Self-tuning of quantitative feedback theory for force control of an electro-hydraulic test machine

The aim of this paper is to design and fabricate an electro-hydraulic test machine (EHTM) for conducting performance and stability test of force and/or position control in the bench system and to propose a robust adaptive controller—the self-tuning quantitative feedback theory (STQFT)—in order to improve the force control performance of electro-hydraulic actuators (EHAs). By using the gradient descent method to adjust the QFT parameters, the novel STQFT controller is able to adapt to a wide range of working conditions. Experiments were carried out to evaluate the effectiveness of the proposed control method applied to the EHTM.     

Design and experimental evaluation of a robust force controller for an electro-hydraulic actuator via quantitative feedback theory

This paper presents the design and experimental evaluation of an explicit force controller for a hydraulic actuator in the presence of significant system uncertainties and nonlinearities. The nonlinear version of quantitative feedback theory (QFT) is employed to design a robust time-invariant controller. Two approaches are developed to identify linear time-invariant equivalent model that can precisely represent the nonlinear plant, operating over a wide range. The first approach is based on experimental input–output measurements, obtained directly from the actual system. The second approach is model-based, and utilizes the general nonlinear mathematical model of a hydraulic actuator interacting with an uncertain environment. Given the equivalent models, a controller is then designed to satisfy a priori specified tracking and stability specifications. The controller enjoys the simplicity of fixed-gain controllers while exhibiting robustness. Experimental tests are performed on a hydraulic actuator equipped with a low-cost proportional valve. The results show that the compensated system is not sensitive to the variation of parameters such as environmental stiffness or supply pressure and can equally work well for various set-point forces.     

Pressure control of an electro-hydraulic actuated clutch via novel hysteresis model

This paper mainly focuses on the development of pressure tracking control logic of electro-hydraulic actuators for vehicle application. This is done to improve and ensure the performance of a precise lower-level controller for evolving modern shift control logic. The required performance is obtained by hysteresis model-based feed-forward control and additional feedback control. The hysteresis and the time delay, which adversely affect pressure control, are well known nonlinear behaviors in electro-hydraulic actuators. In order to cope with the hysteresis, a novel hysteresis model is proposed based on a physical phenomenon. A mathematical model based on a characteristic curve obtained in preliminary experiments is presented using only one tuning parameter, and this model can be inverted easily to construct a feed-forward controller. In addition, a feedback controller is designed considering the stability margin of a time delay system. The feedback control inputs ensure compensation of the feed-forward errors caused by model error and uncertainty. The proposed controller is designed to lower computational cost considering applicability for production vehicles. As a result, the developed pressure controller is applied to a transmission control unit of a production vehicle and verified experimentally for various driving scenarios.     

Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach

This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller.     

Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

This paper focuses on adaptive control of strict-feedback nonlinear systems using multilayer neural networks (MNNs). By introducing a modified Lyapunov function, a smooth and singularity-free adaptive controller is firstly designed for a first-order plant. Then, an extension is made to high-order nonlinear systems using neural network approximation and adaptive backstepping techniques. The developed control scheme guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. In addition, the relationship between the transient performance and the design parameters is explicitly given to guide the tuning of the controller. One important feature of the proposed NN controller is the highly structural property which makes it particularly suitable for parallel processing in actual implementation. Simulation studies are included to illustrate the effectiveness of the proposed approach.     

A practical control strategy for servo-pneumatic actuator systems

A practical control strategy with a simple controller structure is proposed for servo-pneumatic cylinder actuator systems. Theoretical analysis reveals that the acceleration of the piston indirectly represents the cylinder chamber pressure difference so it is possible to employ acceleration feedback instead of pressure feedback in the construction of servo-pneumatic actuator control systems. The main features of the control strategy developed in the paper are (1) using acceleration feedback to improve the stability of the system; and (2) introducing time-delay minimisation and optimised null offset compensation to address the problem of time delay and dead zone, which are mainly caused by the compressibility of air and friction. The experimental studies have been conducted using an asymmetric pneumatic cylinder system and the results show that the system performance has been much improved when compared with a conventional PID controller.     

Robust prediction-based control for unstable delay systems: Application to the yaw control of a mini-helicopter

We present a discrete-time prediction-based state-feedback controller. It is shown that this controller stabilizes possibly unstable continuous-time delay systems. The stability is shown to be robust with respect to uncertainties in the knowledge on the plant parameters, the system delay and the sampling period. The proposed prediction-based controller has been tested in a real-time application to control the yaw angular displacement of a 4-rotor mini-helicopter.     

A Lyapunov's direct method for the global robust stabilization of nonlinear cascaded systems

The global robust stabilization problem of cascaded systems with dynamic uncertainty has been approached by the small gain theorem. This method, however, does not produce an explicit Lyapunov function for the closed-loop system. In this paper, we develop a Lyapunov's direct method based recursive approach to solving the global robust stabilization problem for the mentioned systems. This method also produces an explicit Lyapunov function for the closed-loop system which is a superposition of those of individual subsystems. This Lyapunov function is indispensable when the adaptive control of the same class of systems is further considered.     

Global robust output regulation of lower triangular systems with unknown control direction

This paper presents the solvability conditions for the global robust output regulation problem for lower triangular nonlinear systems assuming the control direction is unknown. The approach used is an integration of the robust stabilization technique and Nussbaum gain technique.     

Adaptive control for nonlinear nonnegative dynamical systems

Nonnegative and compartmental models are widespread in engineering systems and life sciences and play a key role in the understanding of these systems. In this paper, we develop a direct adaptive control framework for nonlinear uncertain nonnegative and compartmental dynamical systems. The proposed framework is Lyapunov-based and guarantees partial asymptotic set-point regulation; that is, asymptotic set-point regulation with respect to part of the closed-loop system states associated with the plant. In addition, the adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space.