Fuzzy-Immune-Regulated Adaptive Degree-of-Stability LQR for a Self-Balancing Robotic Mechanism: Design and HIL Realization

Abstract

This letter formulates a fuzzy-immune adaptive system for the online adjustment of the Degree-of-Stability (DoS) of Linear-Quadratic-Regulator (LQR) procedure to strengthen the disturbance attenuation capacity of a self-balancing mechatronic system. The fuzzy-immune adaptive system uses pre-configured control input-based rules to alter the DoS parameter of LQR for dynamically relocating the closed-loop system's eigenvalues in the complex plane's left half. The corresponding changes in the eigenvalues are conveyed to the Riccati equation, which eventually yields the self-adjusting LQR gains. This arrangement allows for the flexible manipulation of the applied control effort and the response speed as the error conditions change. The efficacies of the self-tuning LQR scheme are verified by performing custom-designed hardware-in-theloop experiments on the Quanser rotary inverted pendulum system. As compared to the DoS-LQR, the proposed controller improves the pendulum's transient recovery time, overshoots, input demands, and offsets by 32.3%, 50.5%, 33.9%, and 33.3%, respectively, under disturbances. These experimental outcomes verify that the proposed self-tuning LQR law considerably improves the system's disturbance attenuation capability.

Adaptive optimal control of under-actuated robotic systems using a self-regulating nonlinear weight-adjustment scheme: Formulation and experimental verification

“…The sliding-mode controllers are well known for their strong robustness against disturbances, which comes at the cost of a highly disputed control profile and, hence, may suffer from large chatter in the response [9]. The linear quadratic regulator (LQR) is an optimal control strategy that minimizes the quadratic cost function (QCF) of the system's state variations and control input [10]. Despite its attributes, the LQR yields a fragile effort against modeling uncertainties, identification errors, and nonlinear disturbances [11].…”

“…where, A is the system matrix, B is the input matrix, C is the output matrix, D is the feed-forward matrix, u(t) is the control input signal, x(t) is the state vector, and y(t) is the output vector. The system's input vector and state vector are presented in (10).…”

Research on the motion control strategy of autonomous excavator based on online compensation

“…23,24 The control approach they use is to directly adopt the estimated point as the center of gravity of the manipulator, and it is often impossible to precisely determine the actual center of gravity of the manipulator after the bucket of the excavator is loaded. 25 In the field of excavator automation, there are few studies on online identification of excavator mechanical parameters and compensation control of external disturbance compensation of hydraulic cylinders. In this paper, the gravity, centripetal force, and inertial force of the manipulator are online compensated by online identification of the position parameters of the manipulator’s center of gravity, which effectively eliminates the influence of external interference during manipulator movement and improves the autonomous excavator’s operation accuracy and efficiency.…”

A trajectory tracking control system for paddle boat in intelligent aquaculture

“…Qiu et al [ 23 ] established a neural network to reduce the computation and employed a hyperbolic tangent function to diminish sliding mode chattering based on an adaptive sliding mode control scheme. Iqbal et al [ 24 ] proposed a fuzzy-immune adaptive system that enhanced the anti-jamming capability of a self-balancing robot system and proves the stability of the system. Moreover, Iqbal et al [ 25 ] proposed a novel adaptive PD-type iterative learning control with robustness and effectiveness in handling nonlinearities.…”