Robustness in the face of uncertainties is an important aspect in designing high performance control systems. This paper addresses the problem of accurate trajectory tracking of a small quadcopter unmanned aerial vehicle in the face of uncertainties. Accommodating the worst -case scenario, we propose a hybrid feedback and feedforward autopilot that has the capability to eliminate the cross-coupling disturbance between the lateral and the longitudinal loops with respect to the vertical loop as well as external disturbances (e.g., wind gusts). The proposed control system leverages on the technical benefits of both the nonlinear model predictive control and the fuzzy feedforward compensator. We highlight the efficacy of our hybrid autopilot system with respect to the performance of the conventional PD control systems through rigorous comparative studies. We also present stability analysis of our hybrid control system.

Robust Hybrid Nonlinear Control Systems for the Dynamics of a Quadcopter Drone

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

/pdf/robust-sliding-mode-control-based-on-ga-optimization-and-5d3ooi5npe.pdf

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton.

Intelligent Observer and Control Design for Nonlinear Systems

This paper presents the five degree-of-freedom (5-DOF) control of an electrodynamic MAGLEV suspension with active control of the levitation coils. A sled with built-in permanent magnets moves past an array of fixed coils to generate Lorentz forces that provide lift and guidance. When active control of the levitation coil currents is implemented, multi-DOF trajectory control can be achieved, leading to stable electrodynamic levitation that can actively reject disturbances independently of sled speed. This paper shows successful control of the multi-DOF trajectory of a passive sled by active control of the coil currents, using both a sliding mode controller with nonlinear input mapping from coil currents to levitation forces, and a multi-DOF nonlinear observer. The paper also compares the use of look up tables versus analytical models of the magnetic field density used to estimate the magnetic forces and torques acting on the sled. The performance of the proposed approaches is demonstrated experimentally.

5-DOF Real-Time Control of Active Electrodynamic MAGLEV

Selective harmonic elimination (SHE) has been a widely researched alternative to traditional PWM techniques. This paper presents the selective harmonic elimination of a uniform step asymmetrical multilevel inverter (USAMI) using genetic algorithm (GA) which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to USAMI with any number of levels. As an example, in this paper a 9-level USAMI is considered and the optimum switching angles are calculated to eliminate the 5th, 7th and 11th harmonics.

Genetic Algorithm Application in Asymmetrical 9-Level Inverter

The dynamical model of manipulator robot is represented by equations systems which are nonlinear and strongly coupled. Furthermore, the inertial parameters of manipulator depend on the payload which is often unknown and variable. The sliding mode controller (SMC) provides an effective and robust means of controlling nonlinear plants. The performance of SMC depends on control parameter selection of gain switching (k) and sliding surface constant (s). It is very difficult to obtain the optimal control parameters. In this paper, a control parameter selection algorithm is proposed by genetic algorithm to select the gain switching (k) and sliding surface constant parameter (s) so that the controlled system can achieve a good overall performance in the sliding mode controller design. Testing is done by giving a reference position for joint 1 and joint 2 of the robot manipulator of 45O (degree) with the controller performance indicator is settling time <2 seconds, and the tracking error tolerance is 1%. Simulation results demonstrate better performance of the PML with a genetic algorithm with a small response time by 1.03 seconds to 1.05 seconds joint 1 and 2 as well as for tracking error of the output state by 0.0015 degree for joint 1 and 0.0004 degree for joint 2.

Genetic Algorithm of Sliding Mode Control Design for Manipulator Robot

Magnetic levitation (Maglev) systems make significant contribution to industrial applications due their reduced power consumption, increased power efficiency and reduced cost of maintenance. Common applications include Maglev power generation (e.g. wind turbine), Maglev trains and medical devices (e.g. magnetically suspended artificial heart pump). This paper proposes fuzzy sliding-mode controller ‘FSMC’ with a nonlinear observer been used to estimate the unmeasured states. Simulations are performed with nonlinear mathematical model of the Maglev system, and the results show that the proposed observer and control strategy perform well.

/pdf/fuzzy-sliding-control-with-non-linear-observer-for-magnetic-2hpzfcgu3u.pdf

Fuzzy sliding control with non-linear observer for magnetic levitation systems

Quadcopter UAVs have several complex conditions including nonlinearity, verily coupled, and working area that tend to be subject to disturbances and uncertainties. Sliding Mode Control (SMC), which is one of well-known robust control approaches, is believed to be able in dealing with such issues. However, chattering phenomenon in SMC is still an issue that may lead to performance degradation. Interval Type-2 Fuzzy Logic Control which has the capability to accommodate uncertainties of the system, will play a vital role in eliminating this phenomenon. A combining of the two control methods is investigated in this paper, and the results obtained demonstrate good performance of the approach in dealing with nonlinearity, disturbance, and uncertainty as well as eliminating the chattering phenomenon of SMC.

SLIDING MODE CONTROL – BASED INTERVAL TYPE-2 FUZZY LOGIC CONTROLLER FOR QUADCOPTER UAVs

The availability of information on all states plays a critical role in a wide range of fields including quadcopter UAVs. Sensor limitation, accuracy, noise, and uncertainty are issues that limit acquisition of information on states of such vehicles. Sliding mode observer (SMO), as a nonlinear observer, is believed to be able to deal with such issues. However, the chattering phenomenon in SMO is still an issue that may lead to performance degradation. Interval Type-2 fuzzy logic system (IT2FLS), which has the capability to accommodate uncertainties of the system, will play a vital role in eliminating this phenomenon. A hybrid mechanism by combining the two methods is investigated in this paper. The proposed method is compared with Type-1 fuzzy logic observer to show the advantages of the approach. The results obtained demonstrate good performance of the approach in dealing with noise and uncertainty as well as eliminating the chattering phenomenon of SMO.

Robust sliding mode - based interval Type-2 fuzzy logic observer for quadcopter UAVs

A knowledge of human upper-limb structure and its mechanical functions are important for developing an exoskeleton. The Sliding Mode Control with Fuzzy Type-II is proposed to control the movements of the human extremity joints. The Lagrange method is used to model the dynamics system of human upper-limb. The findings demonstrate the effectiveness of the proposed controller in tracking the desired motion and it is also able to eliminate the chattering problem as well as deal with uncertainties.

/pdf/tracking-human-upper-limb-movements-with-sliding-mode-2fkqnw22fn.pdf

Ahmad Riyad Firdaus

Papers

Genetic Algorithm of Sliding Mode Control Design for Manipulator Robot

Fuzzy sliding control with non-linear observer for magnetic levitation systems

SLIDING MODE CONTROL – BASED INTERVAL TYPE-2 FUZZY LOGIC CONTROLLER FOR QUADCOPTER UAVs

Robust sliding mode - based interval Type-2 fuzzy logic observer for quadcopter UAVs

Tracking human upper-limb movements with sliding mode control type-II fuzzy logic