in the prescribed range, a two-player cooperative game is used. To reduce costs while ensuring control performance, two performance indices are developed, each of which is controlled by an adjustable parameter (i.e., player). Both necessary and sufficient conditions for Pareto-optimality are established. Using these conditions, the Pareto-optimal solution can be obtained. The effectiveness of the control design is demonstrated by the simulation of the two-body pendulum.

A hierarchical constraint approach for dynamic modeling and trajectory tracking control of a mobile robot

Abstract: The dynamic modeling and trajectory tracking control of a mobile robot is handled by a hierarchical constraint approach in this study. When the wheeled mobile robot with complex generalized coordin...

Robust resource allocation strategy for technology innovation ecosystems: state and control constraints

Abstract: This paper considers a robust resource allocation strategy design problem in technology innovation ecosystem. The purpose is for both healthy competition and symbiosis between populations. We formulated this as a control design problem. There are three salient features of this problem. First, the control, since it means the resource and should always be positive, is constrained. Second, the state, since it means the population and should always be positive, is constrained. Third, the system, since it represents the interactions between the societal biomass and the resources, is highly uncertain. We endeavor to propose two separate transformations on the state and the control to convert the system to an equivalent and unconstrained system. In a sense, we embed the constraints into the (nonlinear) intrinsic system structure. Following this, the control design has to address two issues. First, the control is to render desirable performance of this (constraint-embedded) system regardless of the uncertainty. Second, the performance of the original system, which is the primary concern, should be equally within the threshold. This paper should be the first effort that addresses the resource allocation strategy of technology innovation ecosystem from the control perspective.

Contact constraints-based dynamic manipulation control of the multi-fingered hand robot: a force sensorless approach

Abstract: A novel analytical modeling and dynamic manipulation control method of the multi-fingered hand robot has been proposed in the paper. Based on the contact constraints, the explicit dynamics modeling of the hand robot manipulating an object is hierarchically established by Udwadia–Kalaba equation with no auxiliary variable (e.g., Lagrange multipliers or quasi-generalized variables). Through the second order of the contact constraints, the grasping forces of the hand robot in the manipulation work space are derived explicitly and decoupled with the control torques of the finger joints. Consider the hand robot and the object as an entire system in the control design. Motivated by Udwadia’s work, the manipulation task of the grasped object is novelly used to formulate a set of servo constraints. In virtue of following the servo constraints, the hand robot can manipulate the object to accomplish the desired task. With the formulated contact forces model, a model-based dynamic control method is proposed for the hand robot to handle the object, which does not depend on the force feedback from the fingertips sensors (force sensorless). The system performance under the proposed control can be guaranteed by the theoretical proofs and demonstrated by the simulation of a three-fingered hand robot in the three-dimensional work space.

Guaranteeing performance for uncertain nonlinear systems with bounded state constraint and mismatching condition

Abstract: The control design problem for the uncertain nonlinear system with bounded state constraint and mismatching condition is considered in this paper. The uncertainty in the system, which may ...

Closed-Form Representations of Friction

Abstract: The current developed friction models are basically based on the assumption of the normal forces exerted between the contact surfaces known in advance, less work has been done for closed-form (i.e., analytic) modeling in complex mechanical systems where the normal forces vary greatly over time. In this paper, the closed-form representations of friction forces in mechanical systems are newly derived in a way of dynamics. The Udwadia-Kalaba equation is first used to calculate the normal force exerted by the contact surface in mechanical system. The friction force is then calculated via existing friction models. Such closed-form expressions of friction forces contain both the magnitude and direction at any instant of time, even as the normal force is nonconstant. The novel representations offer an effective way for analytically expressing friction force in dynamical systems, which makes it possible for accurate simulation and control design of dynamical systems with non-negligible friction forces.

A Leader–Follower Sequential Game Approach to Optimizing Parameters for Intelligent Vehicle Formation Control

Abstract: A vehicle formation control algorithm is presented to deal with the ineluctable uncertainty and guarantee the vehicle platoon Lyapunov stability (uniform boundedness, uniform ultimate boundedness, and string stability). The uncertainty here is nonlinear, possibly fast time-varying, and is assumed to be within the confines of a known set. Most important of all, the control parameters are optimized to achieve better performances. A Pareto optimal approach is employed to solve such an optimal problem. In the optimal procedure, the control parameters are considered as players and their corresponding value ranges are regarded as the decision sets. The cost function of any player includes three parts: the state cost portion, the time cost portion, and the control cost portion. The optimal parameters minimize the cost functions. The resulting vehicle formation control with optimal parameters impels the vehicle platoon both Lyapunov stability and low cost.

Optimal Parameter Selection for Constraint-Following Control for Mechanical Systems Based on Stackelberg Game

Abstract: This paper proposes an optimal parameter design of control scheme for mechanical systems by adopting the Stackelberg game theory. The goal of the control is to drive the mechanical system to follow the prescribed constraints. The system uncertainty is (possibly fast) time-varying and bounded. A β-measure is defined to gauge the performance. A robust control is proposed to render the β-measure uniformly ultimately bounded. This control scheme is based on feasible design parameters (i.e., parameters within prescribed range), whose choice may not be unique. For optimal (unique) parameter selection, a Stackelberg game is formulated. By taking the control design parameters as the players, for each player, a cost function is built with the consideration of the performance cost, the time cost and the control cost. To follow, the Stackelberg strategy is then carried out via backward induction, which results in the choice of the optimal parameters.