Adaptive Backstepping Integral Sliding Mode Control Combined with Super-Twisting Algorithm For Nonlinear UAV Quadrotor System

Approximations and uncertainties in system modeling lead to inaccurate state estimation and thereby the failure of systematically designed control techniques. A robust control technique is therefore needed for state estimation and control when the system is complex and is accompanied by uncertainties. One of the robust control techniques is sliding mode control (SMC). Sliding mode control is a non-linear control technique with remarkable properties like robustness and insensitive to disturbances. Hence, it is highly recommended for the control of extremely complex plants, like nuclear reactors. However, chattering is one of the main disadvantages that arise while using a sliding mode control. In this paper, a sliding mode technique is used for estimating neutron precursor density, xenon, and iodine concentrations which otherwise are very difficult to measure, and then a sliding mode controller with a reduced chattering effect is proposed for pressurized water reactor (PWR). Stability analysis of the system is carried out using the Lyapunov approach. Simulation results are presented to show the efficiency of the proposed system.

Robust Model-Following control of a three-level Neutral Point Clamped shunt active filter in the medium voltage range

—Stability, robust and reliability are the main objectives to design a controller for highly nonlinear spherical motor. Most of linear and nonlinear controllers are stable, model-base controllers are reliable but in this group sliding mode controller is a robust controller. Therefore in this research sliding mode controller is used to design stable, robust and reliable controller. For intelligence part, the minimum rule base Proportional Integral Derivative (PID) Fuzzy hybrid guidance Controller for three dimensions spherical motor is presented in this research. Guidance control in a three dimensions spherical motor is performed by the robust sliding mode controllers producing the control signals which are applied to systems torque. Sliding mode controller has an important problem, namely chattering. In this research, chattering-free sliding mode controller is design as a robust guidance controller to their robust performance in a wide range of operating conditions. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. Nonlinear type robust sliding mode controller is used to modify PID fuzzy logic theory to design robust and reliable hybrid guidance methodology. This research is used to reduce or eliminate the fuzzy and conventional sliding mode controller problem based on minimum rule base fuzzy logic theory and modified it by sliding mode method to control of spherical motor system.

Sliding mode control has been shown to be a robust and effective control approach for stabilization of nonlinear systems. However the dynamic performance of the controller is a complex function of the system parameters, which is often uncertain or partially known. This paper presents an adaptive fuzzy sliding mode control for a class of underactuated nonlinear mechanical systems. An adaptive fuzzy system is used to approximate the uncertain parts of the underactuated system. The adaptive law is designed based on the Lyapunov method. The proof for the stability and the convergence of the system is presented. Robust performance of the adaptive fuzzy sliding mode control is illustrated using a gantry crane system. Simulation results demonstrate that the system output can track the reference signal in the presence of modelling uncertainties, external disturbances and parameter variation.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

The aim of this paper is to develop a novel robust variable sampling period controller (RVSPC) for networked control systems (NCSs) in the presence of random time delays and packet losses. Different from the existing control techniques for NCSs, the RVSPC controller is constructed as a hybrid robust controller with the adaptive variable sampling period. To adapt to the delay variation, a so-called variable sampling period adjuster based on a time delay predictor (TDP) and a time delay and packet detector is designed to adjust effectively the sampling period. To efficiently compensate for the random delays and packet losses, the hybrid controller is designed as a combination of a quantitative feedback theory (QFT)-based robust controller and a robust state feedback controller (RSFC) with adaptive control gain. A smart switch based on the TDP is then used to select properly the main control unit as the QFT or RSFC corresponding to the current delay and packet loss status. Illustrative examples with NCSs including both computation and communication delays and packet losses are finally carried out to illustrate the effectiveness of the proposed method.

