On an Optimized Fuzzy Supervized Multiphase Guidance Law

Velayati

2019

Automatika

In this paper, an adaptive algorithm is developed which senses the road condition change and estimates a (time-varying) optimal braking slip ratio. This is conducted by two on-line simultaneously operating tire-road friction-curve slope calculators: one based on the accelerometer output and the other based on the wheel speed. The required vehicle speed is estimated using a robust sliding-mode observer. Enforcement of the online optimal braking reference is left to an adaptive sliding mode controller to cope with the system strong nonlinearity, time dependency and the speed and friction-coefficient estimation errors. The algorithm is applied to a half model car and the braking performance is examined. The results indicate that the proposed algorithm substantially reduces the stopping time and distance. The performance of the algorithm is verified using different vehicle initial speeds and especially non-uniform road condition where 8% improvement versus the nonadaptive optimal slip ratio algorithm is recorded. ARTICLE HISTORY

Section: Abs Adaptive Sliding Mode Controlmentioning

confidence: 99%

“…Control of uncertain systems requires a robust control technique such as fractional order PID [11], H ∞ [12,13] or sliding mode controllers [14][15][16]. Backstepping integral sliding mode controller is the subject of study in [17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road

Velayati

2019

Automatika

“…Design approaches adopted may be heuristic [3,4] or classic ones including linear and wide range of non-linear optimization techniques. Consensus-based control of distributed generators to minimize frequency deviation has been addressed in [5].…”

Section: Introductionmentioning

confidence: 99%

Robust decentralized control of interval DC linked hybrid microgrids network

Nabi

IET Control Theory & Appl

2022

Here, a robust sparse decentralized controller (RSDC) for uncertain DC-linked hybrid microgrids is designed. The robustness against load/generation uncertainty is guaranteed using Kharitonov's stability criteria and sparsity is promoted by appending an l 1 term to the typical Linear Quadratic Regulator (LQR). To improve the convergence rate, the differentiable term of the composite cost function is second order approximated, the coordinate descent technique is adopted, and the "active set" idea which limits the search to the effective regions is employed. In addition, the level of sparsity is automatically and optimally adjusted not to compromise the system's stability and robustness. The algorithm is applied to a 69-bus (consist of 7 hybrid microgrid) distribution system and the controller sparsity percentage, design convergence rate, and performances are compared with the H 2 and H ∞ designs. The results indicate that the proposed method is superior to the others in all aspects, which are presented by the numerical indices, the time-domain transient responses, and the closed-loop poles map.

“…A robust sparse design has been discussed in [13], where the H 2 technique for an interconnected system with polytopic uncertainties is simulated. The application of sliding mode in control of uncertain systems has been analysed in [14,15].…”

Section: Introductionmentioning

confidence: 99%

Optimal sparse control of interval networks: Voltage source converter based DC grid study

Nabi

IET Generation Trans & Dist

2021

Self Cite

Decentralized optimal control of interval state space systems involves significant complications due to the required sparsity, robustness, and convergence rate. By adding an l1 term to the standard linear quadratic optimization, the obtained composite index, accommodating the sparsity, contains both differentiable and non-differentiable terms. Knowing that the choice of optimization techniques are problem-dependent, in this paper, several smart provisions such as "Active set", "Coordinate descent", and separately managing the differentiable part of the cost function are put together to form a relatively fast algorithm. In addition, a rule is introduced to adjust automatically the degree of sparsity and a constraint to confine the interval closed-loop system eigenvalues to the desired s plane region. As a result, a robust sparse decentralized control (RSDC) approach for interval networks is established. The RSDC method is utilized for the control of a multi-terminal VSC (Voltage Source Converter ) -based DC (Direct Current) grid with parametric uncertainty under various external random perturbations. The test results show that the proposed method with just 20 feedback links (out of available 100) performs superiorly to the alternative ones in terms of sparsity, convergence rate, and time-domain performances, verified through extensive simulations.