Learning-based robust output tracking control for unknown discrete-time nonlinear systems with dynamic uncertainty

Fuzzy sliding mode control as a robust and intelligent nonlinear control technique is proposed to control processes with severe nonlinearity and unknown models. This paper proposes a new adaptive tracking fuzzy sliding mode controller for nonlinear systems in the presence of fuzzy compensation. The main contribution of the proposed method is that the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. The fuzzy self-adaptive rate is derived through the Lyapunov method, and the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value. The performance of the proposed approach is evaluated for double joint rigid manipulator problems. The simulation results illustrate the effectiveness of our proposed controller.

The idea of sliding mode control (SMC) as a robust control technique is utilized to control systems where the dynamics can be described by [xdot](t) = (A + ΔA)x(t) + (B + ΔB)u(t) + d(t) where ΔA, ΔB and d(t) characterize unknown plant parameters and unexpected disturbances, respectively. The analysis is described in the discrete form using the Euler operator. The proposed controller regards the influence of unknown disturbances and parameter uncertainties as an equivalent disturbance and generates a control function (estimate) to cancel their influence by the mechanism of time delay, The states of the uncertain plant are steered from an arbitrary initial state to a stable surface, s = 0, and the plant output is subsequently regulated. One feature of the control is that the sliding conditions are satisfied without the discontinuous control action used in classical SMC. It therefore retains the positive features of SMC without the disadvantage of control chattering. The controller has been studied through simulations and experiment on an NSK direct drive robot arm driven by a DC motor. The results confirm that the control is robust to disturbances and parameter variations.

\(\mathrm{{H}}_{2}\) and \(\mathrm{{H}}_{\infty }\) optimal control are two important breakthroughs in modern robust control. In recent years, the \(\mathrm{{H}}_{2}\) and \(\mathrm{{H}}_{\infty }\) controller design techniques have gained a lot of research attention. Both have strong theoretical basis and are efficient algorithms for synthesizing optimal controllers. The performance of \(\mathrm{{H}}_{2}\) robust controller is useful to handle stochastic aspects such as measurement noise and capture the control cost. In the robust \(\mathrm{{H}}_{2}\) approach, the controller is designed to minimize an upper bound on the worst case \(\mathrm{{H}}_{2}\) norm for a range of admissible plant perturbations. When a model under analysis is applied with \(\mathrm{{H}}_{2}\) robust controller, the closed loop system will have a good dynamic system performance, but it has poor robustness for the external disturbances of the uncertain system model. The performance of \(\mathrm{{H}}_{\infty }\) is convenient to enforce robustness to model uncertainty, but it is based on compromising system performance. Their combination, the mixed \(\mathrm{{H}}_{2}/\mathrm{{H}}_{\infty }\) allows intuitive quadratic performance specifications of the \(\mathrm{{H}}_{2}\) synthesis with robust stability requirements specifications expressed by the \(\mathrm{{H}}_{\infty }\) synthesis. In time domain aspects, satisfactory time response and closed loop damping can often be achieved by enforcing the closed loop poles into a predetermined region of the left half plane. Combining these requirements to form so-called mixed \(\mathrm{{H}}_{2}/\mathrm{{H}}_{\infty }\) design with regional pole placement constraints allows for more flexible and accurate specification of closed-loop behavior. This chapter introduces the work of design of improved LMI-based robust output feedback controller and related simulations.

Most control techniques require the use of a plant model during the design phase in order to tune the controller parameters.The mathematical models are an approximation of real systems and contain imperfections by several reasons: use of low-order descriptions, unmodelled dynamics, obtaining linear models for a specific operating point (working with poor performance outside of this working point), etc.Therefore, control techniques that work without taking into account these modelling errors, use a fixed-structure model and known parameters (nominal model ) supposing that the model exactly represents the real process, and the imperfections will be removed by means of feedback.However, there exist other control methods called robust control techniques which use these imperfections implicity during the design phase.In the robust control field such imperfections are called uncertainties, and instead of working only with one model (nominal model), a family of models is used forming the nominal model + uncertainties.The uncertainties can be classified in parametric or structured and non-parametric or non-structured.The first ones allow representing the uncertainties into the model coefficients (e.g. the value of a pole placed between maximum and minimum limits).The second ones represent uncertainties as unmodelled dynamics (e.g.differences in the orders of the model and the real system) (Morari and Zafiriou, 1989).The robust control technique which considers more exactly the uncertainties is the Quantitative Feedback Theory (QFT).It is a methodology to design robust controllers based on frequency domain, and was developed by Prof. Isaac Horowitz (Horowitz, 1982;Horowitz and Sidi, 1972;Horowitz, 1993).This technique allows designing robust controllers which fulfil some minimum quantitative specifications considering the presence of uncertainty in the plant model and the existence of perturbations.With this theory, Horowitz showed that the final aim of any control design must be to obtain an open-loop transfer function with the suitable bandwidth (cost of feedback) in order to sensitize the plant and reduce the perturbations.The Nichols plane is used to achieve a desired robust design over the specified region of plant uncertainty where the aim is to design a compensator C(s) and a prefilter F(s) (if it is necessary) (see Figure 1), so that performance and stability specifications are achieved for the family of plants.

ABSTRACTRobust control of under-actuated mechanical systems (UMSs) with model uncertainty is still a challenging problem. For UMSs, the model parametric uncertainties make it difficult to precisely calculate the isolated equilibrium point corresponding to a fixed input. Without an accurate destination state, many set-point control methods cannot eliminate the positioning errors. An improved sliding mode control (ISMC) method is proposed to solve the robust control problem for a class of UMSs with model uncertainty and input disturbance. A balance variable is introduced in the sliding surface design to compensate for the disturbance caused by the inaccurate destination state, and the ISMC method is proposed to make the system state reach the sliding surface in finite time. Linear matrix inequality approach and particle swarm optimisation algorithm are applied to design the sliding mode surface parameters. The simulation results on an UMS are presented to show the effectiveness of the proposed scheme.

To improve the flowrate control accuracy of fuel control system at high supply pressure and large flowrate working condition, this paper proposes a novel dual closed-loop control for fuel metering unit (FMU) to address issues related to unknown velocity, matched/unmatched disturbances, and parameter uncertainties. Firstly, for a comprehensive assessment in flowrate feedback method, a global sensitive analysis is utilized to research the effect ratio of factors affecting the discharge flowrate. Then, a novel dual closed-loop control is proposed, in which the outer loop is flowrate loop, and a novel extended-state-observer (ESO)-based robust adaptive control without velocity sensor is used as the internal displacement loop control. Specifically, ESO can effectively estimate the unknown velocity and unmatched disturbance, and the parameter estimations can be updated by the adaptive law determined only by spool displacement and reference signal. The novel robust control can address matched/unmatched disturbances and model uncertainties effectively. Through Lyapunov method, it follows that the novel ESO-based robust adaptive control can achieve asymptotic tracking performance when occurring time-invariant disturbances and bounded tracking performance when occurring time-varying disturbances. Comparative experiments verify the superiority of the proposed control, which provides a new method and glimpse for the advanced control strategy of aeroengine.

This paper presents a design method of robust nonlinear controller with inner-outer loop structure (robust chattering free backstepping/backstepping sliding mode controller). Because backstepping control and sliding mode control are in nature two different robust controllers, the combination of them brings up a controller which has stronger robustness for synthesizing their advantages. In this paper, the chattering of backstepping sliding mode controller can be effectively prevented by substituting sign function of the sliding mode exponential approaching rate with a simple saturation function. The controller adopts inner-outer loop control structure. The inner loop is used to stabilize attitude by adopting backstepping sliding mode controller. The outer loop is used to track position by adopting backstepping controller. Simulation results show that the control strategy proposed in this paper is effective and has strong robustness in the presence of aerodynamic torque disturbance and parameter uncertainty.